repo_name stringlengths 9 75 | topic stringclasses 30
values | issue_number int64 1 203k | title stringlengths 1 976 | body stringlengths 0 254k | state stringclasses 2
values | created_at stringlengths 20 20 | updated_at stringlengths 20 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
feature-engine/feature_engine | scikit-learn | 8 | add check for NA in categorical encoders | All categorical encoders need to check if data set contains missing values before either training or transforming a data set. | closed | 2019-09-04T08:05:52Z | 2020-04-19T09:53:23Z | https://github.com/feature-engine/feature_engine/issues/8 | [] | solegalli | 0 |
freqtrade/freqtrade | python | 11,523 | Is StaticPairList effective for newly launched pairs? | <!--
Have you searched for similar issues before posting it?
Did you have a VERY good look at the [documentation](https://www.freqtrade.io/en/latest/) and are sure that the question is not explained there
Please do not use the question template to report bugs or to request new features.
-->
## Describe your environment
* Operating system: Windows 11
* Python Version: 3.10.15
* CCXT version: 4.4.33
* Freqtrade Version: 2024.11-dev-d0f326b93
## Your question
I set the `pair_whitelist` to `[".*/USDT:USDT"]` and `pairlists.method` to `"StaticPairList"` in the config file without freqai.
So when the strategy is running, if the exchange launches some new pairs, can I trade these new pairs without doing anything?
If not, how can I do to be able to trade all futures pairs supported on the exchange in real-time? | closed | 2025-03-17T15:39:07Z | 2025-03-17T16:48:17Z | https://github.com/freqtrade/freqtrade/issues/11523 | [
"Question"
] | Chen-Shuai-CS | 1 |
remsky/Kokoro-FastAPI | fastapi | 43 | GPU image does not use GPU | I am using following image: ghcr.io/remsky/kokoro-fastapi-gpu:v0.0.5post1 with unmodified `docker-compose.yml` from repo.
```
kokoro-tts-1 | INFO: Started server process [1]
kokoro-tts-1 | INFO: Waiting for application startup.
kokoro-tts-1 | 11:30:33 AM | INFO | Loading TTS model and voice packs...
kokoro-tts-1 | 11:30:33 AM | INFO | CUDA available: False
kokoro-tts-1 | 11:30:33 AM | INFO | Initializing model on cpu
```
Image runs as `appuser` instead of root and on linux you need to be in proper group to use NVIDIA driver (even when using nvidia-container-toolkit).
```
devilan@darkstar:~/git/Kokoro-FastAPI (master) $ docker exec -it b5ed10b82d7f /bin/bash
appuser@b5ed10b82d7f:/app$ nvidia-smi
Failed to initialize NVML: Insufficient Permissions
```
You should at least point this out clearly in README if this app should not work as root user.
There are some methods to resolve this problem, but each of them compromises system a bit.
You can chmod 666 /dev/nvidia* so no access restriction for this device (but not everyone would like to do so). | closed | 2025-01-13T12:12:31Z | 2025-01-13T13:21:07Z | https://github.com/remsky/Kokoro-FastAPI/issues/43 | [] | DevilaN | 2 |
aws/aws-sdk-pandas | pandas | 2,399 | `wr.dyamodb.read_items` gives a pyarrow error | ### Describe the bug
I'm not quite sure where this problem is arising, but just attempting to read 10 rows from a database table. It looks like the error is thrown before my query is even run: from trying to resolve the table metadata itself?
```py
Traceback (most recent call last):
File "/home/louis/dev/testing/wrangler/wrangler_demo.py", line 3, in <module>
items = wr.dynamodb.read_items(
File "/home/louis/miniconda3/envs/wr310/lib/python3.10/site-packages/awswrangler/_utils.py", line 174, in inner
return func(*args, **kwargs)
File "/home/louis/miniconda3/envs/wr310/lib/python3.10/site-packages/awswrangler/dynamodb/_read.py", line 635, in read_items
return _read_items(
File "/home/louis/miniconda3/envs/wr310/lib/python3.10/site-packages/awswrangler/dynamodb/_read.py", line 384, in _read_items
return _read_items_scan(
File "/home/louis/miniconda3/envs/wr310/lib/python3.10/site-packages/awswrangler/dynamodb/_read.py", line 341, in _read_items_scan
return _utils.table_refs_to_df(items, arrow_kwargs)
File "/home/louis/miniconda3/envs/wr310/lib/python3.10/site-packages/awswrangler/_distributed.py", line 105, in wrapper
return cls.dispatch_func(func)(*args, **kw)
File "/home/louis/miniconda3/envs/wr310/lib/python3.10/site-packages/awswrangler/_utils.py", line 882, in table_refs_to_df
return _table_to_df(pa.concat_tables(tables, promote=True), kwargs=kwargs)
File "pyarrow/table.pxi", line 5371, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Unable to merge: Field user_id has incompatible types: decimal128(4, 0) vs decimal128(6, 0
```
### How to Reproduce
```py
import awswrangler as wr
items = wr.dynamodb.read_items(
table_name="my-table",
max_items_evaluated=10, # limit the number of items to 10 for testing
columns=["foo_id", "user_id", "bar_id"], # specify the columns to read
)
print(items)
```
### Expected behavior
The columns in this query are all N (Number) and should be integer dtype. I can't even get it to run though. I looked at what it was implying and it seems to refer to the arrow decimal128 type's precision (scale being 0 means there are no characters after the decimal place) and my interpretation is that it indicates there are numbers returned at different orders of magnitude (powers of 10)?
I just created a conda environment to test this library out as an alternative to the boto3 client access to dynamodb. Happy to try any suggestions to fix
### Your project
_No response_
### Screenshots
_No response_
### OS
Linux
### Python version
3.10
### AWS SDK for pandas version
3.2.1
### Additional context
```sh
$ pip list
Package Version
----------------- -------
awswrangler 3.2.1
boto3 1.28.7
botocore 1.31.7
jmespath 1.0.1
numpy 1.25.1
packaging 23.1
pandas 2.0.3
pip 23.1.2
pyarrow 12.0.1
python-dateutil 2.8.2
pytz 2023.3
s3transfer 0.6.1
setuptools 67.8.0
six 1.16.0
typing_extensions 4.7.1
tzdata 2023.3
urllib3 1.26.16
wheel 0.38.4
``` | closed | 2023-07-20T16:24:28Z | 2023-07-21T14:23:44Z | https://github.com/aws/aws-sdk-pandas/issues/2399 | [
"bug"
] | lmmx | 1 |
apache/airflow | automation | 47,858 | Toggle to exclude removed tasks from the grid is not available | ### Apache Airflow version
2.10.5
### If "Other Airflow 2 version" selected, which one?
_No response_
### What happened?
As per [AIP 63 doc](https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-65%3A+Improve+DAG+history+in+UI) We should be able to toggle in GRID to exclude removed tasks. As per current implementation I do not see that.
### What you think should happen instead?
_No response_
### How to reproduce
1. Execute DAG runs
2. Remove Tasks
3. Check Grid
### Operating System
Linux
### Versions of Apache Airflow Providers
_No response_
### Deployment
Microsoft ADF Managed Airflow
### Deployment details
_No response_
### Anything else?
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| open | 2025-03-17T11:05:58Z | 2025-03-17T11:38:14Z | https://github.com/apache/airflow/issues/47858 | [
"kind:bug",
"priority:medium",
"area:core",
"area:UI",
"affected_version:3.0.0beta"
] | vatsrahul1001 | 2 |
litestar-org/litestar | api | 3,812 | Bug: new docs theme makes code hard to read | ### Description
Link: https://docs.litestar.dev/3-dev/usage/dto/1-abstract-dto.html
<img width="1352" alt="Снимок экрана 2024-10-16 в 10 53 20" src="https://github.com/user-attachments/assets/69db6f46-0349-45d7-ac2f-e6e08d2c07ed">
Right now it is very hard to read the code, since it is yellow on yellow.
For some people who have problems with recognising yellow colors it might be even impossible to read these examples.
Please, consider adding a more contrast theme or keeping the old one.
### URL to code causing the issue
_No response_
### MCVE
_No response_
### Steps to reproduce
_No response_
### Screenshots
_No response_
### Logs
_No response_
### Litestar Version
main
### Platform
- [ ] Linux
- [ ] Mac
- [ ] Windows
- [ ] Other (Please specify in the description above) | open | 2024-10-16T07:56:18Z | 2025-03-20T15:55:00Z | https://github.com/litestar-org/litestar/issues/3812 | [
"Bug :bug:",
"Documentation :books:"
] | sobolevn | 2 |
tensorly/tensorly | numpy | 27 | Dangerous dependency handling during testing. | There is no guarantee that the dependencies used during testing will match the dependencies the project actually needs.
Currently we test using a conda environment that gets set up in the following manner:
` - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION numpy scipy`
But there is no guarantee that the version used by conda will match the version in requirements.txt | closed | 2018-01-13T03:53:25Z | 2018-01-24T22:40:04Z | https://github.com/tensorly/tensorly/issues/27 | [] | jesuscast | 1 |
openapi-generators/openapi-python-client | rest-api | 288 | Support non-schema components and references (e.g. parameters) | **Describe the bug**
When building the client with the path parameter in the yaml file which are passed through reference, it does not consider the parameter and the generated client does not have those referenced parameters.
**To Reproduce**
Steps to reproduce the behavior:
1. Take any open api sample yaml file that have path parameters in it.
2. Pass that parameter as reference like for example,
parameters:
- $ref: '#/components/parameters/sampleparam'
3. Generate client using command "openapi-python-client generate --path <yaml_path>"
**Expected behavior**
It should consider the path parameters passed through reference too same as the parameters that it considers when passed with the standard way.
**OpenAPI Spec File**
Any openapi spec file could be used.
**Desktop (please complete the following information):**
- OS: macOS 10.15.6
- Python Version: 3.7.3
- openapi-python-client 0.7.3
**Additional context**
It does not raise any error while building the client, but it does not have the referenced parameters in the generated client. | closed | 2021-01-07T07:53:39Z | 2022-08-13T17:38:49Z | https://github.com/openapi-generators/openapi-python-client/issues/288 | [
"✨ enhancement"
] | Aniketghumed | 12 |
dmlc/gluon-cv | computer-vision | 1,657 | feature vector size does not change as num-segments increase | I'm extracting video features using
`python feat_extract.py --data-list video_list.txt --model i3d_resnet50_v1_kinetics400 --save-dir feats --num-segments 10`
Changing the `num-segments` parameter value does not change the dimensionality of the output feature. Both `--num-segments 1` and `--num-segments 10` result in `1 x 2048` feature dims.
Could someone please verify that this is the expected behavior? I assumed that `--num-segment 10` will divide the input video into 10 shorter clips and extracts the feats separately for each clip; resulting in shape `10 x 2048`
Here is the line that I'm referring to:
https://github.com/dmlc/gluon-cv/blob/ab03ca04c588342be5cd659c3f96011c0146ac4f/scripts/action-recognition/feat_extract.py#L199
| closed | 2021-05-10T18:50:57Z | 2021-05-17T13:57:08Z | https://github.com/dmlc/gluon-cv/issues/1657 | [] | R2D2oid | 1 |
langmanus/langmanus | automation | 33 | 测试通过，启动服务端，页面访问时报404 | 使用uv run main.py是正常，然后使用uv run server.py启动了服务端，在浏览器进行xxx:8000访问时报404 not found。部署在远程服务器，然后本地电脑访问的 | closed | 2025-03-18T15:53:54Z | 2025-03-19T03:44:08Z | https://github.com/langmanus/langmanus/issues/33 | [] | xiaobai3310 | 2 |
feder-cr/Jobs_Applier_AI_Agent_AIHawk | automation | 664 | Ignore | Ignore,
very sorry | closed | 2024-10-29T17:04:55Z | 2024-10-29T22:44:38Z | https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/issues/664 | [
"help wanted"
] | Alkhalid3 | 0 |
python-gino/gino | sqlalchemy | 40 | GINO query methods should accept raw SQL | So that user could get model objects from raw SQL. For example:
```python
users = await db.text('SELECT * FROM users WHERE id > :num').gino.model(User).return_model(True).all(num=28, bind=db.bind)
``` | closed | 2017-08-30T02:28:27Z | 2017-08-30T03:16:49Z | https://github.com/python-gino/gino/issues/40 | [
"help wanted",
"task"
] | fantix | 1 |
luispedro/mahotas | numpy | 61 | mh.labeled.bbox strange behavior | I encounter the following strange behavior where mh.labeled.bbox is all zeros:
```
aaa = array([[[2256, 402, 402],
[2256, 402, 402],
[2256, 402, 402]],
[[2256, 402, 402],
[2256, 402, 402],
[2256, 402, 402]],
[[2256, 402, 402],
[2256, 402, 402],
[2256, 402, 402]]], dtype=uint32)
mh.labeled.bbox(aaa)
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
...,
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
mh.labeled.bbox(aaa).shape
(2257, 6)
mh.bbox(aaa==2256) # but this works
array([0, 3, 0, 3, 0, 1])
```
| closed | 2015-06-02T18:18:48Z | 2015-06-04T07:59:26Z | https://github.com/luispedro/mahotas/issues/61 | [] | haehn | 3 |
MycroftAI/mycroft-core | nlp | 2,889 | Adding last spoken sentence | I made an assistant using aiml and now i have switched to mycroft. However one feature i miss is the ability to re speak what was said last. This is a very useful feature especially when one is using headphones or is busy doing something else and missed what was said.
i used a variable in the txt2wav.py file called prevline which stores the last sentence. | closed | 2021-04-28T20:01:25Z | 2021-04-28T23:13:07Z | https://github.com/MycroftAI/mycroft-core/issues/2889 | [
"enhancement"
] | hanzala123 | 6 |
2noise/ChatTTS | python | 237 | [分享] ChatTTS Forge 项目 | 分享下我的项目，实现了大部分 api 需求
https://github.com/lenML/ChatTTS-Forge
还实验性质的写了一个 ssml 语法用来支持长文本定制生成
另外还有 prompt 注入对生成进行风格干预
你可以在 huggingface 上在线体验（速度超快）：
https://huggingface.co/spaces/lenML/ChatTTS-Forge
欢迎提来pr issues | closed | 2024-06-03T16:24:09Z | 2024-06-25T11:38:13Z | https://github.com/2noise/ChatTTS/issues/237 | [
"ad"
] | zhzLuke96 | 4 |
521xueweihan/HelloGitHub | python | 2,468 | 【自荐项目】 - Smalltalk | ## 推荐项目
<!-- 这里是 HelloGitHub 月刊推荐项目的入口，欢迎自荐和推荐开源项目，唯一要求：请按照下面的提示介绍项目。-->
<!-- 点击上方 “Preview” 立刻查看提交的内容 -->
<!--仅收录 GitHub 上的开源项目，请填写 GitHub 的项目地址-->
- 项目地址：
https://github.com/tinystruct/smalltalk
<!--请从中选择（C、C#、C++、CSS、Go、Java、JS、Kotlin、Objective-C、PHP、Python、Ruby、Rust、Swift、其它、书籍、机器学习）-->
- 类别：
Java
<!--请用 20 个左右的字描述它是做什么的，类似文章标题让人一目了然 -->
- 项目标题：
Smalltalk
<!--这是个什么项目、能用来干什么、有什么特点或解决了什么痛点，适用于什么场景、能够让初学者学到什么。长度 32-256 字符-->
- 项目描述：
smalltalk是基于@tinystruct框架的一个示例项目，它支持C/S应用程序和B/S Web应用程序的开发。它允许您通过命令行界面（CLI）或Web界面与OpenAI开发的ChatGPT语言模型进行交互。
<!--令人眼前一亮的点是什么？类比同类型项目有什么特点！-->
- 亮点：
它允许您通过命令行界面（CLI）或Web界面与OpenAI开发的ChatGPT语言模型进行交互。
- 示例代码：（可选）
- 截图：（可选）gif/png/jpg
<img src="https://raw.githubusercontent.com/tinystruct/smalltalk/master/screenshot.png" />
- 后续更新计划：
持续更新...
| closed | 2023-01-13T13:33:52Z | 2023-01-21T23:06:26Z | https://github.com/521xueweihan/HelloGitHub/issues/2468 | [] | m0ver | 1 |
mwaskom/seaborn | pandas | 3,387 | DOC: warning-text of old documentation mentions link to new version but there is none | At https://seaborn.pydata.org/archive/0.11/generated/seaborn.scatterplot.html there is supposed to be a clickable link in the red box, but I can't find anything.
I believe the link should point to https://seaborn.pydata.org/generated/seaborn.scatterplot.html | closed | 2023-06-14T17:25:13Z | 2023-09-29T10:52:28Z | https://github.com/mwaskom/seaborn/issues/3387 | [
"docs"
] | julian-goettingen | 6 |
ScrapeGraphAI/Scrapegraph-ai | machine-learning | 214 | OMP: Error #15: Initializing libiomp5md.dll, but found libomp140.x86_64.dll already initialized | **Describe the bug**
OMP: Error #15: Initializing libiomp5md.dll, but found libomp140.x86_64.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
**To Reproduce**
Steps to reproduce the behavior:
Targetting in the following script various websites, for example this one: https://github.com/VinciGit00/Scrapegraph-ai/releases
`from scrapegraphai.graphs import SmartScraperGraph`
`from scrapegraphai.utils import prettify_exec_info`
`graph_config = {`
`"llm": {`
`"model": "ollama/mistral",`
`"temperature": 1,`
`"format": "json", `
`"model_tokens": 2000,`
`"base_url": "http://localhost:11434",`
`},`
`"embeddings": {`
`"model": "ollama/nomic-embed-text",`
`"temperature": 0,`
`"base_url": "http://localhost:11434",`
`}`
`}`
`smart_scraper_graph = SmartScraperGraph(`
`prompt="List me all the news with their description.",`
`source="https://github.com/VinciGit00/Scrapegraph-ai/releases",`
`config=graph_config`
`)`
`result = smart_scraper_graph.run()`
`print(result)`
**Expected behavior**
A list of news with the json format.
**Desktop:**
- OS: Windows 10
| closed | 2024-05-10T23:49:21Z | 2024-08-08T08:34:51Z | https://github.com/ScrapeGraphAI/Scrapegraph-ai/issues/214 | [
"bug"
] | elmoBG8 | 11 |
miguelgrinberg/flasky | flask | 317 | Suggestion: ENV setup for Chapter 7 Unittest | For Chapter 7, just a suggestion to include a note about setting the FLASK_APP to flasky.py before the unit test output since it has always been hello.py up to this point and one might easily forget that was set and wonder why 'flask test' command throws an error.
Include this:
(venv) $ set FLASK_APP=flasky.py
Before this output:
(venv) $ flask test
test_app_exists (test_basics.BasicsTestCase) ... ok
test_app_is_testing (test_basics.BasicsTestCase) ... ok
.----------------------------------------------------------------------
Ran 2 tests in 0.001s
OK
Thank you.
| closed | 2017-11-23T21:57:18Z | 2017-12-10T19:58:52Z | https://github.com/miguelgrinberg/flasky/issues/317 | [
"bug"
] | ericchou1 | 2 |
coqui-ai/TTS | pytorch | 3,067 | [Bug] tts_to_file gives TypeError: Invalid file: None | ### Describe the bug
When using the xtts-1 model on windows (python 3.11.6), every time I run the `tts_to_file` function, it gives the error `TypeError: Invalid file: None`
### To Reproduce
On windows with python 3.11.6, with torch, torchaudio (not sure if needed, but just to be sure) and TTS installed, run this snippet
```python
import torch
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1").to("cuda" if toch.cuda.is_available() else "cpu")
# any combination of parameters gives the same error.
tts.tts_to_file("Hello, world!", language="en") # Expected error: "TypeError: Invalid file: None"
```
### Expected behavior
The audio output should be written to output.wav, or the specified file name.
### Logs
_No response_
### Environment
```shell
- 🐸TTS Version: 0.17.8
- PyTorch Version: 2.1.0+cpu
- Python Version: 3.11.6
- OS: Windows 11
- CUDA/cuDNN version: null
- GPU models and configuration: AMD Ryzen 7 5700G with Radeon Graphics
- How you installed PyTorch: pip on a virtual environment
```
### Additional context
_No response_ | closed | 2023-10-13T14:15:20Z | 2023-11-10T05:34:00Z | https://github.com/coqui-ai/TTS/issues/3067 | [
"bug"
] | perrylets | 9 |
pallets/flask | python | 5,056 | make blinker a required dependency | It's now part of the Pallets-Eco community, https://github.com/pallets-eco. Making it required will simplify Flask's support, it's a bit of a hack right now that typing doesn't like. | closed | 2023-04-12T21:55:49Z | 2023-04-28T00:05:53Z | https://github.com/pallets/flask/issues/5056 | [] | davidism | 0 |
cobrateam/splinter | automation | 751 | Chrome: screenshot_as_png() Not callable from browser or browser.driver | Hello, I was interested in being able to directly call [screenshot_as_png()](https://github.com/cobrateam/splinter/blob/9a79baf73f10d79e9b3d7695745c937154018874/splinter/driver/webdriver/__init__.py#L978) from the browser object instead of browser.screenshot which would require me to add an extra IO step in reading the image.
I'm using the Chrome Driver and getting the error
```
AttributeError: 'WebDriver' object has no attribute 'screenshot_as_png'
```
But browser.screenshot('filename') seems to work fine, am I missing something? I even tried `browser.element_class.get_screenshot_as_png()` because I believe `.screenshot()` is dependent on `.screenshot_as_png()` if I'm not mistaken | closed | 2019-12-23T15:02:54Z | 2020-01-11T06:45:54Z | https://github.com/cobrateam/splinter/issues/751 | [
"question"
] | gtamba | 2 |
strawberry-graphql/strawberry | django | 3,479 | Ability to disable auto-camelcasing per field | Let's say I want to keep auto-camelcasing enabled for my whole service _except_ for a couple of fields
e.g. maybe i have a translations service and want to write schema/query like this:
```graphql
query {
getTranslations(string: "Hello world") {
en_US
fr_FR
}
}
```
Ideally, I could write resolvers like this
```python
@strawberry.type
class Translations:
@strawberry.field(disable_auto_camelcase=True)
def en_US(self) -> str: ...
@strawberry.field(disable_auto_camelcase=True)
def fr_FR(self) -> str: ...
```
or maybe even for a whole type?
```python
@strawberry.type(disable_auto_camelcase=True)
class Translations:
@strawberry.field
def en_US(self) -> str: ...
@strawberry.field
def fr_FR(self) -> str: ...
```
Thanks!
## Feature Request Type
[x] Alteration (enhancement/optimization) of existing feature(s) | closed | 2024-04-30T15:53:02Z | 2025-03-20T15:56:42Z | https://github.com/strawberry-graphql/strawberry/issues/3479 | [] | magicmark | 3 |
blacklanternsecurity/bbot | automation | 1,601 | How to add multiple api keys per service? | **Describe the bug**
Most of the modules (services) allow you to sign up for a free account with a limited quota per api token. How can I insert more than one token? Does the tool know how to read several tokens for one module and rotate them so that they don't run out quickly and impact the results of scan?
Something like that:
```
modules:
shodan_dns:
api_keys:
- 'api_key_1'
- 'api_key_2'
``` | closed | 2024-07-29T16:45:58Z | 2024-10-02T19:00:03Z | https://github.com/blacklanternsecurity/bbot/issues/1601 | [
"enhancement",
"high-priority"
] | DrorDvash | 7 |
robinhood/faust | asyncio | 68 | Command to delete state for old app version | The app version flag makes it easy to start applications from scratch. However this results in a bunch of state in rocksdb/kafka for older app versions that stick around for a long time. We should try to fix this. | closed | 2018-02-22T23:01:43Z | 2018-11-27T22:05:37Z | https://github.com/robinhood/faust/issues/68 | [
"Issue Type: Enhancement"
] | vineetgoel | 0 |
matplotlib/mplfinance | matplotlib | 134 | Customizing background and grid | Hello! I'm just getting started to this library and i'm surprised at how easy to use it is while looking beautiful and modern. I'm trying to add some more customization to my chart, but i'm struggling with some parameters: i would like to change color to the background and i would like to remove the grid from my chart, but i don't know where to set those values. In addition, with the old mpl_finance, i was able to set an `alpha` parameter, can i do something similar with mplfinance? Thanks in advance! | closed | 2020-05-16T13:12:58Z | 2020-05-17T10:54:07Z | https://github.com/matplotlib/mplfinance/issues/134 | [
"question"
] | Sile25 | 3 |
OpenInterpreter/open-interpreter | python | 1,453 | How to use with gemini api key | ### Is your feature request related to a problem? Please describe.
_No response_
### Describe the solution you'd like
How can I use the api key in https://aistudio.google.com/
### Describe alternatives you've considered
_No response_
### Additional context
_No response_ | closed | 2024-09-12T14:10:44Z | 2025-02-15T13:33:13Z | https://github.com/OpenInterpreter/open-interpreter/issues/1453 | [] | hyroxtvvv | 3 |
huggingface/datasets | numpy | 7,079 | HfHubHTTPError: 500 Server Error: Internal Server Error for url: | ### Describe the bug
newly uploaded datasets, since yesterday, yields an error.
old datasets, works fine.
Seems like the datasets api server returns a 500
I'm getting the same error, when I invoke `load_dataset` with my dataset.
Long discussion about it here, but I'm not sure anyone from huggingface have seen it.
https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1
### Steps to reproduce the bug
this api url:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
respond with:
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Expected behavior
return no error with newer datasets.
With older datasets I can load the datasets fine.
### Environment info
# Browser
When I access the api in the browser:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Request headers
```
Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8
Accept-Encoding gzip, deflate, br, zstd
Accept-Language en-US,en;q=0.5
Connection keep-alive
Host huggingface.co
Priority u=1
Sec-Fetch-Dest document
Sec-Fetch-Mode navigate
Sec-Fetch-Site cross-site
Upgrade-Insecure-Requests 1
User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0
```
### Response headers
```
X-Firefox-Spdy h2
access-control-allow-origin https://huggingface.co
access-control-expose-headers X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range
content-length 80
content-type application/json; charset=utf-8
cross-origin-opener-policy same-origin
date Fri, 26 Jul 2024 19:09:45 GMT
etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c"
referrer-policy strict-origin-when-cross-origin
vary Origin
via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront)
x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ==
x-amz-cf-pop CPH50-C1
x-cache Error from cloudfront
x-error-message Internal Error - We're working hard to fix this as soon as possible!
x-powered-by huggingface-moon
x-request-id Root=1-66a3f479-026417465ef42f49349fdca1
``` | closed | 2024-07-27T08:21:03Z | 2024-09-20T13:26:25Z | https://github.com/huggingface/datasets/issues/7079 | [] | neoneye | 17 |
dsdanielpark/Bard-API | nlp | 206 | Response Error - Unable to get response | I executed this following command to use the Bard API :
from bardapi import Bard, BardCookies
cookie_dict = {
"__Secure-1PSID" : "xxxxxxxxxxxxxxxxxxxxxx",
"__Secure-1PAPISID" : "xxxxxxxxxxxxxxxxxxxxx",
"__Secure-1PSIDCC" : "xxxxxxxxxxxxxxxxxxxxx",
}
bard = BardCookies(cookie_dict = cookie_dict)
result = bard.get_answer("how to use ChatGPT?")
print(result['content'])
I am still getting a response error :
Response Error: b')]}\'\n\n38\n[["wrb.fr",null,null,null,null,[9]]]\n56\n[["di",367],["af.httprm",367,"2847306205436971809",7]]\n25\n[["e",4,null,null,131]]\n'.
Unable to get response.
Please double-check the cookie values and verify your network environment or google account. | closed | 2023-10-10T06:25:19Z | 2024-01-18T15:48:43Z | https://github.com/dsdanielpark/Bard-API/issues/206 | [] | MananAg-1784 | 4 |
quokkaproject/quokka | flask | 69 | Error upload image Content -> Image | Admin -> Content -> Image
Error:
Failed to create model. decoder jpeg not available
| closed | 2013-10-23T09:48:06Z | 2015-07-16T02:56:41Z | https://github.com/quokkaproject/quokka/issues/69 | [] | jniltinho | 6 |
man-group/arctic | pandas | 69 | tickstore query slowly | Arctic said that can query millions of rows per second per client, but when I try to use it in our team, and found that it only thousand of rows per second, Here the code, Does anyone got the same problem or I use it with wrong way.
``` shell
@property
def arctic(self):
if not self._arctic:
log.info("init arctic")
mongo_conn = MongoDB()
self._arctic = Arctic(mongo_host=mongo_conn.client)
library = self._arctic.list_libraries()
if self.tick_db not in library:
self._arctic.initialize_library(self.tick_db, lib_type=arctic.TICK_STORE)
if self.bar_db not in library:
self._arctic.initialize_library(self.bar_db, lib_type=arctic.TICK_STORE)
return self._arctic
...
# res is a dict of tick data
index = self.int_to_date(tick_time)
data = pd.DataFrame(res, [index])
self.arctic[self.tick_db].write(symbol, data)
...
>>> now = time.time(); ac['tick'].read('IF1601', date_range=dr); print(time.time() - now)
Output：
[4021 rows x 26 columns]
3.56284999847
```
thanks.
| closed | 2015-12-24T01:47:22Z | 2015-12-31T08:28:24Z | https://github.com/man-group/arctic/issues/69 | [] | zoe0316 | 5 |
paperless-ngx/paperless-ngx | django | 7,969 | [BUG] PAPERLESS_CONSUMER_POLLING doesn't seem to be taking effect. | ### Description
**ENVIRONMENT**
I have paperless deployed as a set of containers on a server. My core volumes are mounted to the service via NFS and mapped to the containers via the docker-compose.yml file.
```
volumes:
- /mnt/files/paperless-ngx:/usr/src/paperless/data
- /mnt/files/paperless-ngx:/usr/src/paperless/media
- ./export:/usr/src/paperless/export
- /mnt/files/paperless-ngx/uploads:/usr/src/paperless/consume
```
I know that as my "consume folder" is mapped via NFS, I will need to enable `PAPERLESS_CONSUMER_POLLING=<num>`, which I have done in the docker-compose.env file. I can see these settings through the docker gui as well. (see attached screenshot)
```
PAPERLESS_URL=<url>
PAPERLESS_TIME_ZONE=Etc/UTC
PAPERLESS_OCR_LANGUAGE=eng
PAPERLESS_SECRET_KEY=<secret key>
PAPERLESS_CONSUMER_POLLING=30
```
<img width="602" alt="2024-10-20_23-34-45" src="https://github.com/user-attachments/assets/fbb76034-cafa-4417-9750-40275b2d0391">
**ISSUE:**
I don't believe the `PAPERLESS_CONSUMER_POLLING` varible is taking effect; on 2 counts:
1. Files dropped in to the mapped **Consume** folder are not being processed, within the expected timeframe.
2. If I restart the webserver, the files are being processed on startup
3. On startup, I'm seeing the following message in the logs:
`[2024-10-20 22:28:34,134] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume`
Based on the [Configuration Documentation](https://docs.paperless-ngx.com/configuration/#PAPERLESS_CONSUMER_POLLING), I would have expected `inotify` to be disabled when `PAPERLESS_CONSUMER_POLLING` is enabled.
Please can you let me know if I'm doing something wrong or if this is a possible bug?
This is an amazing product and I'd love to see this feature working as expected without me restarting the web server every time I want to process uploaded files.
Many thanks in advance.
Bavo
### Steps to reproduce
explained above
### Webserver logs
```bash
[2024-10-20 22:25:39,220] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2024-10-20 22:28:13,995] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2024-10-20 22:28:14,003] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2024-10-20 22:28:20,130] [INFO] [paperless.auth] Login failed for user `bavo4` from IP `31.51.121.161`.
[2024-10-20 22:28:34,134] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
```
### Browser logs
_No response_
### Paperless-ngx version
2.12.1
### Host OS
Ubuntu 24.04 - Docker 25.0.2, build 29cf629
### Installation method
Docker - official image
### System status
```json
{
"pngx_version": "2.12.1",
"server_os": "Linux-6.8.0-47-generic-x86_64-with-glibc2.36",
"install_type": "docker",
"storage": {
"total": 11892816101376,
"available": 2433494769664
},
"database": {
"type": "postgresql",
"url": "paperless",
"status": "OK",
"error": null,
"migration_status": {
"latest_migration": "paperless_mail.0011_remove_mailrule_assign_tag_squashed_0024_alter_mailrule_name_and_more",
"unapplied_migrations": []
}
},
"tasks": {
"redis_url": "redis://broker:6379",
"redis_status": "OK",
"redis_error": null,
"celery_status": "OK",
"index_status": "OK",
"index_last_modified": "2024-10-20T22:25:11.855280Z",
"index_error": null,
"classifier_status": "OK",
"classifier_last_trained": null,
"classifier_error": null
}
}
```
### Browser
_No response_
### Configuration changes
_No response_
### Please confirm the following
- [X] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [X] This issue is not about the OCR or archive creation of a specific file(s). Otherwise, please see above regarding OCR tools.
- [X] I have already searched for relevant existing issues and discussions before opening this report.
- [X] I have updated the title field above with a concise description. | closed | 2024-10-20T22:44:34Z | 2024-10-20T22:56:54Z | https://github.com/paperless-ngx/paperless-ngx/issues/7969 | [
"not a bug"
] | bavo14 | 0 |
huggingface/diffusers | deep-learning | 10,741 | FluxControlNetImg2ImgPipeline doesn't support generating more than one image | ### Describe the bug
The FluxControlNetImg2ImgPipeline does not support generating more than one image.
The error encountered is: RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 2 but got size 1 for tensor number 1 in the list.
I figured out that the control_mode needs to be sent as a list of control_mode values, matching the requested number of images specified by the num_images_per_prompt parameter.
As I see it, in the file pipeline_flux_controlnet_image_to_image.py, at line 818, the following code needs to be added:
if control_mode is not None:
if batch_size * num_images_per_prompt > 1:
control_mode = [control_mode] * batch_size * num_images_per_prompt
control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
control_mode = control_mode.reshape([-1, 1])
Does this make sense? Would you like PR on this fix?
### Reproduction
FluxControlNetImg2ImgPipeline with num_images_per_prompt=2
### System Info
diffusers = 0.32.2
### Who can help?
@sayakpaul @yiyixuxu | open | 2025-02-06T16:24:17Z | 2025-03-09T15:02:48Z | https://github.com/huggingface/diffusers/issues/10741 | [
"bug",
"stale"
] | liorRabkin | 1 |
sanic-org/sanic | asyncio | 2,862 | New websockets, handling disconnects | ### Is there an existing issue for this?
- [X] I have searched the existing issues
### Is your feature request related to a problem? Please describe.
I'm using version 23.6.0, so it's new websockets. I have followed the [guide](https://sanic.dev/en/guide/advanced/websockets.html#routing) to build something like this:
async def handler(request, ws):
while True:
data = "hello!"
try:
await ws.send(data)
data = await ws.recv()
except Exception as e:
print(e)
break
clean_up()
bp.add_websocket_route(handler, '/ws')
I discovered that when the client disconnects, no exceptions are thrown in this above code. Making it difficult for me to clean up things. What's worse is that the execution of this handler somehow stops in a disconnect, no subsequent code gets run.
I read the source code and figured out the following mechanism to tackle a disconnect:
def ws_disconnect(my_mess, fut):
clean_up(my_mess)
async def handler(request, ws):
ws.connection_lost_waiter.add_done_callback(functools.partial(ws_disconnect, "my_mess"))
...
It works, as the WebsocketImplProtocol calls it upon disconnect. But I could help but thinking there should be a better mechanism to handle websocket clean ups. What am I supposed to do to handle disconnects?
### Describe the solution you'd like
Keep the handler running upon disconnect, and give it a chance to handle exception or check for "connected" attribute.
### Additional context
_No response_ | closed | 2023-11-30T14:50:39Z | 2023-12-01T03:06:27Z | https://github.com/sanic-org/sanic/issues/2862 | [
"feature request"
] | heshiming | 2 |
deepspeedai/DeepSpeed | machine-learning | 6,526 | [REQUEST] parallelize zero_to_fp32.py to use multiple cpu-cores and threads | When https://github.com/microsoft/DeepSpeed/blob/c27483933d50a693fef9c48418d2664cf6a6a6f8/deepspeed/utils/zero_to_fp32.py was written 3 years ago models were small and converted fast. Now with 70B+ models the conversion can take hours.
The original script uses a single cpu core.
Here is a possible implementation algorithm:
The way I was thinking multiple cores could be utilized by loading all shards into the cpu memory and then firing off multiple threads, each re-composing a single layer - the user could specify how many cores to use or by default all cores will be used - so that `n_threads == cores`. I think the total memory usage here will still be `2x model size * dtype` just like in the original script.
Possible additional changes:
- Using `safetensors` would be a bonus because then each tensor could be written separately and there is no need to wait for the whole model to be unsharded to write a single torch tensor. This could also become an option for low RAM nodes, where each layer is unsharded sequentially and total memory usage will be `1x model size * dtype` + `max layer size * dtype`, which for a large model be a huge memory saving, at the cost of not parallelizing - or perhaps using just 1-2 threads, which would already speed things up.
- Switching to universal checkpoint API would be another bonus because the original is very clunky and very difficult to understand/maintain.
cc: @tjruwase
| open | 2024-09-11T22:52:42Z | 2024-10-14T03:10:41Z | https://github.com/deepspeedai/DeepSpeed/issues/6526 | [
"enhancement"
] | stas00 | 4 |
docarray/docarray | fastapi | 1,137 | Handle da inside da in stack mode |
```python
from docarray import BaseDocument, DocumentArray
from docarray.typing import AnyTensor
class Image(BaseDocument):
tensor : AnyTensor
class Video(BaseDocument):
images: DocumentArray[Image]
da = DocumentArray[Video]( Video(images = DocumentArray[Image]([ Image()])])
da.stack()
da[0].images # is not stack but should be
```
in this case `da.stack()` will not stack the tensor in Image because of the nested DocumentArray
this will probably be solved by introducing the concept of dimension in document array | closed | 2023-02-15T13:45:41Z | 2023-02-21T01:37:05Z | https://github.com/docarray/docarray/issues/1137 | [] | AnneYang720 | 0 |
onnx/onnx | pytorch | 6,152 | Missing type support in parser for various types (float16, bfloat16, ...) | # Bug Report
### Is the issue related to model conversion?
No.
### Describe the bug
The parser has only partial support for data type parsing: https://github.com/onnx/onnx/blob/093a8d335a66ea136eb1f16b3a1ce6237ee353ab/onnx/defs/parser.cc#L436
The missing types are:
```
TensorProto_DataType_FLOAT16
TensorProto_DataType_BFLOAT16
TensorProto_DataType_FLOAT8E4M3FN
TensorProto_DataType_FLOAT8E4M3FNUZ
TensorProto_DataType_FLOAT8E5M2
TensorProto_DataType_FLOAT8E5M2FNUZ
TensorProto_DataType_COMPLEX64
TensorProto_DataType_COMPLEX128
```
### System information
System independent.
### Reproduction instructions
Find a sample model and instruction for float16 below; note however that it would be great to support all types not just float16. The error will be the same for the other types.
Sample model:
[gemmfloat16.onnxtxt](https://github.com/user-attachments/files/15513562/gemmfloat16.onnxtxt.txt)
Sample instructions:
```
import onnx
from pathlib import Path
onnx.parser.parse_model(Path("gemmfloat16.onnxtxt.txt").read_text())
```
Result:
`onnx.parser.ParseError: b'[ParseError at position (...)]\nError context: <float16[4, 4] weight = {...}, float16[4] bias = {...}>\nUnhandled type: %d10'`
(Note: The `.onnxtxt` format is not allowed to be uploaded therefore the sample is `.onnxtxt.txt`)
### Expected behavior
ONNX model is parsed successfully. | open | 2024-05-31T12:50:30Z | 2025-01-24T15:02:17Z | https://github.com/onnx/onnx/issues/6152 | [
"bug",
"module: parser",
"contributions welcome"
] | TinaAMD | 1 |
open-mmlab/mmdetection | pytorch | 11,820 | YOLOX input normalisation question | Greatings!
https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/yolox/yolox_s_8xb8-300e_coco.py#L16
I can not see any normalisation step for this model (any YOLOX model actually).
Please can you point me to it?
Best regards! | open | 2024-06-30T20:16:36Z | 2024-06-30T20:16:51Z | https://github.com/open-mmlab/mmdetection/issues/11820 | [] | dmitrysarov | 0 |
PaddlePaddle/ERNIE | nlp | 171 | 论文中用到的BERT是你们重新训练的，还是直接用谷歌开放的？ | 在论文里面没有提到这个细节。 | closed | 2019-06-19T08:14:08Z | 2019-06-21T08:08:06Z | https://github.com/PaddlePaddle/ERNIE/issues/171 | [] | rainarch | 2 |
scikit-learn-contrib/metric-learn | scikit-learn | 272 | Unconsistent behaviour of SDML when ussing skggm with fixed seed. | #### Description
I was using SDML_Supervised() for a subsequent 2D visualization with UMAP (Similar to t-sne) and got large differences in the results on every fit instance while using the same data. Fixing the seed doesn't make a difference. I tracked down the problem to the call of quic() done when skggm is installed, reviewing their code I found there is a fixed seed but anyway the results from that function vary in every call.
note: I am using the latest version from Skggm, will try to reproduce later with the version indicated in the documentation.
#### Steps/Code to Reproduce
```python
from metric_learn import SDML_Supervised
from sklearn.datasets import load_wine
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
wine=load_wine()
X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
SDML=SDML_Supervised(random_state=42)
X_transform=SDML.fit_transform(X_train,y_train)
print(np.sum(np.abs(X_transform - SDML.fit_transform(X_train,y_train))))
```
#### Expected Results
The two instances of SDML fit should have the same result, then the printed difference should be zero.
#### Actual Results
Large numbers in the order of 100 to 300.
#### Versions
Linux-5.0.0-37-generic-x86_64-with-Ubuntu-18.04-bionic
Python 3.6.9 (default, Nov 7 2019, 10:44:02)
[GCC 8.3.0]
NumPy 1.18.1
SciPy 1.4.1
Scikit-Learn 0.22.1
Metric-Learn 0.5.0
Skggm 0.2.8
| closed | 2020-01-13T09:38:45Z | 2020-01-13T15:28:05Z | https://github.com/scikit-learn-contrib/metric-learn/issues/272 | [] | grudloff | 4 |
saulpw/visidata | pandas | 1,499 | Test fails in Guix build: file missing from distribution tarball? | **Steps to reproduce**
1. Check out my [updated Visidata 2.10 package for Guix](https://github.com/ryanprior/guix/blob/update-guix-2.10/gnu/packages/spreadsheet.scm#L86-L119)
2. Run `pre-inst-env guix build visidata`
**Expected result**
Package build should complete with no errors.
**Actual result with screenshot**
During the test phase, pytest errors out complaining that it can't find a necessary file:
```
self = PosixPath('/tmp/guix-build-visidata-2.10.drv-0/visidata-2.10/visidata/../sample_data/sample.tsv')
name = '/tmp/guix-build-visidata-2.10.drv-0/visidata-2.10/visidata/../sample_data/sample.tsv'
flags = 524288, mode = 438
def _opener(self, name, flags, mode=0o666):
# A stub for the opener argument to built-in open()
> return self._accessor.open(self, flags, mode)
E FileNotFoundError: [Errno 2] No such file or directory: '/tmp/guix-build-visidata-2.10.drv-0/visidata-2.10/visidata/../sample_data/sample.tsv'
/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/lib/python3.9/pathlib.py:1120: FileNotFoundError
```
<details>
<summary>View full package build output (733 lines)</summary>
<pre>
starting phase `set-SOURCE-DATE-EPOCH'
phase `set-SOURCE-DATE-EPOCH' succeeded after 0.0 seconds
starting phase `set-paths'
environment variable `PATH' set to `/gnu/store/slsh0qjv5j68xda2bb6h8gsxwyi1j25a-python-wrapper-3.9.9/bin:/gnu/store/7frqm5ijy66f81hr8i1j6791k84lds9w-python-pytest-6.2.5/bin:/gnu/store/6pzbvnfvgdxfar4qhms0d81w9d6n1ylp-python-xlrd-2.0.1/bin:/gnu/store/g2ajyl8xk9aarxrgjbng2hkj3qm2v0z2-tar-1.34/bin:/gnu/store/iixwcv3k49ks1rf34pjgfzmzyhhgwng3-gzip-1.10/bin:/gnu/store/s3hl12jxz9ybs7nsy7kq7ybzz7qnzmsg-bzip2-1.0.8/bin:/gnu/store/c8isj4jq6knv0icfgr43di6q3nvdzkx7-xz-5.2.5/bin:/gnu/store/4ic6244i3ca4b4rxc2wnrgllsidyishv-file-5.39/bin:/gnu/store/ahmmvw21p11ik80lg1f953y7fd8bqkjm-diffutils-3.8/bin:/gnu/store/z39hnrwds1dgcbpfgj8dnv2cngjb2xbl-patch-2.7.6/bin:/gnu/store/39rsx3nl4c31952jybbjb8d6idr5hx7r-findutils-4.8.0/bin:/gnu/store/690qz3fg334dpwn3pn6k59n4wc943p2b-gawk-5.1.0/bin:/gnu/store/wxgv6i8g0p24q5gcyzd0yr07s8kn9680-sed-4.8/bin:/gnu/store/xjwp2hsd9256icjjybfrmznppjicywf6-grep-3.6/bin:/gnu/store/d251rfgc9nm2clzffzhgiipdvfvzkvwi-coreutils-8.32/bin:/gnu/store/55cbpsi18mahg131nmiya6km5b4mscfa-make-4.3/bin:/gnu/store/4y5m9lb8k3qkb1y9m02sw9w9a6hacd16-bash-minimal-5.1.8/bin:/gnu/store/s2pg5k98fl2g2szg9dykxyd9zl3xihv9-ld-wrapper-0/bin:/gnu/store/rc781v4k0drhaqn90xfwwpspki5x0bvf-binutils-2.37/bin:/gnu/store/069aq2v993kpc41yabp5b6vm4wb9jkhg-gcc-10.3.0/bin:/gnu/store/5h2w4qi9hk1qzzgi1w83220ydslinr4s-glibc-2.33/bin:/gnu/store/5h2w4qi9hk1qzzgi1w83220ydslinr4s-glibc-2.33/sbin:/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/bin:/gnu/store/pwzh4npbxhm1rqrbg9lra99wx6sinkmf-python-charset-normalizer-2.0.11/bin'
environment variable `GUIX_PYTHONPATH' set to `/gnu/store/7frqm5ijy66f81hr8i1j6791k84lds9w-python-pytest-6.2.5/lib/python3.9/site-packages:/gnu/store/xs8pxa4rr2zkb2hr5nhkxr7ijxxgmqna-python-dateutil-2.8.2/lib/python3.9/site-packages:/gnu/store/gjv07rwkais79cr0m2vy4ia2xb3irwx5-python-requests-2.27.1/lib/python3.9/site-packages:/gnu/store/pv2gvq3im4czlpdrh0pb7jjkkib19qvy-python-lxml-4.6.3/lib/python3.9/site-packages:/gnu/store/x2maq7qxliy26mzfq1nic7mpjmyx8r7j-python-openpyxl-3.0.9/lib/python3.9/site-packages:/gnu/store/6pzbvnfvgdxfar4qhms0d81w9d6n1ylp-python-xlrd-2.0.1/lib/python3.9/site-packages:/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/lib/python3.9/site-packages:/gnu/store/rh5pmm5ralyl06pgfr83qlfxaav6svjr-python-wcwidth-0.1.8/lib/python3.9/site-packages:/gnu/store/hmyqhci2vvrnvjwm32l26kwgasz7in1w-python-toml-0.10.2/lib/python3.9/site-packages:/gnu/store/xqvr1b5065idb5y8jxgr42cdkiwj6l64-python-six-bootstrap-1.16.0/lib/python3.9/site-packages:/gnu/store/ls2xsqbwsha2ap65dh09f9v0q0v43d91-python-py-1.10.0/lib/python3.9/site-packages:/gnu/store/jl1g2qqlg9sjxrh649x8zp2ysy4fmwh5-python-pluggy-0.13.1/lib/python3.9/site-packages:/gnu/store/jbb1l7nqy3dskqy8i835p8wbi871dmsy-python-packaging-bootstrap-21.3/lib/python3.9/site-packages:/gnu/store/driz9p0pv29s9dlpd7r8m5r65wiia30z-python-more-itertools-8.2.0/lib/python3.9/site-packages:/gnu/store/sww1f0qbddpnj7p1pivrsva83xn7c711-python-iniconfig-1.1.1/lib/python3.9/site-packages:/gnu/store/wp31hr5sia5wydha04ijqiz2kdhck4y0-python-attrs-bootstrap-21.2.0/lib/python3.9/site-packages:/gnu/store/3bjjwwwbniv92j0cg8kp1h5k2q3c42n3-python-six-1.16.0/lib/python3.9/site-packages:/gnu/store/9bzm9zhbw6zk9ynhzx1qhzhzardd419w-python-urllib3-1.26.8/lib/python3.9/site-packages:/gnu/store/hj74j5jjqr55qy8ldvs23rlxanr3f1l7-python-idna-3.3/lib/python3.9/site-packages:/gnu/store/pwzh4npbxhm1rqrbg9lra99wx6sinkmf-python-charset-normalizer-2.0.11/lib/python3.9/site-packages:/gnu/store/sgs95j3njwkkw752iy4i8rycn37cs69z-python-certifi-2021.10.8/lib/python3.9/site-packages:/gnu/store/qmkk3jpm693wajkb1p1w5njf6z7rmpcm-python-jdcal-1.4/lib/python3.9/site-packages:/gnu/store/s3jjk7n2hpwwn6vcxk9l9v19ns4mpnb3-python-et-xmlfile-1.0.1/lib/python3.9/site-packages:/gnu/store/kw41zfr5s3ay2xva287lcpgzqa7bhvh1-python-pyparsing-3.0.6/lib/python3.9/site-packages:/gnu/store/rm5hqml7c77psn8b7gsz4hqabb3xxkq6-python-pysocks-1.7.1/lib/python3.9/site-packages:/gnu/store/0fz1vanf4n5c4b5b5jh3p9sk36p6kfix-python-pyopenssl-21.0.0/lib/python3.9/site-packages:/gnu/store/rgkhhsivbqw737d5zrpj1ql540m37wh8-python-cryptography-3.4.8/lib/python3.9/site-packages:/gnu/store/xr7l6fkj6fwvxyvamxllsp7x0zm4j3ag-python-iso8601-1.0.2/lib/python3.9/site-packages:/gnu/store/4hzdy7w8mnxpi3054m46xd00ixqfhb6g-python-cffi-1.14.4/lib/python3.9/site-packages:/gnu/store/cnzvnhjqwgiqdlyyrkrpb4vkydxm4din-python-asn1crypto-1.4.0/lib/python3.9/site-packages:/gnu/store/1mdg7xc4zx0i9s0kd0hwwq98bgab51s1-python-pycparser-2.21/lib/python3.9/site-packages'
environment variable `PYTHONTZPATH' unset
environment variable `BASH_LOADABLES_PATH' unset
environment variable `C_INCLUDE_PATH' set to `/gnu/store/s3hl12jxz9ybs7nsy7kq7ybzz7qnzmsg-bzip2-1.0.8/include:/gnu/store/c8isj4jq6knv0icfgr43di6q3nvdzkx7-xz-5.2.5/include:/gnu/store/4ic6244i3ca4b4rxc2wnrgllsidyishv-file-5.39/include:/gnu/store/690qz3fg334dpwn3pn6k59n4wc943p2b-gawk-5.1.0/include:/gnu/store/55cbpsi18mahg131nmiya6km5b4mscfa-make-4.3/include:/gnu/store/rc781v4k0drhaqn90xfwwpspki5x0bvf-binutils-2.37/include:/gnu/store/069aq2v993kpc41yabp5b6vm4wb9jkhg-gcc-10.3.0/include:/gnu/store/5h2w4qi9hk1qzzgi1w83220ydslinr4s-glibc-2.33/include:/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/include:/gnu/store/6mjww4iz4xdan74d5bbjfh7il8rngfkk-linux-libre-headers-5.10.35/include'
environment variable `CPLUS_INCLUDE_PATH' set to `/gnu/store/s3hl12jxz9ybs7nsy7kq7ybzz7qnzmsg-bzip2-1.0.8/include:/gnu/store/c8isj4jq6knv0icfgr43di6q3nvdzkx7-xz-5.2.5/include:/gnu/store/4ic6244i3ca4b4rxc2wnrgllsidyishv-file-5.39/include:/gnu/store/690qz3fg334dpwn3pn6k59n4wc943p2b-gawk-5.1.0/include:/gnu/store/55cbpsi18mahg131nmiya6km5b4mscfa-make-4.3/include:/gnu/store/rc781v4k0drhaqn90xfwwpspki5x0bvf-binutils-2.37/include:/gnu/store/069aq2v993kpc41yabp5b6vm4wb9jkhg-gcc-10.3.0/include/c++:/gnu/store/069aq2v993kpc41yabp5b6vm4wb9jkhg-gcc-10.3.0/include:/gnu/store/5h2w4qi9hk1qzzgi1w83220ydslinr4s-glibc-2.33/include:/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/include:/gnu/store/6mjww4iz4xdan74d5bbjfh7il8rngfkk-linux-libre-headers-5.10.35/include'
environment variable `LIBRARY_PATH' set to `/gnu/store/7frqm5ijy66f81hr8i1j6791k84lds9w-python-pytest-6.2.5/lib:/gnu/store/xs8pxa4rr2zkb2hr5nhkxr7ijxxgmqna-python-dateutil-2.8.2/lib:/gnu/store/gjv07rwkais79cr0m2vy4ia2xb3irwx5-python-requests-2.27.1/lib:/gnu/store/pv2gvq3im4czlpdrh0pb7jjkkib19qvy-python-lxml-4.6.3/lib:/gnu/store/x2maq7qxliy26mzfq1nic7mpjmyx8r7j-python-openpyxl-3.0.9/lib:/gnu/store/6pzbvnfvgdxfar4qhms0d81w9d6n1ylp-python-xlrd-2.0.1/lib:/gnu/store/s3hl12jxz9ybs7nsy7kq7ybzz7qnzmsg-bzip2-1.0.8/lib:/gnu/store/c8isj4jq6knv0icfgr43di6q3nvdzkx7-xz-5.2.5/lib:/gnu/store/4ic6244i3ca4b4rxc2wnrgllsidyishv-file-5.39/lib:/gnu/store/690qz3fg334dpwn3pn6k59n4wc943p2b-gawk-5.1.0/lib:/gnu/store/rc781v4k0drhaqn90xfwwpspki5x0bvf-binutils-2.37/lib:/gnu/store/5h2w4qi9hk1qzzgi1w83220ydslinr4s-glibc-2.33/lib:/gnu/store/4jdghmc65q7i7ib89zmvq66l0ghf7jc4-glibc-2.33-static/lib:/gnu/store/fnr1z6xsan0437r0yg48d0y8k32kqxby-glibc-utf8-locales-2.33/lib:/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/lib:/gnu/store/rh5pmm5ralyl06pgfr83qlfxaav6svjr-python-wcwidth-0.1.8/lib:/gnu/store/hmyqhci2vvrnvjwm32l26kwgasz7in1w-python-toml-0.10.2/lib:/gnu/store/xqvr1b5065idb5y8jxgr42cdkiwj6l64-python-six-bootstrap-1.16.0/lib:/gnu/store/ls2xsqbwsha2ap65dh09f9v0q0v43d91-python-py-1.10.0/lib:/gnu/store/jl1g2qqlg9sjxrh649x8zp2ysy4fmwh5-python-pluggy-0.13.1/lib:/gnu/store/jbb1l7nqy3dskqy8i835p8wbi871dmsy-python-packaging-bootstrap-21.3/lib:/gnu/store/driz9p0pv29s9dlpd7r8m5r65wiia30z-python-more-itertools-8.2.0/lib:/gnu/store/sww1f0qbddpnj7p1pivrsva83xn7c711-python-iniconfig-1.1.1/lib:/gnu/store/wp31hr5sia5wydha04ijqiz2kdhck4y0-python-attrs-bootstrap-21.2.0/lib:/gnu/store/3bjjwwwbniv92j0cg8kp1h5k2q3c42n3-python-six-1.16.0/lib:/gnu/store/9bzm9zhbw6zk9ynhzx1qhzhzardd419w-python-urllib3-1.26.8/lib:/gnu/store/hj74j5jjqr55qy8ldvs23rlxanr3f1l7-python-idna-3.3/lib:/gnu/store/pwzh4npbxhm1rqrbg9lra99wx6sinkmf-python-charset-normalizer-2.0.11/lib:/gnu/store/sgs95j3njwkkw752iy4i8rycn37cs69z-python-certifi-2021.10.8/lib:/gnu/store/qmkk3jpm693wajkb1p1w5njf6z7rmpcm-python-jdcal-1.4/lib:/gnu/store/s3jjk7n2hpwwn6vcxk9l9v19ns4mpnb3-python-et-xmlfile-1.0.1/lib:/gnu/store/kw41zfr5s3ay2xva287lcpgzqa7bhvh1-python-pyparsing-3.0.6/lib:/gnu/store/rm5hqml7c77psn8b7gsz4hqabb3xxkq6-python-pysocks-1.7.1/lib:/gnu/store/0fz1vanf4n5c4b5b5jh3p9sk36p6kfix-python-pyopenssl-21.0.0/lib:/gnu/store/rgkhhsivbqw737d5zrpj1ql540m37wh8-python-cryptography-3.4.8/lib:/gnu/store/xr7l6fkj6fwvxyvamxllsp7x0zm4j3ag-python-iso8601-1.0.2/lib:/gnu/store/4hzdy7w8mnxpi3054m46xd00ixqfhb6g-python-cffi-1.14.4/lib:/gnu/store/cnzvnhjqwgiqdlyyrkrpb4vkydxm4din-python-asn1crypto-1.4.0/lib:/gnu/store/1mdg7xc4zx0i9s0kd0hwwq98bgab51s1-python-pycparser-2.21/lib'
environment variable `GUIX_LOCPATH' set to `/gnu/store/fnr1z6xsan0437r0yg48d0y8k32kqxby-glibc-utf8-locales-2.33/lib/locale'
phase `set-paths' succeeded after 0.1 seconds
starting phase `install-locale'
using 'en_US.utf8' locale for category "LC_ALL"
phase `install-locale' succeeded after 0.0 seconds
starting phase `unpack'
visidata-2.10/
visidata-2.10/LICENSE.gpl3
visidata-2.10/MANIFEST.in
visidata-2.10/PKG-INFO
visidata-2.10/README.md
visidata-2.10/bin/
visidata-2.10/bin/vd
visidata-2.10/setup.cfg
visidata-2.10/setup.py
visidata-2.10/visidata/
visidata-2.10/visidata/__init__.py
visidata-2.10/visidata/__main__.py
visidata-2.10/visidata/_input.py
visidata-2.10/visidata/_open.py
visidata-2.10/visidata/_types.py
visidata-2.10/visidata/_urlcache.py
visidata-2.10/visidata/aggregators.py
visidata-2.10/visidata/basesheet.py
visidata-2.10/visidata/bezier.py
visidata-2.10/visidata/canvas.py
visidata-2.10/visidata/canvas_text.py
visidata-2.10/visidata/choose.py
visidata-2.10/visidata/clipboard.py
visidata-2.10/visidata/cliptext.py
visidata-2.10/visidata/cmdlog.py
visidata-2.10/visidata/color.py
visidata-2.10/visidata/colorsheet.py
visidata-2.10/visidata/column.py
visidata-2.10/visidata/customdate.py
visidata-2.10/visidata/ddw/
visidata-2.10/visidata/ddw/input.ddw
visidata-2.10/visidata/ddwplay.py
visidata-2.10/visidata/deprecated.py
visidata-2.10/visidata/describe.py
visidata-2.10/visidata/editor.py
visidata-2.10/visidata/errors.py
visidata-2.10/visidata/expr.py
visidata-2.10/visidata/extensible.py
visidata-2.10/visidata/fill.py
visidata-2.10/visidata/form.py
visidata-2.10/visidata/freeze.py
visidata-2.10/visidata/freqtbl.py
visidata-2.10/visidata/graph.py
visidata-2.10/visidata/help.py
visidata-2.10/visidata/incr.py
visidata-2.10/visidata/join.py
visidata-2.10/visidata/keys.py
visidata-2.10/visidata/layout.py
visidata-2.10/visidata/loaders/
visidata-2.10/visidata/loaders/__init__.py
visidata-2.10/visidata/loaders/_pandas.py
visidata-2.10/visidata/loaders/archive.py
visidata-2.10/visidata/loaders/arrow.py
visidata-2.10/visidata/loaders/csv.py
visidata-2.10/visidata/loaders/eml.py
visidata-2.10/visidata/loaders/fixed_width.py
visidata-2.10/visidata/loaders/frictionless.py
visidata-2.10/visidata/loaders/geojson.py
visidata-2.10/visidata/loaders/graphviz.py
visidata-2.10/visidata/loaders/hdf5.py
visidata-2.10/visidata/loaders/html.py
visidata-2.10/visidata/loaders/http.py
visidata-2.10/visidata/loaders/imap.py
visidata-2.10/visidata/loaders/json.py
visidata-2.10/visidata/loaders/lsv.py
visidata-2.10/visidata/loaders/markdown.py
visidata-2.10/visidata/loaders/mbtiles.py
visidata-2.10/visidata/loaders/mysql.py
visidata-2.10/visidata/loaders/npy.py
visidata-2.10/visidata/loaders/odf.py
visidata-2.10/visidata/loaders/pandas_freqtbl.py
visidata-2.10/visidata/loaders/parquet.py
visidata-2.10/visidata/loaders/pcap.py
visidata-2.10/visidata/loaders/pdf.py
visidata-2.10/visidata/loaders/png.py
visidata-2.10/visidata/loaders/postgres.py
visidata-2.10/visidata/loaders/rec.py
visidata-2.10/visidata/loaders/sas.py
visidata-2.10/visidata/loaders/shp.py
visidata-2.10/visidata/loaders/spss.py
visidata-2.10/visidata/loaders/sqlite.py
visidata-2.10/visidata/loaders/texttables.py
visidata-2.10/visidata/loaders/tsv.py
visidata-2.10/visidata/loaders/ttf.py
visidata-2.10/visidata/loaders/unzip_http.py
visidata-2.10/visidata/loaders/usv.py
visidata-2.10/visidata/loaders/vcf.py
visidata-2.10/visidata/loaders/vds.py
visidata-2.10/visidata/loaders/xlsb.py
visidata-2.10/visidata/loaders/xlsx.py
visidata-2.10/visidata/loaders/xml.py
visidata-2.10/visidata/loaders/xword.py
visidata-2.10/visidata/loaders/yaml.py
visidata-2.10/visidata/macos.py
visidata-2.10/visidata/macros.py
visidata-2.10/visidata/main.py
visidata-2.10/visidata/mainloop.py
visidata-2.10/visidata/man/
visidata-2.10/visidata/man/vd.1
visidata-2.10/visidata/man/vd.txt
visidata-2.10/visidata/man/visidata.1
visidata-2.10/visidata/melt.py
visidata-2.10/visidata/memory.py
visidata-2.10/visidata/menu.py
visidata-2.10/visidata/metasheets.py
visidata-2.10/visidata/misc.py
visidata-2.10/visidata/modify.py
visidata-2.10/visidata/motd.py
visidata-2.10/visidata/movement.py
visidata-2.10/visidata/path.py
visidata-2.10/visidata/pivot.py
visidata-2.10/visidata/plugins.py
visidata-2.10/visidata/pyobj.py
visidata-2.10/visidata/regex.py
visidata-2.10/visidata/save.py
visidata-2.10/visidata/search.py
visidata-2.10/visidata/selection.py
visidata-2.10/visidata/settings.py
visidata-2.10/visidata/sheets.py
visidata-2.10/visidata/shell.py
visidata-2.10/visidata/slide.py
visidata-2.10/visidata/sort.py
visidata-2.10/visidata/statusbar.py
visidata-2.10/visidata/tests/
visidata-2.10/visidata/tests/__init__.py
visidata-2.10/visidata/tests/conftest.py
visidata-2.10/visidata/tests/test_commands.py
visidata-2.10/visidata/tests/test_edittext.py
visidata-2.10/visidata/tests/test_path.py
visidata-2.10/visidata/textsheet.py
visidata-2.10/visidata/threads.py
visidata-2.10/visidata/transpose.py
visidata-2.10/visidata/undo.py
visidata-2.10/visidata/unfurl.py
visidata-2.10/visidata/utils.py
visidata-2.10/visidata/vdobj.py
visidata-2.10/visidata/vendor/
visidata-2.10/visidata/vendor/appdirs.py
visidata-2.10/visidata/window.py
visidata-2.10/visidata/wrappers.py
visidata-2.10/visidata.egg-info/
visidata-2.10/visidata.egg-info/PKG-INFO
visidata-2.10/visidata.egg-info/SOURCES.txt
visidata-2.10/visidata.egg-info/dependency_links.txt
visidata-2.10/visidata.egg-info/entry_points.txt
visidata-2.10/visidata.egg-info/requires.txt
visidata-2.10/visidata.egg-info/top_level.txt
phase `unpack' succeeded after 0.0 seconds
starting phase `ensure-no-mtimes-pre-1980'
phase `ensure-no-mtimes-pre-1980' succeeded after 0.0 seconds
starting phase `enable-bytecode-determinism'
phase `enable-bytecode-determinism' succeeded after 0.0 seconds
starting phase `ensure-no-cythonized-files'
phase `ensure-no-cythonized-files' succeeded after 0.0 seconds
starting phase `patch-usr-bin-file'
phase `patch-usr-bin-file' succeeded after 0.0 seconds
starting phase `patch-source-shebangs'
patch-shebang: ./bin/vd: changing `/usr/bin/env python3' to `/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/bin/python3'
patch-shebang: ./setup.py: changing `/usr/bin/env python3' to `/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/bin/python3'
phase `patch-source-shebangs' succeeded after 0.0 seconds
starting phase `patch-generated-file-shebangs'
phase `patch-generated-file-shebangs' succeeded after 0.0 seconds
starting phase `build'
running "python setup.py" with command "build" and parameters ()
running build
running build_py
file visidata.py (for module visidata) not found
creating build
creating build/lib
creating build/lib/visidata
copying visidata/cmdlog.py -> build/lib/visidata
copying visidata/_input.py -> build/lib/visidata
copying visidata/help.py -> build/lib/visidata
copying visidata/customdate.py -> build/lib/visidata
copying visidata/_types.py -> build/lib/visidata
copying visidata/layout.py -> build/lib/visidata
copying visidata/modify.py -> build/lib/visidata
copying visidata/__main__.py -> build/lib/visidata
copying visidata/main.py -> build/lib/visidata
copying visidata/plugins.py -> build/lib/visidata
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copying visidata/colorsheet.py -> build/lib/visidata
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copying visidata/transpose.py -> build/lib/visidata
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copying visidata/freqtbl.py -> build/lib/visidata
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copying visidata/shell.py -> build/lib/visidata
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copying visidata/cliptext.py -> build/lib/visidata
copying visidata/textsheet.py -> build/lib/visidata
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copying visidata/macros.py -> build/lib/visidata
copying visidata/unfurl.py -> build/lib/visidata
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copying visidata/utils.py -> build/lib/visidata
creating build/lib/visidata/loaders
copying visidata/loaders/xlsb.py -> build/lib/visidata/loaders
copying visidata/loaders/sas.py -> build/lib/visidata/loaders
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copying visidata/loaders/markdown.py -> build/lib/visidata/loaders
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copying visidata/loaders/pandas_freqtbl.py -> build/lib/visidata/loaders
copying visidata/loaders/pdf.py -> build/lib/visidata/loaders
copying visidata/loaders/graphviz.py -> build/lib/visidata/loaders
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copying visidata/loaders/xlsx.py -> build/lib/visidata/loaders
copying visidata/loaders/yaml.py -> build/lib/visidata/loaders
copying visidata/loaders/pcap.py -> build/lib/visidata/loaders
copying visidata/loaders/arrow.py -> build/lib/visidata/loaders
copying visidata/loaders/shp.py -> build/lib/visidata/loaders
copying visidata/loaders/frictionless.py -> build/lib/visidata/loaders
copying visidata/loaders/postgres.py -> build/lib/visidata/loaders
copying visidata/loaders/eml.py -> build/lib/visidata/loaders
copying visidata/loaders/lsv.py -> build/lib/visidata/loaders
copying visidata/loaders/rec.py -> build/lib/visidata/loaders
copying visidata/loaders/png.py -> build/lib/visidata/loaders
copying visidata/loaders/mbtiles.py -> build/lib/visidata/loaders
copying visidata/loaders/vcf.py -> build/lib/visidata/loaders
copying visidata/loaders/npy.py -> build/lib/visidata/loaders
copying visidata/loaders/__init__.py -> build/lib/visidata/loaders
copying visidata/loaders/archive.py -> build/lib/visidata/loaders
copying visidata/loaders/html.py -> build/lib/visidata/loaders
copying visidata/loaders/fixed_width.py -> build/lib/visidata/loaders
copying visidata/loaders/imap.py -> build/lib/visidata/loaders
copying visidata/loaders/http.py -> build/lib/visidata/loaders
copying visidata/loaders/texttables.py -> build/lib/visidata/loaders
copying visidata/loaders/unzip_http.py -> build/lib/visidata/loaders
copying visidata/loaders/sqlite.py -> build/lib/visidata/loaders
copying visidata/loaders/usv.py -> build/lib/visidata/loaders
copying visidata/loaders/csv.py -> build/lib/visidata/loaders
copying visidata/loaders/hdf5.py -> build/lib/visidata/loaders
copying visidata/loaders/spss.py -> build/lib/visidata/loaders
package init file 'visidata/vendor/__init__.py' not found (or not a regular file)
creating build/lib/visidata/vendor
copying visidata/vendor/appdirs.py -> build/lib/visidata/vendor
creating build/lib/visidata/tests
copying visidata/tests/conftest.py -> build/lib/visidata/tests
copying visidata/tests/test_commands.py -> build/lib/visidata/tests
copying visidata/tests/test_edittext.py -> build/lib/visidata/tests
copying visidata/tests/test_path.py -> build/lib/visidata/tests
copying visidata/tests/__init__.py -> build/lib/visidata/tests
running egg_info
writing visidata.egg-info/PKG-INFO
writing dependency_links to visidata.egg-info/dependency_links.txt
writing entry points to visidata.egg-info/entry_points.txt
writing requirements to visidata.egg-info/requires.txt
writing top-level names to visidata.egg-info/top_level.txt
file visidata.py (for module visidata) not found
reading manifest file 'visidata.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
adding license file 'LICENSE.gpl3'
writing manifest file 'visidata.egg-info/SOURCES.txt'
creating build/lib/visidata/ddw
copying visidata/ddw/input.ddw -> build/lib/visidata/ddw
creating build/lib/visidata/man
copying visidata/man/vd.1 -> build/lib/visidata/man
copying visidata/man/vd.txt -> build/lib/visidata/man
copying visidata/man/visidata.1 -> build/lib/visidata/man
file visidata.py (for module visidata) not found
warning: build_py: byte-compiling is disabled, skipping.
running build_scripts
creating build/scripts-3.9
copying and adjusting bin/vd -> build/scripts-3.9
changing mode of build/scripts-3.9/vd from 644 to 755
phase `build' succeeded after 0.4 seconds
starting phase `install'
running "python setup.py" with command "install" and parameters ("--prefix=/gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10" "--no-compile" "--single-version-externally-managed" "--root=/")
running install
running build
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running egg_info
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reading manifest template 'MANIFEST.in'
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file visidata.py (for module visidata) not found
warning: build_py: byte-compiling is disabled, skipping.
running build_scripts
running install_lib
creating /gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10
creating /gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10/lib
creating /gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10/lib/python3.9
creating /gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10/lib/python3.9/site-packages
creating /gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10/lib/python3.9/site-packages/visidata
[cut 281 lines of file install messages…]
phase `install' succeeded after 0.4 seconds
starting phase `add-install-to-pythonpath'
phase `add-install-to-pythonpath' succeeded after 0.0 seconds
starting phase `add-install-to-path'
phase `add-install-to-path' succeeded after 0.0 seconds
starting phase `wrap'
find-files: /gnu/store/7rh1wh9kmi6852zs6j1zn4ll2w2y2mny-visidata-2.10/sbin: No such file or directory
phase `wrap' succeeded after 0.0 seconds
starting phase `check'
============================= test session starts ==============================
platform linux -- Python 3.9.9, pytest-6.2.5, py-1.10.0, pluggy-0.13.1
rootdir: /tmp/guix-build-visidata-2.10.drv-0/visidata-2.10
plugins: hypothesis-6.0.2
collected 21 items
visidata/tests/test_commands.py F [ 4%]
visidata/tests/test_edittext.py ................... [ 95%]
visidata/tests/test_path.py . [100%]
=================================== FAILURES ===================================
________________________ TestCommands.test_baseCommands ________________________
self = <visidata.tests.test_commands.TestCommands object at 0x7ffff5aa78b0>
mock_screen = <Mock id='140737315126000'>
def test_baseCommands(self, mock_screen):
'exec each global command at least once'
cmdlist = visidata.vd.commands
vs = visidata.Sheet('test_commands')
vs.reload()
vd = visidata.vd
nerrs = 0
ntotal = 0
for longname in cmdlist.keys():
if not isTestableCommand(longname, cmdlist):
continue
ntotal += 1
print(longname)
> self.runOneTest(mock_screen, longname)
visidata/tests/test_commands.py:93:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
visidata/tests/test_commands.py:119: in runOneTest
vs.reload.__wrapped__(vs)
visidata/sheets.py:944: in reload
self.setCols(list(self.optlines(itsource, 'header')))
visidata/sheets.py:930: in optlines
yield next(it)
visidata/loaders/tsv.py:45: in iterload
with self.source.open_text(encoding=self.options.encoding) as fp:
visidata/path.py:222: in open_text
return self.open(mode=mode, encoding=encoding or vd.options.encoding, errors=vd.options.encoding_errors, newline=newline)
visidata/path.py:262: in open
return FileProgress(path, fp=self._path.open(*args, **kwargs), **kwargs)
/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/lib/python3.9/pathlib.py:1252: in open
return io.open(self, mode, buffering, encoding, errors, newline,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = PosixPath('/tmp/guix-build-visidata-2.10.drv-0/visidata-2.10/visidata/../sample_data/sample.tsv')
name = '/tmp/guix-build-visidata-2.10.drv-0/visidata-2.10/visidata/../sample_data/sample.tsv'
flags = 524288, mode = 438
def _opener(self, name, flags, mode=0o666):
# A stub for the opener argument to built-in open()
> return self._accessor.open(self, flags, mode)
E FileNotFoundError: [Errno 2] No such file or directory: '/tmp/guix-build-visidata-2.10.drv-0/visidata-2.10/visidata/../sample_data/sample.tsv'
/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/lib/python3.9/pathlib.py:1120: FileNotFoundError
----------------------------- Captured stdout call -----------------------------
open-config
=============================== warnings summary ===============================
visidata/tests/test_commands.py::TestCommands::test_baseCommands
/gnu/store/65i3nhcwmz0p8rqbg48gaavyky4g4hwk-python-3.9.9/lib/python3.9/site-packages/pkg_resources/__init__.py:1130: DeprecationWarning: Use of .. or absolute path in a resource path is not allowed and will raise exceptions in a future release.
return get_provider(package_or_requirement).get_resource_filename(
-- Docs: https://docs.pytest.org/en/stable/warnings.html
=========================== short test summary info ============================
FAILED visidata/tests/test_commands.py::TestCommands::test_baseCommands - Fil...
=================== 1 failed, 20 passed, 1 warning in 0.52s ====================
error: in phase 'check': uncaught exception:
%exception #<&invoke-error program: "pytest" arguments: () exit-status: 1 term-signal: #f stop-signal: #f>
phase `check' failed after 1.2 seconds
command "pytest" failed with status 1
</pre>
</details>
**Additional context**
The tests seem to be looking for a file called `sample.tsv`. I checked the distribution tarball on PyPI and it doesn't seem to have that file in it. Maybe adding the file would fix the issue? | closed | 2022-08-31T01:38:46Z | 2022-09-12T00:55:11Z | https://github.com/saulpw/visidata/issues/1499 | [
"bug",
"fixed"
] | ryanprior | 3 |
autogluon/autogluon | scikit-learn | 3,892 | Hyperparemeter Optimization for Time Series stoped working | **Describe the bug**
I carefully installed autogluon for Windows-Pip-GPU (https://auto.gluon.ai/stable/install.html). autogluon works perfecty withouth HPO (fast training to best results). When I try to HPO `PatchTST` I got this error `prediction-net-state.pt`. Which does not happen when I HPO on autogluon for CPU. I am on a Windows 11 laptop with all the last updates. Bellow I attach all the relevant information for you to check the error.
```Bash
Beginning AutoGluon training... Time limit = 7200s
AutoGluon will save models to 'autogluon-m4-hourly'
=================== System Info ===================
AutoGluon Version: 1.0.0
Python Version: 3.9.18
Operating System: Windows
Platform Machine: AMD64
Platform Version: 10.0.22631
CPU Count: 32
GPU Count: 1
Memory Avail: 15.75 GB / 31.69 GB (49.7%)
Disk Space Avail: 769.92 GB / 952.89 GB (80.8%)
===================================================
Fitting with arguments:
{'enable_ensemble': False,
'eval_metric': MASE,
'hyperparameter_tune_kwargs': {'num_trials': 2,
'scheduler': 'local',
'searcher': 'random'},
'hyperparameters': {'PatchTST': {'nhead': Categorical[2, 4]}},
'known_covariates_names': [],
'num_val_windows': 2,
'prediction_length': 48,
'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
'random_seed': 123,
'refit_every_n_windows': 1,
'refit_full': False,
'target': 'target',
'time_limit': 7200,
'verbosity': 2}
Inferred time series frequency: 'H'
Provided train_data has 148060 rows, 200 time series. Median time series length is 700 (min=700, max=960).
Provided dataset contains following columns:
target: 'target'
AutoGluon will gauge predictive performance using evaluation metric: 'MASE'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
===================================================
Starting training. Start time is 2024-01-29 17:14:49
Models that will be trained: ['PatchTST']
Hyperparameter tuning model PatchTST. Tuning model for up to 7198.9s of the 7198.9s remaining.
Traceback (most recent call last):
File "c:\Users\diego.villacreses\OneDrive - Universidad de Las Américas\Desktop\MT\forecast_ventas\notebooks\tmp2.py", line 19, in <module>
predictor.fit(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\core\utils\decorators.py", line 31, in _call
return f(*gargs, **gkwargs)
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\predictor.py", line 681, in fit
self._learner.fit(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\learner.py", line 63, in fit
return self._fit(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\learner.py", line 118, in _fit
self.trainer.fit(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\trainer\auto_trainer.py", line 63, in fit
self._train_multi(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\trainer\abstract_trainer.py", line 587, in _train_multi
model_names_trained += self.tune_model_hyperparameters(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\trainer\abstract_trainer.py", line 448, in tune_model_hyperparameters
model_hpo = self.load_model(model_hpo_name, path=model_path, model_type=type(model))
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\trainer\abstract_trainer.py", line 170, in load_model
return model_type.load(path=os.path.join(self.path, path), reset_paths=self.reset_paths)
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\models\multi_window\multi_window_model.py", line 225, in load
model.most_recent_model = model.model_base_type.load(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\autogluon\timeseries\models\gluonts\abstract_gluonts.py", line 208, in load
model.gts_predictor = PyTorchPredictor.deserialize(Path(path) / cls.gluonts_model_path)
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\gluonts\torch\model\predictor.py", line 117, in deserialize
state_dict = torch.load(
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\torch\serialization.py", line 791, in load
with _open_file_like(f, 'rb') as opened_file:
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\torch\serialization.py", line 271, in _open_file_like
return _open_file(name_or_buffer, mode)
File "C:\Users\diego.villacreses\AppData\Local\miniconda3\envs\ag\lib\site-packages\torch\serialization.py", line 252, in __init__
super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\diego.villacreses\\notebooks\\autogluon-m4-hourly\\models\\PatchTST\\d196b_00000\\W1\\gluon_ts\\prediction-net-state.pt'
```
**To Reproduce**
```Python
import pandas as pd
from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor
from autogluon.common import space
df = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly_subset/train.csv")
train_data = TimeSeriesDataFrame.from_data_frame(
df,
id_column="item_id",
timestamp_column="timestamp"
)
predictor = TimeSeriesPredictor(
prediction_length=48,
path="autogluon-m4-hourly",
target="target",
eval_metric="MASE",
)
predictor.fit(
train_data,
hyperparameters={
"PatchTST": {"nhead": space.Categorical(2, 4)},
},
hyperparameter_tune_kwargs={
"num_trials": 2,
"scheduler": "local", # local, FIFO ASHA
"searcher": "random", # auto, bayes, random
},
enable_ensemble=False,
time_limit=60*60*2, # total training time in seconds
num_val_windows=2,
)
print(predictor.leaderboard())
```
**Screenshots / Logs**
<!-- If applicable, add screenshots or logs to help explain your problem. -->
**Installed Versions**
```Bash
absl-py 2.1.0
accelerate 0.21.0
aiohttp 3.9.3
aiohttp-cors 0.7.0
aiosignal 1.3.1
aliyun-python-sdk-core 2.14.0
aliyun-python-sdk-kms 2.16.2
ansicon 1.89.0
antlr4-python3-runtime 4.9.3
anyio 4.2.0
arrow 1.3.0
async-timeout 4.0.3
attrs 23.2.0
autogluon 1.0.0
autogluon.common 1.0.0
autogluon.core 1.0.0
autogluon.features 1.0.0
autogluon.multimodal 1.0.0
autogluon.tabular 1.0.0
autogluon.timeseries 1.0.0
backoff 2.2.1
beautifulsoup4 4.12.3
blessed 1.20.0
blis 0.7.11
boto3 1.34.30
botocore 1.34.30
cachetools 5.3.2
catalogue 2.0.10
catboost 1.2.2
certifi 2023.11.17
cffi 1.16.0
charset-normalizer 3.3.2
click 8.1.7
cloudpathlib 0.16.0
cloudpickle 3.0.0
colorama 0.4.6
colorful 0.5.6
confection 0.1.4
contourpy 1.2.0
crcmod 1.7
croniter 1.4.1
cryptography 42.0.1
cycler 0.12.1
cymem 2.0.8
datasets 2.10.1
dateutils 0.6.12
deepdiff 6.7.1
defusedxml 0.7.1
dill 0.3.6
distlib 0.3.8
editor 1.6.6
et-xmlfile 1.1.0
evaluate 0.4.1
exceptiongroup 1.2.0
fastai 2.7.13
fastapi 0.109.0
fastcore 1.5.29
fastdownload 0.0.7
fastprogress 1.0.3
filelock 3.13.1
fonttools 4.47.2
frozenlist 1.4.1
fsspec 2023.12.2
future 0.18.3
gdown 5.0.1
gluonts 0.14.3
google-api-core 2.15.0
google-auth 2.27.0
google-auth-oauthlib 1.2.0
googleapis-common-protos 1.62.0
gpustat 1.1.1
graphviz 0.20.1
grpcio 1.60.0
h11 0.14.0
huggingface-hub 0.20.3
hyperopt 0.2.7
idna 3.6
imageio 2.33.1
importlib-metadata 7.0.1
importlib-resources 6.1.1
inquirer 3.2.3
itsdangerous 2.1.2
Jinja2 3.1.3
jinxed 1.2.1
jmespath 0.10.0
joblib 1.3.2
jsonschema 4.17.3
kiwisolver 1.4.5
langcodes 3.3.0
lazy_loader 0.3
lightgbm 4.1.0
lightning 2.0.9.post0
lightning-cloud 0.5.62
lightning-utilities 0.10.1
llvmlite 0.41.1
Markdown 3.5.2
markdown-it-py 3.0.0
MarkupSafe 2.1.4
matplotlib 3.8.2
mdurl 0.1.2
mlforecast 0.10.0
model-index 0.1.11
mpmath 1.3.0
msgpack 1.0.7
multidict 6.0.4
multiprocess 0.70.14
murmurhash 1.0.10
networkx 3.2.1
nlpaug 1.1.11
nltk 3.8.1
nptyping 2.4.1
numba 0.58.1
numpy 1.24.4
nvidia-ml-py 12.535.133
nvidia-ml-py3 7.352.0
oauthlib 3.2.2
omegaconf 2.2.3
opencensus 0.11.4
opencensus-context 0.1.3
opendatalab 0.0.10
openmim 0.3.9
openpyxl 3.1.2
openxlab 0.0.34
ordered-set 4.1.0
orjson 3.9.12
oss2 2.17.0
packaging 23.2
pandas 2.1.4
patsy 0.5.6
pillow 10.2.0
pip 23.3.2
platformdirs 3.11.0
plotly 5.18.0
preshed 3.0.9
prometheus-client 0.19.0
protobuf 4.23.4
psutil 5.9.8
py-spy 0.3.14
py4j 0.10.9.7
pyarrow 6.0.1
pyasn1 0.5.1
pyasn1-modules 0.3.0
pycparser 2.21
pycryptodome 3.20.0
pydantic 1.10.14
Pygments 2.17.2
PyJWT 2.8.0
pyparsing 3.1.1
pyrsistent 0.20.0
PySocks 1.7.1
pytesseract 0.3.10
python-dateutil 2.8.2
python-multipart 0.0.6
pytorch-lightning 2.0.9.post0
pytorch-metric-learning 1.7.3
pytz 2023.4
PyWavelets 1.5.0
pywin32 306
PyYAML 6.0.1
ray 2.6.3
readchar 4.0.5
regex 2023.12.25
requests 2.28.2
requests-oauthlib 1.3.1
responses 0.18.0
rich 13.4.2
rsa 4.9
runs 1.2.2
s3transfer 0.10.0
safetensors 0.4.2
scikit-image 0.20.0
scikit-learn 1.4.0
scipy 1.9.1
sentencepiece 0.1.99
seqeval 1.2.2
setuptools 60.2.0
six 1.16.0
smart-open 6.4.0
sniffio 1.3.0
soupsieve 2.5
spacy 3.7.2
spacy-legacy 3.0.12
spacy-loggers 1.0.5
srsly 2.4.8
starlette 0.35.1
starsessions 1.3.0
statsforecast 1.4.0
statsmodels 0.14.1
sympy 1.12
tabulate 0.9.0
tenacity 8.2.3
tensorboard 2.15.1
tensorboard-data-server 0.7.2
tensorboardX 2.6.2.2
text-unidecode 1.3
thinc 8.2.2
threadpoolctl 3.2.0
tifffile 2024.1.30
timm 0.9.12
tokenizers 0.13.3
toolz 0.12.1
torch 2.0.1+cu118
torchmetrics 1.1.2
torchvision 0.15.2+cu118
tqdm 4.65.2
traitlets 5.14.1
transformers 4.31.0
typer 0.9.0
types-python-dateutil 2.8.19.20240106
typing_extensions 4.9.0
tzdata 2023.4
urllib3 1.26.18
utilsforecast 0.0.10
uvicorn 0.27.0.post1
virtualenv 20.21.0
wasabi 1.1.2
wcwidth 0.2.13
weasel 0.3.4
websocket-client 1.7.0
websockets 12.0
Werkzeug 3.0.1
wheel 0.42.0
window-ops 0.0.14
xgboost 2.0.3
xmod 1.8.1
xxhash 3.4.1
yarl 1.9.4
zipp 3.17.0
```
```python
from autogluon.core.utils import show_versions
show_versions()
```
```Bash
date : 2024-01-29
time : 17:22:48.832973
python : 3.9.18.final.0
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
num_cores : 32
cpu_ram_mb : 32452.703125
cuda version : None
num_gpus : 1
gpu_ram_mb : [7963]
avail_disk_size_mb : None
accelerate : 0.21.0
async-timeout : 4.0.3
autogluon : 1.0.0
autogluon.common : 1.0.0
autogluon.core : 1.0.0
autogluon.features : 1.0.0
autogluon.multimodal : 1.0.0
autogluon.tabular : 1.0.0
autogluon.timeseries : 1.0.0
boto3 : 1.34.30
catboost : 1.2.2
defusedxml : 0.7.1
evaluate : 0.4.1
fastai : 2.7.13
gluonts : 0.14.3
hyperopt : 0.2.7
imodels : None
jinja2 : 3.1.3
joblib : 1.3.2
jsonschema : 4.17.3
lightgbm : 4.1.0
lightning : 2.0.9.post0
matplotlib : 3.8.2
mlforecast : 0.10.0
networkx : 3.2.1
nlpaug : 1.1.11
nltk : 3.8.1
nptyping : 2.4.1
numpy : 1.24.4
nvidia-ml-py3 : 7.352.0
omegaconf : 2.2.3
onnxruntime-gpu : None
openmim : 0.3.9
orjson : 3.9.12
pandas : 2.1.4
Pillow : 10.2.0
psutil : 5.9.8
PyMuPDF : None
pytesseract : 0.3.10
pytorch-lightning : 2.0.9.post0
pytorch-metric-learning: 1.7.3
ray : 2.6.3
requests : 2.28.2
scikit-image : 0.20.0
scikit-learn : 1.4.0
scikit-learn-intelex : None
scipy : 1.9.1
seqeval : 1.2.2
setuptools : 60.2.0
skl2onnx : None
statsforecast : 1.4.0
statsmodels : 0.14.1
tabpfn : None
tensorboard : 2.15.1
text-unidecode : 1.3
timm : 0.9.12
torch : 2.0.1+cu118
torchmetrics : 1.1.2
torchvision : 0.15.2+cu118
tqdm : 4.65.2
transformers : 4.31.0
utilsforecast : 0.0.10
vowpalwabbit : None
xgboost : 2.0.3
```
| closed | 2024-01-29T22:24:03Z | 2024-03-01T14:49:15Z | https://github.com/autogluon/autogluon/issues/3892 | [
"bug: unconfirmed",
"module: timeseries"
] | DiegoDVillacreses | 3 |
plotly/dash | plotly | 3,074 | Memory Leakage When Navigating Away from Home Page with dash.register_page() | **Describe your context**
There are issues with memory leakage in our Dash application. Specifically, each time users navigate away from the home page, we observe a memory leak of about 15MB. Objects are getting detached but not fully removed, and each time the user changes tabs, new objects are created while the old ones are not removed.
- replace the result of `pip list | grep dash` below
```
dash==2.17.0
gunicorn==20.0.4
pandas>=1.1.5
```
- if frontend related, tell us your Browser, Version and OS
- Browser chrome
**Describe the bug**
- Navigation: We use dash.register_page() to add a new page to our application. Users navigate away from the home page using dcc.Location and dcc.Link
- Component Setup: The home page layout includes an ag-Grid component within a dcc.Tabs component.
- Callbacks: A callback renders content dynamically based on the selected tab within dcc.Tabs.
**Expected behavior**
Remove detached objects
**Screenshots**
| open | 2024-11-13T15:38:29Z | 2024-11-15T15:09:23Z | https://github.com/plotly/dash/issues/3074 | [
"bug",
"P2"
] | andre996 | 0 |
MaartenGr/BERTopic | nlp | 1,890 | Tracking the source index of new topics when merging models | Hi Maarten,
I'm playing with the merge_models feature, which is very useful, but I'm wondering if there is a way for a merged model to keep track the index of new topics added to it from their original models.
One use case of this is if I have some other metadata relating to my topics before the merge, and I want to link that metadata to the topics after the merge.
At the moment I'm doing
```python
from umap import UMAP
from bertopic import BERTopic
from datasets import load_dataset
import numpy as np
import pandas as pd
dataset = load_dataset("CShorten/ML-ArXiv-Papers")["train"]
abstracts_1 = dataset["abstract"][:5_000]
abstracts_2 = dataset["abstract"][5_000:10_000]
# Create topic models
umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', random_state=42)
topic_model_1 = BERTopic(umap_model=umap_model, min_topic_size=20).fit(abstracts_1)
topic_model_2 = BERTopic(umap_model=umap_model, min_topic_size=20).fit(abstracts_2)
# calculate topic stat
topic_info_1 = topic_model_1.get_topic_info()
topic_info_1['Topic stat'] = np.random.randint(1, 10, topic_info_1.shape[0])
topic_info_2 = topic_model_2.get_topic_info()
topic_info_2['Topic stat'] = np.random.randint(1, 10, topic_info_2.shape[0])
merged_model = BERTopic.merge_models([topic_model_1, topic_model_2], min_similarity=0.9)
merged_info = merged_model.get_topic_info()
# map new merged topics to original model 2
new_topic_nums = merged_info['Name'][len(topic_info_1):]
new_topic_nums = new_topic_nums.str.split("_", n=1).str[0].astype('int')
all_old_stats = topic_info_1['Topic stat']
selected_new_stats = topic_info_2['Topic stat'].loc[topic_info_2['Topic'].isin(new_topic_nums)]
merged_stats = pd.concat([all_old_stats, selected_new_stats]).tolist()
merged_info['Topic stat'] = merged_stats
```
But this seems really hacky and gets really complicated when merging more than 2 models.
It would be great to have a dictionary or something that mapped each sequential merge e.g.
```
{
"1": { # merge 1
"5": 53, # topic num in original model: topic num in merged model
"19: 54,
... },
"2": { # merge 2
"7": 61,
12: 62,
... },
...
}
````
or something like that. I know there's already a topic mapper used for other purposes. Not sure if that could be utilised?
| open | 2024-03-27T04:47:30Z | 2024-04-01T23:28:46Z | https://github.com/MaartenGr/BERTopic/issues/1890 | [] | zilch42 | 2 |
statsmodels/statsmodels | data-science | 8,929 | ENH: GAM combining different penalizations, splines | We don't have an option to combine two different spline types in GAM
https://stackoverflow.com/questions/76541891/use-both-cyclic-and-b-spline-smoothers-in-statsmodels-gam
We had this question once before, soon after I had merged GAM.
Related we don't have a simple way to add different penalties, Penalty classes, either for overlapping or non-overlapping params. | open | 2023-06-24T13:13:50Z | 2024-07-23T12:54:44Z | https://github.com/statsmodels/statsmodels/issues/8929 | [
"type-enh",
"topic-penalization",
"prio-elev",
"comp-gam"
] | josef-pkt | 0 |
lepture/authlib | flask | 379 | Auth required after fetch_token with AsyncOAuth2Client | **Describe the bug**
Documentation shows client.get, .post, etc being used immediately after client.fetch_token() but this does not seem to work.
**To Reproduce**
```
from authlib.integrations.httpx_client.oauth2_client import OAuth2Client, OAuth2Auth
oauth_client = AsyncOAuth2Client(...)
token = await oauth_client.fetch_token(
API_TOKEN_URL,
authorization_response=str(request.url),
grant_type='authorization_code',
)
user_request = await oauth_client.get(API_URL + "/users")
```
_**401 here**_
However, if you use:
```
user_request = await oauth_client.get(API_URL + "/users", auth=OAuth2Auth(client.token))
... or ...
client.auth = OAuth2Auth(client.token)
user_request = await oauth_client.get(API_URL + "/users")
```
_**200 here**_
**Expected behavior**
As per the docs suggest, I should be able to use .get, .post, etc immediately after using fetch_token() or maybe the docs should be updated for the methods above? I don't know how it's supposed to work. The docs make it seem like I should be able to use .get immediately against an endpoint requiring authentication.
**Environment:**
- OS: Linux
- Python Version: 3.9.6
- Authlib Version: 0.15.4
| closed | 2021-08-27T04:34:59Z | 2021-10-18T12:16:57Z | https://github.com/lepture/authlib/issues/379 | [
"bug"
] | elijahsgh | 4 |
torchbox/wagtail-grapple | graphql | 39 | example app docker build fails | 
| closed | 2019-12-04T19:14:59Z | 2022-02-25T16:58:11Z | https://github.com/torchbox/wagtail-grapple/issues/39 | [
"status: Needs info"
] | easherma | 4 |
clovaai/donut | computer-vision | 38 | Need code for SROIE custom dataset regarding | Hi Neha,
Kindly send me a code for DONUT using a custom dataset. | open | 2022-08-29T08:10:05Z | 2022-08-29T08:10:05Z | https://github.com/clovaai/donut/issues/38 | [] | SankarSennan | 0 |
TencentARC/GFPGAN | deep-learning | 83 | Better hair enhancement | Is it possible for this tool to enhance all of subject's hair and not only the hair that's around their face? | open | 2021-10-19T15:25:11Z | 2021-10-19T15:25:11Z | https://github.com/TencentARC/GFPGAN/issues/83 | [] | ivellios1988 | 0 |
vllm-project/vllm | pytorch | 14,747 | [Installation]: Cannot compile vLLM from source on XPU | ### Your current environment
<details>
<summary>The output of `python collect_env.py`</summary>
```text
Your output of `python collect_env.py` here
```
</details>
### 🐛 Describe the bug
Compiling vLLM from source for XPU or building the dockerfile results in failure each time you attempt to run vLLM.
```
python3 -m vllm.entrypoints.openai.api_server --model /llm/models/qwen2.5-1.5b-instruct --device xpu --max_model_len 1024
```
```
[W313 09:42:19.085066709 OperatorEntry.cpp:154] Warning: Warning only once for all operators, other operators may also be overridden.
Overriding a previously registered kernel for the same operator and the same dispatch key
operator: aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()
registered at /pytorch/build/aten/src/ATen/RegisterSchema.cpp:6
dispatch key: XPU
previous kernel: registered at /pytorch/build/aten/src/ATen/RegisterCPU.cpp:30477
new kernel: registered at /build/intel-pytorch-extension/build/Release/csrc/gpu/csrc/aten/generated/ATen/RegisterXPU.cpp:468 (function operator())
INFO 03-13 09:42:21 [__init__.py:256] Automatically detected platform xpu.
[W313 09:42:22.469965695 OperatorEntry.cpp:154] Warning: Warning only once for all operators, other operators may also be overridden.
Overriding a previously registered kernel for the same operator and the same dispatch key
operator: aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()
registered at /pytorch/build/aten/src/ATen/RegisterSchema.cpp:6
dispatch key: XPU
previous kernel: registered at /pytorch/build/aten/src/ATen/RegisterCPU.cpp:30477
new kernel: registered at /build/intel-pytorch-extension/build/Release/csrc/gpu/csrc/aten/generated/ATen/RegisterXPU.cpp:468 (function operator())
INFO 03-13 09:42:22 [api_server.py:912] vLLM API server version 0.7.4.dev389+g72e4bd5e
INFO 03-13 09:42:22 [api_server.py:913] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='/llm/models/qwen2.5-1.5b-instruct', task='auto', tokenizer=None, hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, download_dir=None, load_format='auto', config_format=<ConfigFormat.AUTO: 'auto'>, dtype='auto', kv_cache_dtype='auto', max_model_len=1024, guided_decoding_backend='xgrammar', logits_processor_pattern=None, model_impl='auto', distributed_executor_backend=None, pipeline_parallel_size=1, tensor_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=None, enable_prefix_caching=None, disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=None, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, max_num_seqs=None, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='xpu', num_scheduler_steps=1, use_tqdm_on_load=True, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', worker_extension_cls='', generation_config='auto', override_generation_config=None, enable_sleep_mode=False, calculate_kv_scales=False, additional_config=None, enable_reasoning=False, reasoning_parser=None, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False)
INFO 03-13 09:42:22 [api_server.py:209] Started engine process with PID 301
[W313 09:42:24.376887599 OperatorEntry.cpp:154] Warning: Warning only once for all operators, other operators may also be overridden.
Overriding a previously registered kernel for the same operator and the same dispatch key
operator: aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()
registered at /pytorch/build/aten/src/ATen/RegisterSchema.cpp:6
dispatch key: XPU
previous kernel: registered at /pytorch/build/aten/src/ATen/RegisterCPU.cpp:30477
new kernel: registered at /build/intel-pytorch-extension/build/Release/csrc/gpu/csrc/aten/generated/ATen/RegisterXPU.cpp:468 (function operator())
INFO 03-13 09:42:26 [__init__.py:256] Automatically detected platform xpu.
INFO 03-13 09:42:27 [config.py:576] This model supports multiple tasks: {'classify', 'generate', 'reward', 'embed', 'score'}. Defaulting to 'generate'.
WARNING 03-13 09:42:27 [_logger.py:68] bfloat16 is only supported on Intel Data Center GPU, Intel Arc GPU is not supported yet. Your device is Intel(R) Arc(TM) A770 Graphics, which is not supported. will fallback to float16
WARNING 03-13 09:42:27 [_logger.py:68] CUDA graph is not supported on XPU, fallback to the eager mode.
INFO 03-13 09:42:31 [config.py:576] This model supports multiple tasks: {'reward', 'generate', 'embed', 'classify', 'score'}. Defaulting to 'generate'.
WARNING 03-13 09:42:31 [_logger.py:68] bfloat16 is only supported on Intel Data Center GPU, Intel Arc GPU is not supported yet. Your device is Intel(R) Arc(TM) A770 Graphics, which is not supported. will fallback to float16
WARNING 03-13 09:42:31 [_logger.py:68] CUDA graph is not supported on XPU, fallback to the eager mode.
INFO 03-13 09:42:31 [llm_engine.py:235] Initializing a V0 LLM engine (v0.7.4.dev389+g72e4bd5e) with config: model='/llm/models/qwen2.5-1.5b-instruct', speculative_config=None, tokenizer='/llm/models/qwen2.5-1.5b-instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=1024, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, device_config=xpu, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=/llm/models/qwen2.5-1.5b-instruct, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=False, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=True,
INFO 03-13 09:42:31 [xpu.py:36] Cannot use None backend on XPU.
INFO 03-13 09:42:31 [xpu.py:42] Using IPEX attention backend.
WARNING 03-13 09:42:31 [_logger.py:68] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'")
INFO 03-13 09:42:31 [importing.py:16] Triton not installed or not compatible; certain GPU-related functions will not be available.
INFO 03-13 09:42:31 [parallel_state.py:948] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0
2025:03:13-09:42:31:( 301) |CCL_WARN| value of CCL_ATL_TRANSPORT changed to be ofi (default:mpi)
2025:03:13-09:42:31:( 301) |CCL_WARN| value of CCL_LOCAL_RANK changed to be 0 (default:-1)
2025:03:13-09:42:31:( 301) |CCL_WARN| value of CCL_LOCAL_SIZE changed to be 1 (default:-1)
2025:03:13-09:42:31:( 301) |CCL_WARN| value of CCL_PROCESS_LAUNCHER changed to be none (default:hydra)
2025:03:13-09:42:32:( 439) |CCL_WARN| no membind support for NUMA node 0, skip thread membind
2025:03:13-09:42:32:( 301) |CCL_WARN| device_family is unknown, topology discovery could be incorrect, it might result in suboptimal performance
2025:03:13-09:42:32:( 301) |CCL_WARN| pidfd is not supported, fallbacks to drmfd exchange mode
2025:03:13-09:42:32:( 301) |CCL_ERROR| ze_fd_manager.cpp:214 fill_device_fds: condition fds[dev_idx] > 0 failed
open failed: fd: -1, errno: No such file or directory
ERROR 03-13 09:42:32 [engine.py:411] oneCCL: ze_fd_manager.cpp:214 fill_device_fds: EXCEPTION: open failed: fd: -1, errno: No such file or directory
ERROR 03-13 09:42:32 [engine.py:411] Traceback (most recent call last):
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 402, in run_mp_engine
ERROR 03-13 09:42:32 [engine.py:411] engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 125, in from_engine_args
ERROR 03-13 09:42:32 [engine.py:411] return cls(ipc_path=ipc_path,
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 77, in __init__
ERROR 03-13 09:42:32 [engine.py:411] self.engine = LLMEngine(*args, **kwargs)
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/llm_engine.py", line 274, in __init__
ERROR 03-13 09:42:32 [engine.py:411] self.model_executor = executor_class(vllm_config=vllm_config, )
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/executor/executor_base.py", line 52, in __init__
ERROR 03-13 09:42:32 [engine.py:411] self._init_executor()
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/executor/uniproc_executor.py", line 46, in _init_executor
ERROR 03-13 09:42:32 [engine.py:411] self.collective_rpc("init_device")
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/executor/uniproc_executor.py", line 56, in collective_rpc
ERROR 03-13 09:42:32 [engine.py:411] answer = run_method(self.driver_worker, method, args, kwargs)
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/utils.py", line 2238, in run_method
ERROR 03-13 09:42:32 [engine.py:411] return func(*args, **kwargs)
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/worker/worker_base.py", line 604, in init_device
ERROR 03-13 09:42:32 [engine.py:411] self.worker.init_device() # type: ignore
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/worker/xpu_worker.py", line 82, in init_device
ERROR 03-13 09:42:32 [engine.py:411] self.init_worker_distributed_environment()
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/worker/xpu_worker.py", line 180, in init_worker_distributed_environment
ERROR 03-13 09:42:32 [engine.py:411] torch.distributed.all_reduce(torch.zeros(1).xpu())
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper
ERROR 03-13 09:42:32 [engine.py:411] return func(*args, **kwargs)
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] File "/vllm/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py", line 2806, in all_reduce
ERROR 03-13 09:42:32 [engine.py:411] work = group.allreduce([tensor], opts)
ERROR 03-13 09:42:32 [engine.py:411] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-13 09:42:32 [engine.py:411] RuntimeError: oneCCL: ze_fd_manager.cpp:214 fill_device_fds: EXCEPTION: open failed: fd: -1, errno: No such file or directory
Process SpawnProcess-1:
Traceback (most recent call last):
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 413, in run_mp_engine
raise e
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 402, in run_mp_engine
engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 125, in from_engine_args
return cls(ipc_path=ipc_path,
^^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/multiprocessing/engine.py", line 77, in __init__
self.engine = LLMEngine(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/engine/llm_engine.py", line 274, in __init__
self.model_executor = executor_class(vllm_config=vllm_config, )
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/executor/executor_base.py", line 52, in __init__
self._init_executor()
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/executor/uniproc_executor.py", line 46, in _init_executor
self.collective_rpc("init_device")
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/executor/uniproc_executor.py", line 56, in collective_rpc
answer = run_method(self.driver_worker, method, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/utils.py", line 2238, in run_method
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/worker/worker_base.py", line 604, in init_device
self.worker.init_device() # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/worker/xpu_worker.py", line 82, in init_device
self.init_worker_distributed_environment()
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/worker/xpu_worker.py", line 180, in init_worker_distributed_environment
torch.distributed.all_reduce(torch.zeros(1).xpu())
File "/vllm/lib/python3.12/site-packages/torch/distributed/c10d_logger.py", line 81, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py", line 2806, in all_reduce
work = group.allreduce([tensor], opts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: oneCCL: ze_fd_manager.cpp:214 fill_device_fds: EXCEPTION: open failed: fd: -1, errno: No such file or directory
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/entrypoints/openai/api_server.py", line 992, in <module>
uvloop.run(run_server(args))
File "/vllm/lib/python3.12/site-packages/uvloop/__init__.py", line 109, in run
return __asyncio.run(
^^^^^^^^^^^^^^
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/asyncio/runners.py", line 195, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
File "/vllm/lib/python3.12/site-packages/uvloop/__init__.py", line 61, in wrapper
return await main
^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/entrypoints/openai/api_server.py", line 947, in run_server
async with build_async_engine_client(args) as engine_client:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/contextlib.py", line 210, in __aenter__
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/entrypoints/openai/api_server.py", line 139, in build_async_engine_client
async with build_async_engine_client_from_engine_args(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/.local/share/uv/python/cpython-3.12.9-linux-x86_64-gnu/lib/python3.12/contextlib.py", line 210, in __aenter__
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/vllm/lib/python3.12/site-packages/vllm-0.7.4.dev389+g72e4bd5e.xpu-py3.12.egg/vllm/entrypoints/openai/api_server.py", line 233, in build_async_engine_client_from_engine_args
raise RuntimeError(
RuntimeError: Engine process failed to start. See stack trace for the root cause.
```
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions. | open | 2025-03-13T09:47:58Z | 2025-03-24T15:55:18Z | https://github.com/vllm-project/vllm/issues/14747 | [
"installation"
] | HumerousGorgon | 10 |
jwkvam/bowtie | plotly | 207 | query params | Use Case:
1. Allow deeplinking and sharing links.
- [ ] for appropriate controllers, option to use query param
- [ ] when loading page, if there are query params load them
- [ ] query params should take precedence over stored state | open | 2018-02-20T21:27:48Z | 2018-09-23T04:27:25Z | https://github.com/jwkvam/bowtie/issues/207 | [
"enhancement"
] | jwkvam | 0 |
Gozargah/Marzban | api | 1,600 | Update notification and one-click update via web interface. | I would like to be notified of a new version release directly in the web interface and the ability to update at the click of a button from the same.
For example, add an icon here to symbolize an available update and when you click on it you will see the new available version, a link to the changes and a button to update.
| closed | 2025-01-12T07:32:20Z | 2025-01-12T11:29:16Z | https://github.com/Gozargah/Marzban/issues/1600 | [
"Duplicate"
] | OwnerLink | 2 |
pytorch/pytorch | numpy | 149,194 | LSTM slow on PackedSequence | ### 🐛 Describe the bug
Using LSTM with `PackedSequence` input is very slow. This effect is extreme at high sequence lengths, see tables below. Given that PackedSequence is the only way to get correct output and state for a sequence with non-homogenious length I think this is a big challenge in usability of RNNs. From less detailed experiments, similar slowdown occured for GRU.
Below is a script that reproduces it, both on GPU and CPU. It has commented sections for plotting and profiling. Here is how much slower using PackedSequence is:
| L | Packed / LSTM Forward (%) | Packed / LSTM Backward (%) |
|------|---------------------------|----------------------------|
| 10 | 526.90 | 277.19 |
| 20 | 739.90 | 460.88 |
| 50 | 1162.63 | 381.99 |
| 100 | 1506.77 | 395.81 |
| 200 | 2300.32 | 590.51 |
| 500 | 5967.95 | 1715.68 |
| 1000 | 9583.25 | 2793.80 |
| 2000 | 10983.58 | 5242.34 |
| 4000 | 11384.78 | 8090.40 |
```python
import time
import torch
import torch.nn as nn
import numpy as np
from functools import lru_cache
# Define the LSTM model
class SimpleLSTM(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2)
def forward(self, x):
# x shape: (sequence_length, batch_size, input_size)
out, _ = self.lstm(x)
return out
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
@lru_cache
def get_triangular_lengths(B: int, T: int) -> torch.Tensor:
return torch.from_numpy(np.linspace(1, T, num=B).round().astype(np.int64))
class PackedLSTM(nn.Module):
def __init__(self, input_size: int, hidden_size: int):
super().__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2)
def forward(self, x: torch.Tensor):
# x shape: (sequence_length, batch_size, input_size)
T, B, D = x.shape
# lengths shape: (batch_size,)
lengths = get_triangular_lengths(B, T)
packed_x = pack_padded_sequence(x, lengths.cpu(), enforce_sorted=False)
packed_out, _ = self.lstm(packed_x)
out, _ = pad_packed_sequence(packed_out)
return out
def benchmark(lstm_cls, input_size, hidden_size, batch_size, seq_len, quiet:bool =False):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = lstm_cls(input_size, hidden_size).to(device)
model.train() # Set the model to training mode
loss_fn = nn.MSELoss()
# Generate random input data and target
n_repeats = round( (100_000/seq_len)**(3/4) ) + 1
fwd = []
bckwd = []
for i in range(n_repeats):
input_data = torch.randn(seq_len, batch_size, input_size, device=device)
target = torch.randn(
seq_len, batch_size, hidden_size, device=device
) # Random target for loss computation
# Measure the time taken for a forward pass
start_time = time.time()
output = model(input_data)
forward_time = time.time() - start_time
fwd.append(forward_time)
# Measure the time taken for a backward pass
loss = loss_fn(output, target) # Compute loss
model.zero_grad()
start_time = time.time()
loss.backward()
backward_time = time.time() - start_time
bckwd.append(backward_time)
# Print the results
if not quiet:
print(
f"{lstm_cls.__name__} on {device}: Seq Length: {seq_len}, Forward: {forward_time:.5f} seconds, Backward: {backward_time:.5f} seconds"
)
return sum(fwd) / n_repeats, sum(bckwd) / n_repeats
# Parameters
input_size = 16 # Number of input features
hidden_size = 128 # Number of LSTM units
batch_size = 32 # Number of sequences to process in parallel
sequence_lengths = [
10,
20,
50,
100,
200,
500,
1000,
2000,
4000,
] # Different sequence lengths to benchmark
# Run the benchmark
for cls in [PackedLSTM, SimpleLSTM, ]:
forward_times = []
backward_times = []
for seq_len in sequence_lengths:
benchmark(
cls, input_size, hidden_size, batch_size, seq_len, quiet=True
)
forward_time, backward_time = benchmark(
cls, input_size, hidden_size, batch_size, seq_len
)
forward_times.append(forward_time)
backward_times.append(backward_time)
print(f"forward_times_{cls.__name__} = {forward_times}")
print(f"backward_times_{cls.__name__} = {backward_times}")
# # Plotting the results
# plt.figure(figsize=(10, 5))
# plt.plot(sequence_lengths, forward_times, label="Forward Time", marker="o")
# plt.plot(sequence_lengths, backward_times, label="Backward Time", marker="o")
# plt.xlabel("Sequence Length")
# plt.ylabel("Time (seconds)")
# plt.title(f"{cls.__name__} Forward and Backward Pass Time vs Sequence Length")
# plt.legend()
# plt.grid()
# plt.ylim(-10, 185)
# # plt.xscale('log') # Use logarithmic scale for better visualization
# # plt.yscale('log') # Use logarithmic scale for better visualization
# plt.show()
# import cProfile
# import io
# import pstats
#
#
# def profile_function(f, *args, **kwargs):
# pr = cProfile.Profile()
# pr.enable()
# result = f(*args, **kwargs)
# pr.disable()
#
# s = io.StringIO()
# ps = pstats.Stats(pr, stream=s).sort_stats("cumulative")
# ps.print_stats()
#
# print(s.getvalue()) # Print the profiling results
# return result # Return the original function result
#
#
# profile_function(benchmark, PackedLSTM, input_size, hidden_size, batch_size, 1_000)
```
### Observations, Implications
I see multiple posts about this in forums and stack overflow: https://stackoverflow.com/questions/72073853/pytorch-pack-padded-sequence-is-extremely-slow
https://discuss.pytorch.org/t/gru-training-very-slow-with-sequence-packing/192222
https://discuss.pytorch.org/t/pytorch-pack-padded-sequence-is-really-slow/150508
It must be that most people a) don't use PackedSequence in the first place, or b) didn't use big values of T in their timeseries and didn't mind the ~3-5 times slowdown for small T. Otherwise this is a big blocker. I'm using PackedSequence to deal with sometimes short sequences in replay buffer in RL context.
I would just use forward on padded sequence, but then I can't get correct final state. The problem is that in RL, I want to get the final state on history, and then do single step forward from that step on different possible inputs (critic Q(s,a) in SAC, for example).
Profiling has shown that most time is spent in forward / backward methods, not in packing / unpacking.
### Versions
Collecting environment information...
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Red Hat Enterprise Linux release 8.10 (Ootpa) (x86_64)
GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-22)
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.28
Python version: 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-553.22.1.el8_10.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
MIG 1g.10gb Device 0:
Nvidia driver version: 560.35.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 1
Core(s) per socket: 48
Socket(s): 2
NUMA node(s): 2
Vendor ID: AuthenticAMD
CPU family: 25
Model: 1
Model name: AMD EPYC 7643 48-Core Processor
Stepping: 1
CPU MHz: 2300.000
CPU max MHz: 3640.9170
CPU min MHz: 1500.0000
BogoMIPS: 4591.43
Virtualization: AMD-V
L1d cache: 32K
L1i cache: 32K
L2 cache: 512K
L3 cache: 32768K
NUMA node0 CPU(s): 0-47
NUMA node1 CPU(s): 48-95
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca sme sev sev_es
Versions of relevant libraries:
[pip3] mypy==1.8.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.3
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] optree==0.10.0
[pip3] torch==2.6.0
[pip3] triton==3.2.0
[conda] numpy 1.26.3 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] optree 0.10.0 pypi_0 pypi
[conda] torch 2.6.0 pypi_0 pypi
[conda] triton 3.2.0 pypi_0 pypi
cc @mikaylagawarecki | open | 2025-03-14T14:01:34Z | 2025-03-17T15:20:08Z | https://github.com/pytorch/pytorch/issues/149194 | [
"module: rnn",
"triaged",
"topic: performance"
] | ikamensh | 0 |
neuml/txtai | nlp | 243 | Add notebook for Embeddings SQL functions | Add notebook for Embeddings SQL functions | closed | 2022-03-09T12:34:35Z | 2022-03-09T12:36:43Z | https://github.com/neuml/txtai/issues/243 | [] | davidmezzetti | 0 |
deepfakes/faceswap | deep-learning | 1,017 | Check failed: vec.size() == NDIMS | 5/03/2020 20:04:39 INFO No existing state file found. Generating.
05/03/2020 20:04:42 INFO Creating new 'original' model in folder: 'E:\AiLearning\project\model'
05/03/2020 20:04:42 INFO Loading Trainer from Original plugin...
05/03/2020 20:04:42 INFO Enabled TensorBoard Logging
2020-05-03 20:05:01.916652: F .\tensorflow/core/util/bcast.h:111] Check failed: vec.size() == NDIMS (1 vs. 2)
Process exited.
why?
help me | closed | 2020-05-03T12:08:43Z | 2020-08-03T07:42:08Z | https://github.com/deepfakes/faceswap/issues/1017 | [] | chunxingque | 1 |
jupyter-widgets-contrib/ipycanvas | jupyter | 58 | CanvasView and clear_rect | Clearing a rectangle is not visible in previous views of the widget. To be more precise construct a canvas widget and show it:
```
from ipycanvas import Canvas
canvas = Canvas(size=(200, 200))
canvas.fill_rect(0,0,100,100)
canvas
```
Now clear a rectangle:
```
canvas.clear_rect(0,0,20,20)
```
This does not result in a change of the visible canvas widget.
Showing another instance of the widget by putting
```
canvas
```
in a new cell shows the cleared rectangle however.
Is this intentional?
It seems to be due to the fact that the views are updated by drawing the model canvas on them, but clearing a rectangle makes it transparent. Putting `this.clear()` before
the call to `drawImage` in `updateCanvas` fixes this for me. | closed | 2019-12-20T15:56:44Z | 2019-12-22T09:46:24Z | https://github.com/jupyter-widgets-contrib/ipycanvas/issues/58 | [] | JeremiasE | 5 |
jupyterlab/jupyter-ai | jupyter | 865 | Remove "Replace selection" checkbox | ### Problem
Same as #864.
Current "replace selection" checkbox implementation is likely broken by #859.
### Proposed Solution
- Remove the "replace selection" checkbox
- Allow for text selection to be replaced if a text selection is active in the code action toolbar (which currently only allows for the active cell to be replaced).
- Implement a message-global "Replace selection" action within a hamburger menu at the top of each message (e.g. next to the timestamp). This should observe the same behavior as the Replace button in the code action toolbar, i.e. replace text if a text selection is active, replace cell if there exists an active cell, and render as disabled if neither condition is satisfied.
| closed | 2024-06-30T13:53:42Z | 2024-07-12T23:19:43Z | https://github.com/jupyterlab/jupyter-ai/issues/865 | [
"enhancement",
"priority"
] | dlqqq | 0 |
tensorflow/tensor2tensor | machine-learning | 1,232 | How much more memory and computation does Universal Transformer require than vanilla Transformer for translation? | If i'm not misunderstanding, Universal Transformer uses bigger but shared encoder and decoder blocks,
So if with same number of parameters for the whole model , each Universal Transformer block will need much more memory and computation than vanilla Transformer with same number of layers(or recurrent steps for UT).
Can you share the batch size for Universal Transformer and vanilla Transformer during training with same gpu, and speed of inference?
@MostafaDehghani | closed | 2018-11-17T04:05:56Z | 2018-11-21T09:45:26Z | https://github.com/tensorflow/tensor2tensor/issues/1232 | [] | wenyong-h | 2 |
jpadilla/django-rest-framework-jwt | django | 104 | Should getting a token require POST? | This is mostly academic, but retrieving a JWT token has no side-effects on the server, so I reckon it should be possible with a GET request. Thoughts?
| closed | 2015-04-24T05:53:43Z | 2015-04-24T12:30:45Z | https://github.com/jpadilla/django-rest-framework-jwt/issues/104 | [] | AlexHill | 1 |
mwaskom/seaborn | pandas | 2,841 | RFE: move from husl to hsluv | Looks like `husl` module is no longer maintained (last release was in 2015) and instead can be used `hsluv`
https://pypi.org/project/hsluv/
https://github.com/hsluv/hsluv-python/
| closed | 2022-06-08T17:15:27Z | 2022-07-16T12:30:24Z | https://github.com/mwaskom/seaborn/issues/2841 | [] | kloczek | 22 |
zwczou/weixin-python | flask | 61 | AttributeError: invalid attribute "notify_url" | File "/home/tzy/venvs/wxpay/lib/python2.7/site-packages/weixin/msg.py", line 216, in __getattr__
raise AttributeError('invalid attribute "' + key + '"')
AttributeError: invalid attribute "notify_url
请问这个问题怎么解决
python == 2.7.5
用的是flask框架
调用pay_jsapi接口时报错 | closed | 2020-03-03T04:28:37Z | 2020-03-30T02:44:34Z | https://github.com/zwczou/weixin-python/issues/61 | [] | Near-Tam | 1 |
sinaptik-ai/pandas-ai | data-visualization | 1,665 | MySQL columns type and result type problems | # Issue Title: pandasai Chat Function Returns Non-numeric Value for Expected Numeric Result
## Description
When using the `pai.chat` function to query data, the function expects a numeric value but returns a non-numeric value, resulting in an `InvalidOutputValueMismatch` exception. Additionally, I would like to know if the column type definitions in pandasai can be consistent with MySQL types such as `varchar(32)` and `datetime`.
## Code Example
`import pandasai as pai
from pandasai_local import LocalLLM
import matplotlib
import matplotlib.pyplot as plt
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('app.log', encoding='utf-8'),
logging.StreamHandler()
]
)
lm_studio_llm = LocalLLM(api_base="http://localhost:11434/v1", model="deepseek-coder-v2:16b", api_key="ollama")
pai.config.set({"llm": lm_studio_llm })
ndtzeqk = pai.load("example/sys-mkt-tender-count")
yggrxx = pai.load("example/hr-employee-personal")
zbxxdjd = pai.load("example/sys-mkt-bidding-wf")
gcxmxx = pai.load("example/sys-mkt-project")
respose = pai.chat("业主合同管理中拓展额是多少", ndtzeqk, yggrxx, zbxxdjd, gcxmxx)
print(respose)
`
## Error Message
/home/alan/anaconda3/envs/d_a2/bin/python /home/alan/DataE_v3/api_sql_v3.py
Dataset loaded successfully.
Dataset loaded successfully.
Dataset loaded successfully.
Dataset loaded successfully.
Dataset loaded successfully.
Traceback (most recent call last):
File "/home/alan/DataE_v3/api_sql_v3.py", line 27, in <module>
respose = pai.chat("豪方东园燃气工程业主合同的建安拓展额是多少？结果以数值类型输出", ndtzeqk, yggrxx, zbxxdjd, gcxmxx, yzhtgl)
...
File "/home/alan/anaconda3/envs/d_a2/lib/python3.11/site-packages/pandasai/core/response/parser.py", line 43, in _validate_response
raise InvalidOutputValueMismatch(
pandasai.exceptions.InvalidOutputValueMismatch: Invalid output: Expected a numeric value for result type 'number', but received a non-numeric value.
## Additional Questions
Can the type definition of columns in pandasai be consistent with MySQL types such as varchar(32) and datetime?
## Expected Behavior
The pai.chat function should return a numeric value when a numeric result is expected, and column type definitions should be compatible with MySQL types.
## Possible Solution
I’m not sure whether the issue is with pandasai or the llm. | open | 2025-03-10T09:35:00Z | 2025-03-10T12:25:21Z | https://github.com/sinaptik-ai/pandas-ai/issues/1665 | [] | Alan-zhong | 2 |
gradio-app/gradio | data-science | 10,661 | ModuleNotFoundError: No module named 'websockets.asyncio' when importing supabase | ### Describe the bug
I am using supabase as the backend for my project and hosting it on Hugging Face. It was working about a month ago, but now I'm getting this runtime error when I try to import supabase: "ModuleNotFoundError: No module named 'websockets.asyncio'".
I have added asyncio and websockets to my requirements.txt file. I tried to add websockets.asycio to the requirements file as well, but that caused a build error. I also did a full factory rebuild, and that didn't help either.
Note that everything works when I run the code locally; the problem is only when I run it on Hugging Face.
Thanks in advance!
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
import supabase
```
### Screenshot
_No response_
### Logs
```shell
===== Application Startup at 2025-02-23 19:10:40 =====
Traceback (most recent call last):
File "/home/user/app/workshops.py", line 11, in <module>
import supabase
File "/usr/local/lib/python3.10/site-packages/supabase/__init__.py", line 13, in <module>
from realtime import AuthorizationError, NotConnectedError
File "/usr/local/lib/python3.10/site-packages/realtime/__init__.py", line 9, in <module>
from ._async.client import AsyncRealtimeClient
File "/usr/local/lib/python3.10/site-packages/realtime/_async/client.py", line 13, in <module>
from websockets.asyncio.client import ClientConnection, connect
ModuleNotFoundError: No module named 'websockets.asyncio'
Traceback (most recent call last):
File "/home/user/app/workshops.py", line 11, in <module>
import supabase
File "/usr/local/lib/python3.10/site-packages/supabase/__init__.py", line 13, in <module>
from realtime import AuthorizationError, NotConnectedError
File "/usr/local/lib/python3.10/site-packages/realtime/__init__.py", line 9, in <module>
from ._async.client import AsyncRealtimeClient
File "/usr/local/lib/python3.10/site-packages/realtime/_async/client.py", line 13, in <module>
from websockets.asyncio.client import ClientConnection, connect
ModuleNotFoundError: No module named 'websockets.asyncio'
```
### System Info
```shell
As mentioned earlier, the issue is only when I run the code on Hugging Face, which is presumably running the latest version of Gradio.
```
### Severity
Blocking usage of gradio | closed | 2025-02-23T19:16:44Z | 2025-03-03T02:53:53Z | https://github.com/gradio-app/gradio/issues/10661 | [
"bug",
"needs repro"
] | loganpearce17 | 5 |
jupyterhub/zero-to-jupyterhub-k8s | jupyter | 3,463 | Jupyterhub stable docker image not working out of the box | Hello,
Apologies in advance for polluting this repo with my request but it sounds like it should work out of the box.
I'm trying to do a very basic setup on the latest version of jupyterhub and I'm stuck on something very simple.
But as this is the standard docker image, I'm like what's going on... it should work right?
I'm using minikube on a MacBook Pro M1 with docker image: jupyterhub/k8s-hub:3.3.7
I'm getting this:
```
File "/usr/local/etc/jupyterhub/jupyterhub_config.py", line 9, in <module>
from kubernetes import client
ModuleNotFoundError: No module named 'kubernetes'
```
Now if I add:
```
RUN pip3 install kubernetes
```
in the docker file
I then get:
```
File "/usr/local/etc/jupyterhub/z2jh.py", line 6, in <module>
from collections import Mapping
ImportError: cannot import name 'Mapping' from 'collections' (/usr/local/lib/python3.11/collections/__init__.py)
```
And I'm stuck.
I thought it was a Python version issue but clearly that image specifies Python 3.11 in its configuration.
Let me know if I'm missing something obvious.
Is 3.3.7 the latest stable release? 4.0.0 tags are all dev it seems...
Thank you for your support.
```
python3 --version
Python 3.11.9
``` | closed | 2024-07-19T08:16:27Z | 2024-07-21T22:39:31Z | https://github.com/jupyterhub/zero-to-jupyterhub-k8s/issues/3463 | [
"support"
] | ThomasLabstep | 3 |
MycroftAI/mycroft-core | nlp | 2,656 | skill keep reloading indefinitely if there are files with a future date | due to a temporary problem in my system, i ended up with a skill with __init.py modification time set in the future. this caused that skill to constantly be reloaded my mycroft core, and unloaded just after the loading was complted. took some hours of debug to understand this was actually the problem.
perhaps skills with files with modification date from the future should just be stopped from loading and have a debug log about it? | closed | 2020-08-12T14:48:15Z | 2020-09-22T11:25:36Z | https://github.com/MycroftAI/mycroft-core/issues/2656 | [] | notmart | 1 |
mlfoundations/open_clip | computer-vision | 918 | Error loading state_dict using pre-trained checkpoint with CustomTextCLIP model | Dear developers:
I recently reinstalled the latest environment for open_clip. When I tried to train new data using a pre-trained checkpoint and model, I encountered an error. The error message indicates that almost all keys in the state_dict are missing or not recognized.
traning parameters are:
train_command = [
'python', '-m', 'open_clip_train.main',
'--save-frequency', '1',
'--zeroshot-frequency', '1',
'--report-to', 'tensorboard',
'--train-data', train_csv,
'--val-data', val_csv,
'--csv-img-key', 'img_path',
'--csv-caption-key', 'label',
'--warmup', '1000',
'--batch-size', '32',
'--lr', '5e-6',
'--wd', '0.1',
'--epochs', '1000',
'--workers', '4',
'--model','hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224',
'--csv-separator', ',',
'--pretrained','../models/open_clip-main/logs/-model_coca_ViT-L-14-lr_5e-06-b_64-j_8-p_amp/checkpoints/epoch_20.pt',
'--val-frequency', '10'
]
subprocess.run(train_command)
Error Message:
RuntimeError: Error(s) in loading state_dict for CustomTextCLIP:
Missing key(s) in state_dict: "visual.trunk.cls_token", "visual.trunk.pos_embed", "visual.trunk.patch_embed.proj.weight", "visual.trunk.patch_embed.proj.bias", "visual.trunk.blocks.0.norm1.weight", "visual.trunk.blocks.0.norm1.bias", "visual.trunk.blocks.0.attn.qkv.weight", "visual.trunk.blocks.0.attn.qkv.bias", "visual.trunk.blocks.0.attn.proj.weight", "visual.trunk.blocks.0.attn.proj.bias", "visual.trunk.blocks.0.norm2.weight", "visual.trunk.blocks.0.norm2.bias", "visual.trunk.blocks.0.mlp.fc1.weight", "visual.trunk.blocks.0.mlp.fc1.bias", "visual.trunk.blocks.0.mlp.fc2.weight", "visual.trunk.blocks.0.mlp.fc2.bias", ...
I don't know how it happened. | closed | 2024-07-22T22:41:17Z | 2024-07-23T19:36:43Z | https://github.com/mlfoundations/open_clip/issues/918 | [] | cubense | 2 |
ansible/ansible | python | 84,794 | delegate_to localhost listed as running on remote host in play recap | ### Summary
When I run a task with Ansible on a remote host and a second task locally with `delegate_to`, the summary of all tasks shows both tasks ran at the remote host and none on the local host.
If I verify this, it shows that the location of where the tasks are supposed to run is correct, but the play recap is partly misleading.
The following playbook illustrates the issue
```yaml
---
# file: playbook.yml
- name: Test playbook
hosts: all
gather_facts: false
tasks:
- name: Run on the remote
ansible.builtin.command:
cmd: hostname
register: _output_remote
- name: Run locally
ansible.builtin.command:
cmd: hostname
delegate_to: localhost
register: _output_local
- name: Output hostnames
ansible.builtin.debug:
msg: "Ran on {{ item }}"
loop:
- "{{ _output_remote['stdout'] }}"
- "{{ _output_local['stdout'] }}"
```
If you run this playbook with `ansible-playbook, -i remote_host.example.com, playbook.yml` the output will show three tasks being executed:
1. `Run on the remote`
2. `Run locally`
3. `Output hostnames`
The output showing:
```
```
Task 2 and 3 are supposed to run locally. Task 2 runs locally, showing the local host name `local_host`. Task 3 runs locally, since all use of `ansible.builtin.debug` is automatically running locally.
### Issue Type
Bug Report
### Component Name
default_callback
### Ansible Version
```console
ansible --version
ansible [core 2.18.2]
[...]
python version = 3.13.2 (main, Feb 4 2025, 00:00:00) [GCC 14.2.1 20250110 (Red Hat 14.2.1-7)] (/usr/bin/python3)
jinja version = 3.1.5
libyaml = True
```
### Configuration
```console
# if using a version older than ansible-core 2.12 you should omit the '-t all'
$ ansible-config dump --only-changed -t all
```
### OS / Environment
Fedora 40/41
### Steps to Reproduce
```yaml
---
# file: playbook.yml
- name: Test playbook
hosts: all
gather_facts: false
tasks:
- name: Run on the remote
ansible.builtin.command:
cmd: hostname
register: _output_remote
- name: Run locally
ansible.builtin.command:
cmd: hostname
delegate_to: localhost
register: _output_local
- name: Output hostnames
ansible.builtin.debug:
msg: "Ran on {{ item }}"
loop:
- "{{ _output_remote['stdout'] }}"
- "{{ _output_local['stdout'] }}"
```
### Expected Results
I was expecting to see `local_host.local.int` being added to the play recap. The output of task 2 indicates that the target host `remote_host.example.com` has been changed to `localhost`, but the recap at the bottom misses it completely.
There does not seem to be an attribution of locally delegated tasks to the local host within the recap. Other callback plugins with list by execution, time, etc seem to make this distinction, which then leads to contradicting information between the recap and the rest of the output.
### Actual Results
```console
[...]
TASK [Run on the remote]
[...]
changed: [remote_.host.example.com]
TASK [Run locally]
[...]
changed: [remote_host.example.com -> localhost]
TASK [Output hostnames]
[....]
ok: [remote_host.example.com] => (item=remote_host) => {
"msg": "Ran on remote_host"
}
ok: [remote_host.example.com] => (item=local_host) => {
"msg": "Ran on local_host.local.int"
}
PLAY RECAP *******************************************************************************************************************
remote_host.example.com : ok=3 changed=2 unreachable=0 failed=0 skipped=0 rescued=0 ignored=0
....
```
### Code of Conduct
- [x] I agree to follow the Ansible Code of Conduct | closed | 2025-03-08T08:34:43Z | 2025-03-18T08:01:22Z | https://github.com/ansible/ansible/issues/84794 | [
"bug",
"affects_2.18"
] | Spreadcat | 3 |
graphdeco-inria/gaussian-splatting | computer-vision | 921 | How do I do backpropagation for depth? | We are conducting research through 3dgs.
(Submodules / diff-guassian-rasterization / cuda_rasterization)
I don't know how to do the backpropagation to depth here.
| open | 2024-08-03T02:40:11Z | 2024-08-03T02:40:11Z | https://github.com/graphdeco-inria/gaussian-splatting/issues/921 | [] | Kohwang | 0 |
Farama-Foundation/PettingZoo | api | 518 | [Proposal] Adding type hints | Per [this](https://sethmlarson.dev/blog/2021-10-18/tests-arent-enough-case-study-after-adding-types-to-urllib3) recently article, we really should work to add type hints to Gym over time. This is something that can be worried about after the major upcoming changes are planned, but none-the-less this is important for a package of Gym's scale. Incremental contributions towards this end are very welcome. I'm also going to be worrying about getting this added to Gym. | closed | 2021-10-18T21:56:04Z | 2022-05-02T22:44:43Z | https://github.com/Farama-Foundation/PettingZoo/issues/518 | [
"help wanted",
"in progress"
] | jkterry1 | 0 |
miguelgrinberg/Flask-SocketIO | flask | 1,512 | failed: WebSocket is closed before the connection is established. | **Your question**
I am writing a website in Flask, use gunicorn + nginx. To exchange data with the client, I use Flask-SocketIO.
I get the error in console:
```
websocket.js:88 WebSocket connection to 'ws://185.151.245.211/socket.io/?EIO=4&transport=websocket&sid=leA-pXn1TBOM1dPMAAAp' failed:
websocket.js:88 WebSocket connection to 'ws://185.151.245.211/socket.io/?EIO=4&transport=websocket&sid=IJqPh-L8-GzUCHaFAAAq' failed:
websocket.js:205 WebSocket connection to 'ws://185.151.245.211/socket.io/?EIO=4&transport=websocket&sid=hz86wPr5hCgPmG1qAAAr' failed: WebSocket is closed before the connection is established.
```
**Please tell me what the problem is?**
_wsgi.py_:
```
from webapp import app, socketio
if __name__ == '__main__':
socketio.run(app, logger=True, engineio_logger=True, use_reloader=False)
```
_gunicorn.conf.py_:
```
bind = '185.151.245.211:8000'
max_requests = 1000
worker_class = "gevent"
workers = 1
loglevel = "info"
```
_webapp/__init__.py_:
```
from gevent import monkey
monkey.patch_all()
import grpc.experimental.gevent
grpc.experimental.gevent.init_gevent()
from flask import Flask, session, request
from config import MqttConfig, MailConfig, ProductionConfig
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_mail import Mail
from flask_script import Manager
from flask_socketio import SocketIO
from flask_mqtt import Mqtt
from flask_login import LoginManager
from flask_babel import lazy_gettext as _l
from apscheduler.schedulers.gevent import GeventScheduler
app = Flask(__name__)
app.config.from_object(ProductionConfig)
app.config.from_object(MqttConfig)
app.config.from_object(MailConfig)
db = SQLAlchemy(app)
migrate = Migrate(app, db, render_as_batch=True)
mail = Mail(app)
mqtt = Mqtt(app)
manager = Manager(app, db)
login_manager = LoginManager(app)
login_manager.login_view = 'auth'
login_manager.login_message = _l("Необходимо авторизоваться для доступа к закрытой странице")
login_manager.login_message_category = "error"
scheduler = GeventScheduler()
scheduler.start()
socketio = SocketIO(app, async_mode='gevent') # Production Version
import webapp.views
from webapp import models
if __name__ == "__main__":
manager.run()
```
_profile.html_:
```
<html>
<script src="https://cdn.socket.io/3.1.1/socket.io.min.js" integrity="sha384-gDaozqUvc4HTgo8iZjwth73C6dDDeOJsAgpxBcMpZYztUfjHXpzrpdrHRdVp8ySO" crossorigin="anonymous"></script>
<script>
document.addEventListener("DOMContentLoaded", function(event) {
var socket = io.connect('http://' + document.domain + ':' + location.port);
socket.on('connect', function() {
console.log(socket.id);
});
});
</script>
</html>
```
_/etc/nginx/sites-available/projectnew_:
```
server {
listen 80;
server_name 185.151.245.211 www.185.151.245.211;
client_max_body_size 75M;
location / {
include proxy_params;
proxy_pass http://185.151.245.211:8000;
}
location /static {
alias /home/sammy/projectnew/webapp/static;
expires 30d;
}
location /socket.io {
include proxy_params;
proxy_http_version 1.1;
proxy_buffering off;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "Upgrade";
proxy_set_header Host $host;
proxy_pass http://185.151.245.211:8000/socket.io;
}
}
```
_/etc/systemd/system/projectnew.service_:
```
[Unit]
Description=Gunicorn instance to serve projectnew
After=network.target
[Service]
User=sammy
Group=www-data
WorkingDirectory=/home/sammy/projectnew
Environment="PATH=/home/sammy/projectnew/projectenv/bin"
ExecStart=/home/sammy/projectnew/projectenv/bin/gunicorn wsgi:app
[Install]
WantedBy=multi-user.target
```
**Logs**
_nginx error.log_:
`2021/04/01 13:36:29 [error] 204428#204428: *7895 connect() failed (111: Connection refused) while connecting to upstream, client: 176.105.207.105, server: 185.151.245.211, request: "GET /profile/2 HTTP/1.1", upstream: "http://185.151.245.211:8000/profile/2", host: "185.151.245.211", referrer: "http://185.151.245.211/contacts"`
_projectnew journal_:
`Apr 01 13:54:37 toaa gunicorn[205658]: http://185.151.245.211 is not an accepted origin. (further occurrences of this error will be logged with level INFO)`
| closed | 2021-04-01T12:03:11Z | 2021-04-01T13:51:46Z | https://github.com/miguelgrinberg/Flask-SocketIO/issues/1512 | [
"question"
] | Yasha261998 | 2 |
plotly/dash | jupyter | 2,733 | [BUG] dcc.Dropdown value does not update when an option is removed (regression from #1868) |
**Describe your context**
Please provide us your environment, so we can easily reproduce the issue.
- replace the result of `pip list | grep dash` below
```
dash 2.13.0
```
**Describe the bug**
Regression #1868
When the options of a dcc.Dropdown are updated to remove a currently selected option, the UI updates to remove that value, but the value parameter does not update.
**Expected behavior**
Removing an option from options should also remove that option from value, that way it will be sync with the UI.
**Screenshots**
Using the same code as #1868 but with version 2.13.0 we have
| closed | 2024-01-29T14:44:17Z | 2024-04-09T14:12:46Z | https://github.com/plotly/dash/issues/2733 | [
"regression",
"bug",
"sev-2"
] | Thuener | 1 |
AirtestProject/Airtest | automation | 467 | 手机锁屏后执行脚本异常 | @yimelia
在命令行里运行如下命令：
py -3.6 airtest run test2.air --device android:///10.0.2.55:7556?ori_method=ADBORI&&touch_method=ADBTOUCH --log
当手机锁屏时，脚本运行结果会异常，提示找不到目标
某些手机（vivox23）无法设置永不锁屏（最多30分钟），脚本应如何设置，才能在手机锁屏时进行解锁（或者在运行前先自动进行解锁操作）
错误截图如下：
| open | 2019-07-22T03:13:17Z | 2019-07-22T03:32:25Z | https://github.com/AirtestProject/Airtest/issues/467 | [
"bug",
"module/yosemite"
] | ymdhtt | 4 |
junyanz/pytorch-CycleGAN-and-pix2pix | deep-learning | 1,584 | Applying to a general image restoration task | Hello
How are you?
Thanks for contributing to this project.
I am going to use this method for a general image restoration (low quality -> high quality).
There are several kinds of images such as blur and noise samples with noise in the low quality images.
Is it possible to apply this method without any architecture change to such a general image restoration task?
Thanks | open | 2023-06-20T11:37:48Z | 2023-06-20T11:37:48Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1584 | [] | rose-jinyang | 0 |
microsoft/nni | deep-learning | 5,134 | torch.jit._trace.TracingCheckError: Tracing failed sanity checks! ERROR: Graphs differed across invocations! | **Describe the issue**:
Hello, I'm using NNI to prune my model(DD3D https://github.com/TRI-ML/dd3d). And an error as shown in the title occurred when initializing a class named ModelSpeedup. After debugging, it is found that the torch.jit.trace API was called when the TorchGraph class was initialized. However, the DD3D model cannot use this interface. Is there any other solution.
**Environment**:
- NNI version: 2.8
- Training service (local|remote|pai|aml|etc): local
- Client OS:
- Server OS (for remote mode only):
- Python version: 2.8
- PyTorch/TensorFlow version: torch 1.9.0+cu102
- Is conda/virtualenv/venv used?: conda
- Is running in Docker?: no
**Log message**:
File "/home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/jit/_trace.py", line 744, in trace
_module_class,
File "/home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/jit/_trace.py", line 985, in trace_module
_module_class,
File "/home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/jit/_trace.py", line 521, in _check_trace
raise TracingCheckError(*diag_info)
torch.jit._trace.TracingCheckError: Tracing failed sanity checks!
ERROR: Graphs differed across invocations!
Graph diff:
graph(%self.1 : __torch__.tridet.modeling.dd3d.core.DD3D,
%batched_inputs : Tensor):
%2 : __torch__.tridet.modeling.dd3d.fcos3d.FCOS3DHead = prim::GetAttr[name="fcos3d_head"](%self.1)
%3 : __torch__.tridet.modeling.dd3d.fcos2d.FCOS2DHead = prim::GetAttr[name="fcos2d_head"](%self.1)
%4 : __torch__.detectron2.modeling.backbone.fpn.FPN = prim::GetAttr[name="backbone"](%self.1)
%60 : float = prim::Constant[value=2.](), scope: __module.backbone # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/nn/functional.py:3690:0
- %61 : Tensor = prim::Constant[value={1e-05}](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer/__module.backbone.bottom_up.base_layer.norm # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/batch_norm.py:48:0
? ^^^^^^ ^ ^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^
+ %61 : float = prim::Constant[value=0.10000000000000001](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer/__module.backbone.bottom_up.base_layer.norm # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/nn/functional.py:2282:0
? ^^^^^ ^^ ^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^ ^ +
- %62 : int = prim::Constant[value=-1](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer/__module.backbone.bottom_up.base_layer.norm # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/batch_norm.py:50:0
? ^^^ ^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ %62 : float = prim::Constant[value=1.0000000000000001e-05](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer/__module.backbone.bottom_up.base_layer.norm # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/torch/nn/functional.py:2282:0
? ^^^^^ +++++++++++++++++++ ^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
- %63 : int = prim::Constant[value=6](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer/__module.backbone.bottom_up.base_layer.norm # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/batch_norm.py:53:0
? ^^^ ^ -------------------------------------------- ^^^^^^^^^^^^^^ -
+ %63 : bool = prim::Constant[value=1](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^^ ^ ^^^^^^^^^^^^^^
- %64 : bool = prim::Constant[value=1](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^^ ^
+ %64 : int = prim::Constant[value=0](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^ ^
- %65 : int = prim::Constant[value=0](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^
+ %65 : bool = prim::Constant[value=0](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^^
- %66 : bool = prim::Constant[value=0](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^^ ^
+ %66 : int = prim::Constant[value=3](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^ ^
- %67 : int = prim::Constant[value=3](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^
+ %67 : int = prim::Constant[value=1](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^
- %68 : int = prim::Constant[value=1](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
- %69 : NoneType = prim::Constant(), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer
? ^
+ %68 : NoneType = prim::Constant(), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.base_layer
? ^
- %70 : int = prim::Constant[value=2](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.level1/__module.backbone.bottom_up.level1.0 # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^^^^^^
+ %69 : int = prim::Constant[value=2](), scope: __module.backbone/__module.backbone.bottom_up/__module.backbone.bottom_up.level1/__module.backbone.bottom_up.level1.0 # /home/yaoshw/anaconda3/envs/DD3D/lib/python3.7/site-packages/detectron2/layers/wrappers.py:215:0
? ^^^^^^^^
- %71 : __torch__.detectron2.modeling.backbone.fpn.LastLevelP6P7 = prim::GetAttr[name="top_block"](%4)
? ^
+ %70 : __torch__.detectron2.modeling.backbone.fpn.LastLevelP6P7 = prim::GetAttr[name="top_block"](%4)
? ^
- %72 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_output3"](%4)
? ^
+ %71 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_output3"](%4)
? ^
- %73 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_lateral3"](%4)
? ^
+ %72 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_lateral3"](%4)
? ^
- %74 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_output4"](%4)
? ^
+ %73 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_output4"](%4)
? ^
- %75 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_lateral4"](%4)
? ^
+ %74 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_lateral4"](%4)
? ^
- %76 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_output5"](%4)
? ^
+ %75 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_output5"](%4)
? ^
- %77 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_lateral5"](%4)
? ^
+ %76 : __torch__.detectron2.layers.wrappers.Conv2dCB = prim::GetAttr[name="fpn_lateral5"](%4)
? ^
- %78 : __torch__.tridet.modeling.feature_extractor.dla.DLA = prim::GetAttr[name="bottom_up"](%4)
? ^
+ %77 : __torch__.tridet.modeling.feature_extractor.dla.DLA = prim::GetAttr[name="bottom_up"](%4)
? ^
- %79 : __torch__.tridet.modeling.feature_extractor.dla.Tree = prim::GetAttr[name="level5"](%78)
? ^ ^
+ %78 : __torch__.tridet.modeling.feature_extractor.dla.Tree = prim::GetAttr[name="level5"](%77)
? ^ ^
+ %79 : __torch__.tridet.modeling.feature_extractor.dla.Tree = prim::GetAttr[name="level4"](%77)
......
**How to reproduce it?**:
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "../"))
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
import logging
from collections import OrderedDict, defaultdict
import hydra
import torch
import wandb
from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
from torch.cuda import amp
from torch.nn import SyncBatchNorm
from torch.nn.parallel import DistributedDataParallel
from tqdm import tqdm
import detectron2.utils.comm as d2_comm
from detectron2.data import MetadataCatalog
from detectron2.evaluation import DatasetEvaluators, inference_on_dataset
from detectron2.modeling import build_model
from detectron2.solver import build_lr_scheduler, build_optimizer
from detectron2.utils.events import CommonMetricPrinter, get_event_storage
import tridet.modeling # pylint: disable=unused-import
import tridet.utils.comm as comm
from tridet.data import build_test_dataloader, build_train_dataloader
from tridet.data.dataset_mappers import get_dataset_mapper
from tridet.data.datasets import random_sample_dataset_dicts, register_datasets
from tridet.evaluators import get_evaluator
from tridet.modeling import build_tta_model
from tridet.utils.s3 import sync_output_dir_s3
from tridet.utils.setup import setup
from tridet.utils.train import get_inference_output_dir, print_test_results
from tridet.utils.visualization import mosaic, save_vis
from tridet.utils.wandb import flatten_dict, log_nested_dict
from tridet.visualizers import get_dataloader_visualizer, get_predictions_visualizer
from tridet.structures.image_list import ImageList
import cv2
print('CUDA available: {}'.format(torch.cuda.is_available()))
device = 'cuda'
@hydra.main(config_path="../configs/", config_name="defaults")
def main(cfg):
setup(cfg)
model = build_model(cfg)
checkpoint_file = "../model_final.pth"#cfg.MODEL.CKPT
if checkpoint_file:
Checkpointer(model).load(checkpoint_file)
model_input = (torch.zeros((1, 3, 384, 1280), dtype=torch.float32, device='cuda:0'))
config_list = [{
'sparsity_per_layer': 0.3,
'op_types': ['Conv2d']
}, {
'exclude': True,
'op_names': ['fcos2d_head.cls_logits','fcos2d_head.box2d_reg','fcos3d_head.box3d_quat.0',
'fcos3d_head.box3d_ctr.0', 'fcos3d_head.box3d_depth.0', 'fcos3d_head.box3d_size.0',
'fcos3d_head.box3d_conf.0']
}]
from nni.compression.pytorch.pruning import L1NormPruner
pruner = L1NormPruner(model, config_list)
_, masks = pruner.compress()
pruner._unwrap_model()
from nni.compression.pytorch.speedup import ModelSpeedup
spedd_up = ModelSpeedup(model, model_input, masks,map_location=None,batch_dim=0, confidence=1)
spedd_up.speedup_model()
if __name__ == '__main__':
main() # pylint: disable=no-value-for-parameter
| closed | 2022-09-21T02:29:35Z | 2022-10-14T08:43:37Z | https://github.com/microsoft/nni/issues/5134 | [
"waiting user confirm",
"support"
] | yaoshw | 9 |
sktime/sktime | data-science | 7,368 | [ENH] interfacing TSC algorithms by Dempster et al | It would be nice to add some of the state-of-art algorithms by Dempster et al (maintained primarily by Dempster = @angus924).
There are two sub-points here:
* end state of the interface/package architecture. The repositories by @angus924 are not full packages, only executable code. They could be turned into full packages, but would then require maintenance as full packages.
* the algorithms are/were released under GPL, a copyleft license. Some have already been added to `sktime`, and it looks like much of the code is identical, but `sktime` is permissive.
* For the algorithms already in `sktime`, we need to clarify the license status, e.g., where they forked by a non-owner from GPL repositories, in which case there is a license violation that we need to resolve, or copied by an owner, in which case the license was changed with authorization.
* For the algorithms already in `sktime`, we still need to clarify the code end state, e.g., merge back in the original package, which have separate bugfixes applied to the code, etc. We could look at primary location in `sktime`, or merge back in the author package, which would then need to be transformed into a proper python package
* For the algorithms not in `sktime`, the license issue interacts with the lack of a package that is importable. We can of course attach a GPL license to a subfolder, but then need to warn the user.
The algorithms:
- [ ] QUANT https://github.com/angus924/quant
- [ ] HYDRA https://github.com/angus924/hydra
- [ ] the rocket algorithms (already in `sktime`) - https://github.com/angus924/minirocket, https://github.com/angus924/rocket
- [ ] the multirocket algorithms (already in `sktime`), maintained by @ChangWeiTan, @ViktorvdValk - https://github.com/ChangWeiTan/MultiRocket | open | 2024-11-06T09:08:26Z | 2024-11-06T09:13:13Z | https://github.com/sktime/sktime/issues/7368 | [
"interfacing algorithms",
"module:classification",
"module:transformations",
"enhancement"
] | fkiraly | 2 |
elliotgao2/toapi | flask | 110 | 使用toapi run 时出现问题 | 
| closed | 2018-01-28T00:48:21Z | 2018-03-04T12:12:05Z | https://github.com/elliotgao2/toapi/issues/110 | [] | opooc | 1 |
fastapi/sqlmodel | fastapi | 437 | Upsert of 2 table with a one to many relation onto them: fastest implementation (single vs batch vs one commit vs ?) | ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I used the GitHub search to find a similar issue and didn't find it.
- [X] I searched the SQLModel documentation, with the integrated search.
- [X] I already searched in Google "How to X in SQLModel" and didn't find any information.
- [X] I already read and followed all the tutorial in the docs and didn't find an answer.
- [X] I already checked if it is not related to SQLModel but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to SQLModel but to [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy).
### Commit to Help
- [X] I commit to help with one of those options 👆
### Example Code
```python
class Lei(LeiBase, table=True):
"""
lei db. Start from csv taken here
https://www.gleif.org/fr/lei-data/lei-mapping/download-isin-to-lei-relationship-files/
Could be completed by direct gleif api calls
https://documenter.getpostman.com/view/7679680/SVYrrxuU?version=latest#3e013a79-b5f6-46a7-b9e9-299fde0b3a03
"""
id: Optional[int] = Field(default=None, primary_key=True)
created_at: datetime = Field(default_factory=datetime.utcnow)
isins: List['Isin'] = Relationship(back_populates='lei')
class Isin(SQLModel, table=True):
"""
isin db. Start from csv taken here
https://www.gleif.org/fr/lei-data/lei-mapping/download-isin-to-lei-relationship-files/
Could be completed by direct gleif api calls
https://documenter.getpostman.com/view/7679680/SVYrrxuU?version=latest#3e013a79-b5f6-46a7-b9e9-299fde0b3a03
"""
id: Optional[int] = Field(default=None, primary_key=True)
created_at: datetime = Field(default_factory=datetime.utcnow)
isin: str = Field(index=True)
lei_id: Optional[int] = Field(default=None, foreign_key='lei.id')
lei: Optional['Lei'] = Relationship(back_populates='isins')
def create_or_update_isins_leis(grouped_isins: Dict[str, List[str]], session: Session):
"""
Code suggestion, to be improved. isin_lei_models is not provided but
"""
for lei, isins in grouped_isins.items():
isin_lei_models(lei, isins, session)
session.commit()
def isin_lei_models(lei: str, isins: List[str], session: Session):
"""
Possible way to upsert lei and isins
"""
in_db_lei = session.exec(select(Lei).where(col(Lei.lei) == lei)).first() or Lei(lei=lei)
isins_to_insert = list(set(isins) - {isin.isin for isin in in_db_lei.isins})
for isin in isins_to_insert:
if in_db_isin := session.exec(select(Isin).where(col(Isin.isin) == isin)).first():
in_db_isin.lei = lei
session.add(in_db_isin)
return
session.add(Isin(isin=isin, lei=lei))
```
### Description
I simplified the code as much as to focus on my interrogation. I have a simple one to many relation between Lei and Isin. Imagine that I am given new {lei: isins} values and I have to either insert or update the lei. My question is simple: should I commit once at the end or in the for loop just above ? or in the lei loop? Should I batch if I have 10M leis? Is the answer the same in Create and Upsert mode?
I found this related stackoverflow thread https://stackoverflow.com/questions/24377193/best-way-to-update-millions-of-records-in-sql-table-with-foreign-keys, which describes well what I am trying to accomplish, but the solution seems far fetched (buy maybe it is not?)
I can experiment all suggested ideas if needs be :)
### Operating System
Linux
### Operating System Details
python:3.10-slim docker image
Requirements.in (then converted to txt via pip-compile)
```
bcrypt==3.2.0
certifi==2021.5.30
cryptography==3.4.8
dash==2.6.0
dash-auth==1.4.1
dash_core_components==2.0.0
dash_bootstrap_components==1.2.0
fastapi==0.80.0
jupyter==1.0.0
jupyter-dash==0.4.2
nltk==3.7
numpy==1.23.1
openpyxl==3.0.10
pandas==1.4.3
passlib==1.7.4
pillow==9.2.0
plotly==5.9.0
psycopg2==2.9.1
pydeps==1.10.22
python-dateutil==2.8.2
python-dotenv==0.19.0
python-editor==1.0.4
python-jose==3.3.0
python-multipart==0.0.5
requests==2.26.0
sqladmin==0.3.0
sqlalchemy==1.4.35 # Needed, otherwise relationship are not working. See https://github.com/tiangolo/sqlmodel/issues/315
sqlmodel==0.0.6
strsimpy==0.2.1
tqdm==4.64.0
uvicorn==0.18.2
```
### SQLModel Version
0.0.6
### Python Version
3.10
### Additional Context
In creation mode, a csv with 8 millions LEI/ISINS line takes less thant 30 minutes to insert. In upsert I gave up, it was too long with my naive implentation :) | open | 2022-09-05T11:52:41Z | 2022-09-06T13:21:54Z | https://github.com/fastapi/sqlmodel/issues/437 | [
"question"
] | tepelbaum | 2 |
mage-ai/mage-ai | data-science | 5,100 | [BUG] Circualr reference error when returning pyspark dataframe | ### Mage version
latest
### Describe the bug
When returning a pyspark dataframe in a data loader block, the error below is thrown:
[ERROR] LocalStorage.write_json_file: Circular reference detected
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
The above exception was the direct cause of the following exception:
Exception Traceback (most recent call last)
Exception: Failed to read json file: /home/src/mage_data/default_repo/pipelines/test_pipe/.variables/load_block/output_0/data.json
If the dataframe is converted to pandas before returning, it works as expected. Spark works as creating the pyspark dataframe and saving it as a databricks table is working as expected.
### To reproduce
This was tested on azure using an azure databricks cluster, openjdk17 and databricks-connect and by setting up the following environment variables on container creation through terraform: DATABRICKS_HOST, DATABRICKS_CLUSTER_ID, DATABRICKS_TOKEN, SPARK_REMOTE. The pipeline type was also changed to databricks.
### Expected behavior
_No response_
### Screenshots
_No response_
### Operating system
_No response_
### Additional context
_No response_ | open | 2024-05-21T20:47:05Z | 2024-05-23T17:57:33Z | https://github.com/mage-ai/mage-ai/issues/5100 | [
"bug"
] | MRehd | 0 |
donnemartin/system-design-primer | python | 1,031 | How to ace a systems design interview link is not working | open | 2024-12-15T13:17:25Z | 2025-01-12T13:45:47Z | https://github.com/donnemartin/system-design-primer/issues/1031 | [] | MahmoudNasser01 | 1 | |
tiangolo/uvicorn-gunicorn-fastapi-docker | pydantic | 20 | Pull access denied for ttiangolo/uvicorn-gunicorn-fastapi | H, I get this error when I try to build an image from this tool:
`pull access denied for ttiangolo/uvicorn-gunicorn-fastapi, repository does not exist or may require 'docker login': denied: requested access to the resource is denied`
How can I get past this? It doesn't happen with other repos. | closed | 2019-10-24T13:59:34Z | 2020-04-10T17:15:58Z | https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker/issues/20 | [] | PyDataBlog | 3 |
dynaconf/dynaconf | flask | 492 | Checking key for being deleted too early | Checking key for being deleted too early prevents it from refreshing if the a key was delete and then set again:
```
if key in self._deleted:
return default
```
```
def get(
self,
key,
default=None,
cast=None,
fresh=False,
dotted_lookup=True,
parent=None,
):
"""
Get a value from settings store, this is the prefered way to access::
>>> from dynaconf import settings
>>> settings.get('KEY')
:param key: The name of the setting value, will always be upper case
:param default: In case of not found it will be returned
:param cast: Should cast in to @int, @float, @bool or @json ?
:param fresh: Should reload from loaders store before access?
:param dotted_lookup: Should perform dotted-path lookup?
:param parent: Is there a pre-loaded parent in a nested data?
:return: The value if found, default or None
"""
if "." in key and dotted_lookup:
return self._dotted_get(
dotted_key=key,
default=default,
cast=cast,
fresh=fresh,
parent=parent,
)
key = upperfy(key)
if key in self._deleted:
return default
if (
fresh
or self._fresh
or key in getattr(self, "FRESH_VARS_FOR_DYNACONF", ())
) and key not in dir(default_settings):
self.unset(key)
self.execute_loaders(key=key)
``` | closed | 2020-12-18T13:26:09Z | 2021-03-01T18:35:01Z | https://github.com/dynaconf/dynaconf/issues/492 | [
"bug"
] | dmugtasimov | 2 |
wkentaro/labelme | deep-learning | 389 | Propose to exclude imageData from JSON annotation file. And use imagePath only for information purposes. | Now if annotation is created in JSON file, image data also is stored in JSON file. Is there any arguments for this decision? I see only disadvantages of this:
1) sometimes it seems to be convenient to handle annotation files manually: create it for new image on the basis of one or may be several other files and then update using LabelMe editor. But now you need to do something with ImageData. Yes, I know about "keep previous" mode, but sometimes handcrafting is more convenient.
2) writing converter to LaberlMe annotation format becomes much more sofisticated.
3) JSON files have huge size (compared with normal json), it makes more time to copy them, more place to store, more difficult to open in text editor.
4) Storing the same information in to places always increase risk of misalignment errors. You can be sure that you annotate JPG file you have selected but you actually annotate only copy stored in json. Sometimes it can become not the same. But other tools that use your annotation (NNets etc) uses JPG , and it will be hard to catch the mistake.
Last point also refers to imagePath field. Actually then you load JPG image (loadFile function) JPG and JSON files are paired by filename. But then imagePath is read from JSON. This ambigulity also can cause problems. For instance, I expect that if I rename both JPG and JSON, it will be OK, but suddenly it appeares, that I also have to edit every JSON file.
I propose
1) exclude imageData from JSON at all
2) either exclude imagePath as well, or keep it just for information purposes (for human readiing in JSON), but don't use it in a program.
What do you think about it? Modification seems not to be difficult, I'm going to do it. | closed | 2019-05-06T11:28:25Z | 2020-01-27T01:27:28Z | https://github.com/wkentaro/labelme/issues/389 | [] | IlyaOvodov | 9 |
onnx/onnx | machine-learning | 6,289 | [Feature request] Implement the reference runtime with the array api | ### System information
_No response_
### What is the problem that this feature solves?
Enable multi backend support in the reference runtime.
### Alternatives considered
_No response_
### Describe the feature
https://github.com/data-apis/array-api-compat
### Will this influence the current api (Y/N)?
_No response_
### Feature Area
_No response_
### Are you willing to contribute it (Y/N)
Yes
### Notes
_No response_ | open | 2024-08-08T22:33:44Z | 2025-01-01T17:46:43Z | https://github.com/onnx/onnx/issues/6289 | [
"topic: enhancement",
"module: reference implementation",
"contributions welcome"
] | justinchuby | 1 |
marshmallow-code/apispec | rest-api | 76 | Getting Assert when using many=True | Getting this assert when I use a Marshmallow schema with many=True
> Schemas with many=True are only supported for 'json' location (aka 'in: body')
apispec/ext/marshmallow/swagger.py ~230.
I am using the following decorator:
`@use_kwargs(MySchema(many=True), locations=('json',))`
| closed | 2016-05-20T15:37:38Z | 2016-05-20T18:43:44Z | https://github.com/marshmallow-code/apispec/issues/76 | [] | incognick | 1 |
ScrapeGraphAI/Scrapegraph-ai | machine-learning | 777 | use in windows 10 error : UnboundLocalError: local variable 'browser' referenced before assignment | playwright install :
use :
error :
**UnboundLocalError: local variable 'browser' referenced before assignment**
detail is :
``` UnboundLocalError Traceback (most recent call last)
Cell In[4], line 27
18 # ************************************************
19 # Create the SmartScraperGraph instance and run it
20 # ************************************************
21 smart_scraper_graph = SmartScraperGraph(
22 prompt="Find some information about what does the company do, the name and a contact email.",
23 source="https://scrapegraphai.com/",
24 config=graph_config
25 )
---> 27 result = smart_scraper_graph.run()
28 print(result)
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\graphs\smart_scraper_graph.py:212, in SmartScraperGraph.run(self)
204 """
205 Executes the scraping process and returns the answer to the prompt.
206
207 Returns:
208 str: The answer to the prompt.
209 """
211 inputs = {"user_prompt": self.prompt, self.input_key: self.source}
--> 212 self.final_state, self.execution_info = self.graph.execute(inputs)
214 return self.final_state.get("answer", "No answer found.")
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\graphs\base_graph.py:284, in BaseGraph.execute(self, initial_state)
282 return (result["_state"], [])
283 else:
--> 284 return self._execute_standard(initial_state)
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\graphs\base_graph.py:198, in BaseGraph._execute_standard(self, initial_state)
185 graph_execution_time = time.time() - start_time
186 log_graph_execution(
187 graph_name=self.graph_name,
188 source=source,
(...)
196 exception=str(e)
197 )
--> 198 raise e
199 node_exec_time = time.time() - curr_time
200 total_exec_time += node_exec_time
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\graphs\base_graph.py:182, in BaseGraph._execute_standard(self, initial_state)
180 with self.callback_manager.exclusive_get_callback(llm_model, llm_model_name) as cb:
181 try:
--> 182 result = current_node.execute(state)
183 except Exception as e:
184 error_node = current_node.node_name
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\nodes\fetch_node.py:130, in FetchNode.execute(self, state)
128 return self.handle_local_source(state, source)
129 else:
--> 130 return self.handle_web_source(state, source)
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\nodes\fetch_node.py:305, in FetchNode.handle_web_source(self, state, source)
303 else:
304 loader = ChromiumLoader([source], headless=self.headless, **loader_kwargs)
--> 305 document = loader.load()
307 if not document or not document[0].page_content.strip():
308 raise ValueError("""No HTML body content found in
309 the document fetched by ChromiumLoader.""")
File D:\softs\anaconda3\envs\flux\lib\site-packages\langchain_core\document_loaders\base.py:31, in BaseLoader.load(self)
29 def load(self) -> list[Document]:
30 """Load data into Document objects."""
---> 31 return list(self.lazy_load())
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\docloaders\chromium.py:192, in ChromiumLoader.lazy_load(self)
189 scraping_fn = getattr(self, f"ascrape_{self.backend}")
191 for url in self.urls:
--> 192 html_content = asyncio.run(scraping_fn(url))
193 metadata = {"source": url}
194 yield Document(page_content=html_content, metadata=metadata)
File D:\softs\anaconda3\envs\flux\lib\site-packages\nest_asyncio.py:30, in _patch_asyncio.<locals>.run(main, debug)
28 task = asyncio.ensure_future(main)
29 try:
---> 30 return loop.run_until_complete(task)
31 finally:
32 if not task.done():
File D:\softs\anaconda3\envs\flux\lib\site-packages\nest_asyncio.py:98, in _patch_loop.<locals>.run_until_complete(self, future)
95 if not f.done():
96 raise RuntimeError(
97 'Event loop stopped before Future completed.')
---> 98 return f.result()
File D:\softs\anaconda3\envs\flux\lib\asyncio\futures.py:201, in Future.result(self)
199 self.__log_traceback = False
200 if self._exception is not None:
--> 201 raise self._exception.with_traceback(self._exception_tb)
202 return self._result
File D:\softs\anaconda3\envs\flux\lib\asyncio\tasks.py:232, in Task.__step(***failed resolving arguments***)
228 try:
229 if exc is None:
230 # We use the `send` method directly, because coroutines
231 # don't have `__iter__` and `__next__` methods.
--> 232 result = coro.send(None)
233 else:
234 result = coro.throw(exc)
File D:\softs\anaconda3\envs\flux\lib\site-packages\scrapegraphai\docloaders\chromium.py:136, in ChromiumLoader.ascrape_playwright(self, url)
134 results = f"Error: Network error after {self.RETRY_LIMIT} attempts - {e}"
135 finally:
--> 136 await browser.close()
138 return results
```
pelase help me
| closed | 2024-10-29T14:05:21Z | 2025-01-06T04:19:58Z | https://github.com/ScrapeGraphAI/Scrapegraph-ai/issues/777 | [] | 1272870698 | 11 |
ultralytics/ultralytics | machine-learning | 19,055 | OBB does not return boxes, despite producing an image and bounding box visualizations | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
Hi,
I'm encountering an issue where the YOLO models (`yolo11n-obb.pt`, `yolo11m-obb.pt`) don't return the bounding box values, even though the output includes the image with bounding boxes visually displayed.
I have searched in the documentation and I found the same issue in this discussion:
[https://github.com/orgs/ultralytics/discussions/8462#discussioncomment-10200090](https://github.com/orgs/ultralytics/discussions/8462#discussioncomment-10200090)
The proposed solution was to decrease the confidence score, but I don't think that’s the issue because when I run the script, the output image correctly shows the bounding boxes and labels. However, the resulting output does not contain any values for the bounding box coordinates in the "boxes" field.
- **My** code:
``` bash
from ultralytics import YOLO
model = YOLO("yolo11n-obb.pt")
frame = 'yt_videos/image.png'
results = model(frame, save=True)
print(results)
```
**- Result Output**
`Speed: 3.3ms preprocess, 81.1ms inference, 1.9ms postprocess per image at shape (1, 3, 576, 1024)
Results saved to runs/obb/track4
[ultralytics.engine.results.Results object with attributes:
boxes: None
keypoints: None
masks: None
names: {0: 'plane', 1: 'ship', 2: 'storage tank', 3: 'baseball diamond', 4: 'tennis court', 5: 'basketball court', 6: 'ground track field', 7: 'harbor', 8: 'bridge', 9: 'large vehicle', 10: 'small vehicle', 11: 'helicopter', 12: 'roundabout', 13: 'soccer ball field', 14: 'swimming pool'}
obb: ultralytics.engine.results.OBB object
orig_img: array([[[137, 136, 135],
[137, 136, 135],
[137, 136, 135],
...,
[177, 176, 175],
[177, 176, 175],
[177, 176, 175]],
[[137, 136, 135],
[137, 136, 135],
[137, 136, 135],
...,
[177, 176, 175],
[177, 176, 175],
[177, 176, 175]],
[[137, 136, 135],
[137, 136, 135],
[137, 136, 135],
...,
[177, 176, 175],
[177, 176, 175],
[177, 176, 175]],
...,
[[ 87, 92, 95],
[ 87, 92, 95],
[ 87, 92, 95],
...,
[ 45, 75, 73],
[ 46, 76, 74],
[ 44, 75, 73]],
[[ 87, 92, 95],
[ 87, 92, 95],
[ 87, 92, 95],
...,
[ 49, 77, 75],
[ 47, 76, 74],
[ 44, 74, 72]],
[[ 91, 96, 99],
[ 91, 96, 99],
[ 93, 98, 101],
...,
[ 50, 77, 75],
[ 46, 75, 73],
[ 42, 71, 70]]], dtype=uint8)
orig_shape: (1080, 1920)`
Am I missing something?
I would appreciate any help,
Thank you
### Additional
_No response_ | closed | 2025-02-04T05:51:50Z | 2025-02-17T05:28:55Z | https://github.com/ultralytics/ultralytics/issues/19055 | [
"question",
"OBB"
] | FabianEP11 | 8 |
PokeAPI/pokeapi | graphql | 217 | App Showcase section | We get a tonne of new apps being developed with PokéAPI. We should build a section that helps to promote them, especially ones that are new and from first-time programmers.
This doesn't need to be anything fancy, but a simple form for people to submit their projects with a URL link and an image, with a simple way of approving apps, is all it takes.
Who would like to work on something like this?
| closed | 2016-06-24T15:55:09Z | 2017-06-12T12:51:54Z | https://github.com/PokeAPI/pokeapi/issues/217 | [
"enhancement"
] | phalt | 3 |
ploomber/ploomber | jupyter | 758 | Do not override user settings when running cloud set-key tests | Some of the cloud tests call the set-key command to verify the functionality. However, this causes the user's existing key to be overridden after running the tests.
We should add a `pytest.fixture` that backs up the user settings and it restores them after the test runs. Essentially, we need to backup the file that stores the key.
https://github.com/ploomber/ploomber/blob/9e11fcbcdf763d6f3111e8254dc327a56306971e/tests/cli/test_cloud.py#L125 | closed | 2022-05-12T22:11:22Z | 2022-05-19T02:42:57Z | https://github.com/ploomber/ploomber/issues/758 | [
"good first issue"
] | edublancas | 2 |
ets-labs/python-dependency-injector | asyncio | 816 | Cannot build using GCC v13 & v14 | Hello, I cannot install dependency injector having versions 13 and 14 of gcc on my system. Could you provide any information which versions of gcc are supported? | open | 2024-09-04T21:01:40Z | 2024-12-08T10:39:14Z | https://github.com/ets-labs/python-dependency-injector/issues/816 | [] | fedya-eremin | 1 |
gradio-app/gradio | data-visualization | 10,211 | Gallery preview row unable to display large number of images (overflow images are hidden and cannot be selected) | ### Describe the bug
<img width="1439" alt="image" src="https://github.com/user-attachments/assets/7e8e1de0-1bbf-477c-afb6-af5a62fe269f" />
<img width="1439" alt="image" src="https://github.com/user-attachments/assets/216ad393-772c-4875-9385-cf7ba57e2efe" />
### Have you searched existing issues? 🔎
- [X] I have searched and found no existing issues
### Fix the bug
```python
css = """
.thumbnails {
justify-content: space-between !important;
}
"""
with gr.Blocks(css=css) as demo:
```
### System Info
```shell
gradio@5.9.0
```
### Severity
I can work around it | open | 2024-12-16T15:14:14Z | 2024-12-16T16:50:42Z | https://github.com/gradio-app/gradio/issues/10211 | [
"bug"
] | xiaomofa | 0 |
LibreTranslate/LibreTranslate | api | 375 | Bug after latest update: cannot start | On a `linux/amd64` machine
docker-compose.yaml:
```yaml
services:
libretranslate:
image: libretranslate/libretranslate
container_name: libretranslate
hostname: libretranslate
environment:
# https://github.com/LibreTranslate/LibreTranslate#arguments
# https://github.com/LibreTranslate/LibreTranslate#manage-api-keys
LT_HOST: 0.0.0.0 # 127.0.0.1
LT_PORT: 5000
LT_FRONTEND_LANGUAGE_SOURCE: "en"
LT_FRONTEND_LANGUAGE_TARGET: en
# LT_FRONTEND_TIMEOUT: 500
LT_API_KEYS: "true"
LT_API_KEYS_DB_PATH: /libretranslate_db.db
LT_THREADS: 2 # 4
LT_REQUIRE_API_KEY_ORIGIN: "true"
LT_REQ_LIMIT: 20 # no limit
LT_BATCH_LIMIT: 2 # no limit
volumes:
- ${DOCKER}/libretranslate_db/libretranslate_db.db:/libretranslate_db.db
- ${DOCKER}/libretranslate/.local:/home/libretranslate/.local
ports:
5000:5000
```
Logs:
```sh
$ docker logs libretranslate
(URLError(TimeoutError(110, 'Connection timed out')),)
(URLError(TimeoutError(110, 'Connection timed out')),)
(URLError(TimeoutError(110, 'Connection timed out')),)
(URLError(TimeoutError(110, 'Connection timed out')),)
Updating language models
Found 58 models
Downloading Arabic → English (1.0) ...
Downloading Azerbaijani → English (1.5) ...
Downloading Catalan → English (1.7) ...
Downloading Chinese → English (1.7) ...
Downloading Czech → English (1.5) ...
Downloading Danish → English (1.3) ...
Downloading Dutch → English (1.4) ...
Downloading English → Arabic (1.0) ...
Downloading English → Azerbaijani (1.5) ...
Downloading English → Catalan (1.7) ...
Downloading English → Chinese (1.7) ...
Downloading English → Czech (1.5) ...
Downloading English → Danish (1.3) ...
Downloading English → Dutch (1.4) ...
Downloading English → Esperanto (1.5) ...
Downloading English → Finnish (1.5) ...
Downloading English → French (1.0) ...
Downloading English → German (1.0) ...
Downloading English → Greek (1.5) ...
Downloading English → Hebrew (1.5) ...
Downloading English → Hindi (1.1) ...
Downloading English → Hungarian (1.5) ...
Downloading English → Indonesian (1.2) ...
Downloading English → Irish (1.1) ...
Downloading English → Italian (1.0) ...
Downloading English → Japanese (1.1) ...
Downloading English → Korean (1.1) ...
Downloading English → Persian (1.5) ...
Downloading English → Polish (1.1) ...
Downloading English → Portuguese (1.0) ...
Downloading English → Russian (1.7) ...
Downloading English → Slovak (1.5) ...
Downloading English → Spanish (1.0) ...
Downloading English → Swedish (1.5) ...
Downloading English → Turkish (1.5) ...
Downloading English → Ukranian (1.4) ...
Downloading Esperanto → English (1.5) ...
Downloading Finnish → English (1.5) ...
Downloading French → English (1.0) ...
Downloading German → English (1.0) ...
Downloading Greek → English (1.5) ...
Downloading Hebrew → English (1.5) ...
Downloading Hindi → English (1.1) ...
Downloading Hungarian → English (1.5) ...
Downloading Indonesian → English (1.2) ...
Downloading Irish → English (1.1) ...
Downloading Italian → English (1.0) ...
Downloading Japanese → English (1.1) ...
Downloading Korean → English (1.1) ...
Downloading Persian → English (1.5) ...
Downloading Polish → English (1.1) ...
Downloading Portuguese → English (1.0) ...
Downloading Russian → English (1.0) ...
Downloading Slovak → English (1.5) ...
Downloading Spanish → English (1.0) ...
Downloading Swedish → English (1.5) ...
Downloading Turkish → English (1.5) ...
Downloading Ukranian → English (1.4) ...
Cannot update models (normal if you're offline): name 'app' is not defined
Traceback (most recent call last):
File "./venv/bin/libretranslate", line 33, in <module>
sys.exit(load_entry_point('libretranslate==1.3.8', 'console_scripts', 'libretranslate')())
File "/app/venv/lib/python3.8/site-packages/libretranslate/main.py", line 176, in main
app = create_app(args)
File "/app/venv/lib/python3.8/site-packages/libretranslate/app.py", line 163, in create_app
limiter = Limiter(
File "/app/venv/lib/python3.8/site-packages/flask_limiter/extension.py", line 272, in __init__
self.init_app(app)
File "/app/venv/lib/python3.8/site-packages/flask_limiter/extension.py", line 278, in init_app
config = app.config
AttributeError: 'Blueprint' object has no attribute 'config'
``` | closed | 2023-01-01T17:01:58Z | 2023-01-01T18:41:25Z | https://github.com/LibreTranslate/LibreTranslate/issues/375 | [
"bug"
] | schklom | 2 |
deeppavlov/DeepPavlov | nlp | 1,385 | Create Architecture Diagram of DeepPavlov Library | closed | 2021-01-27T09:16:05Z | 2023-07-06T12:07:44Z | https://github.com/deeppavlov/DeepPavlov/issues/1385 | [] | danielkornev | 4 | |
ccxt/ccxt | api | 24,813 | fetchOHLCV issue with 'since' ? | ### Operating System
Windows
### Programming Languages
JavaScript
### CCXT Version
last
### Description
Hi,
I contact you because I think I've found a bug with the Alpaca integration, I try to fetch the candles, then I use the following code:
```
const symbol = 'BTC/USDT';
const timeframe = '1d';
const since = 1701867696765; // 6 December 2023 13:01:36.765
const limit = 200;
return await globalThis.exchange.fetchOHLCV(symbol, timeframe, since, limit);
```
But I got strange candles timestamps:
**The first:** [1724648400000, 18.135275524, 159.35, 18.135275524, 158.687, 0.000510664] where 1724648400000 is Monday, 26 August 2024 05:00:00
**The last:** [1736402400000, 194.2875, 194.825, 189.022, 190.35, 33.749763368] where 1736402400000 is Thursday, 9 January 2025 06:00:00
So, the candles are not from "6 December 2023 13:01:36.765" and "6 December 2023 13:01:36.765" + 200 days.
Right ?
### Code
```
const symbol = 'BTC/USDT';
const timeframe = '1d';
const since = 1701867696765; // 6 December 2023 13:01:36.765
const limit = 200;
return await globalThis.exchange.fetchOHLCV(symbol, timeframe, since, limit);
```
BTW, I have tested with both Alpaca and Binance, and I got the same problem. | closed | 2025-01-09T13:23:50Z | 2025-01-11T17:56:40Z | https://github.com/ccxt/ccxt/issues/24813 | [] | vd3d | 4 |
dpgaspar/Flask-AppBuilder | flask | 1,601 | Unable to Expose POST method in BaseView | If you'd like to report a bug in Flask-Appbuilder, fill out the template below. Provide
any extra information that may be useful
### Environment
Flask-Appbuilder version: 3.2.1
pip freeze output:
### Describe the expected results
Tell us what should happen.
```python
Paste a minimal example that causes the problem.
```
#Bulk Email to Lead
class CustomView(BaseView):
default_view = "bulkemail"
@expose("/bulkemail/",methods="POST")
@has_access
def bulkemail(self):
return self.render_template("bulkemail.html")
appbuilder.add_view(CustomView(), "Bulk Email",category="Utilities")
### Describe the actual results
Tell us what happens instead.
I want to send the html template form data to the Baseview.
```pytb
Paste the full traceback if there was an exception.
```
**The method is not allowed for the requested URL.**
### Steps to reproduce
| closed | 2021-04-06T16:14:31Z | 2021-07-21T03:21:44Z | https://github.com/dpgaspar/Flask-AppBuilder/issues/1601 | [
"stale"
] | naresnayak | 2 |
dgtlmoon/changedetection.io | web-scraping | 1,920 | [feature] Wachete import / export as Wachete alternative | It's possible to export your waches from Wachete, so it should be possible to import their XLS file directly into changedetection.io
The format looks fairly clean, I think it should be no problems. We can even try to keep their UUIDs
[wachete-export-example.xlsx](https://github.com/dgtlmoon/changedetection.io/files/13217931/wachete-export-example.xlsx)
And then changedetection.io becomes another fantastic alternative :)
actually the UI should offer to import any kind of XSLX file with configurable columns | closed | 2023-10-31T14:57:54Z | 2023-11-01T18:23:37Z | https://github.com/dgtlmoon/changedetection.io/issues/1920 | [
"enhancement"
] | dgtlmoon | 2 |
graphql-python/graphene-mongo | graphql | 40 | unable to install via pip - Some packages may not be found! | Error:
Download error on https://pypi.python.org/simple/pytest-runner/: EOF occurred in violation of protocol (_ssl.c:590) -- Some packages may not be found!
Couldn't find index page for 'pytest-runner' (maybe misspelled?)
I resolved the problem by:
1. I have to go to https://pypi.org/simple/pytest-runner/
2. download pytest_runner-4.2-py2.py3-none-any.whl
3. run pip install ./pytest_runner-4.2-py2.py3-none-any.whl
4. pip install graphene-mongo | closed | 2018-07-13T09:07:33Z | 2018-08-30T07:47:13Z | https://github.com/graphql-python/graphene-mongo/issues/40 | [
"work in progress"
] | ahmad88me | 2 |
google/seq2seq | tensorflow | 361 | Error while executing | I am executing the following command as a batch file on windows10
`python -m bin.train --config_paths="C:\Users\seq2seqten\data\Configs\basicconfig" --model_params "vocab_source: C:\Users\seq2seqten\data\knowdata\" --input_pipeline_train "class: ParallelTextInputPipeline params: source_files: - C:\Users\seq2seqten\data\knowdata\knoworg.bin.txt target_files: - C:\Users\seq2seqten\data\knowdata\knowans.bin.txt --batch_size 32 --train_steps 1000000 --output_dir C:\Users\seq2seqten\data\knowdata\nmt_tutorial.txt`
I am getting the error:```
self.get_mark())
yaml.scanner.ScannerError: mapping values are not allowed here
in "<unicode string>", line 1, column 106:
... ta" --input_pipeline_train class:
^
```
Can you please let me know what I am missing | open | 2019-09-14T16:16:44Z | 2019-09-14T16:17:07Z | https://github.com/google/seq2seq/issues/361 | [] | newmluser | 0 |
deepfakes/faceswap | deep-learning | 904 | Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR when Extracting | Error on extrace:
```
2019-10-14 00:35:36.958535: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2019-10-14 00:35:36.964129: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
10/14/2019 00:35:39 ERROR Got Exception on main handler:
Traceback (most recent call last):
File "/home/tianze/face/lib/cli.py", line 128, in execute_script
process.process()
File "/home/tianze/face/scripts/extract.py", line 61, in process
self.run_extraction()
File "/home/tianze/face/scripts/extract.py", line 187, in run_extraction
self.extractor.launch()
File "/home/tianze/face/plugins/extract/pipeline.py", line 180, in launch
self._launch_aligner()
File "/home/tianze/face/plugins/extract/pipeline.py", line 326, in _launch_aligner
self._aligner.initialize(**kwargs)
File "/home/tianze/face/plugins/extract/_base.py", line 330, in initialize
self.init_model()
File "/home/tianze/face/plugins/extract/align/fan.py", line 41, in init_model
self.model.predict(placeholder)
File "/home/tianze/face/lib/model/session.py", line 66, in predict
return self._model.predict(feed, batch_size=batch_size)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/engine/training.py", line 1169, in predict
steps=steps)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/engine/training_arrays.py", line 294, in predict_loop
batch_outs = f(ins_batch)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
return self._call(inputs)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
fetched = self._callable_fn(*array_vals)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1458, in __call__
run_metadata_ptr)
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv2d_1/convolution}}]]
[[conv2d_56/BiasAdd/_6051]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
To Reproduce
Extract from mp4
Expected behavior
Extraction
Log:
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_card_most_free DEBUG Active GPU Card with most free VRAM: {'device': 'Tesla V100-PCIE-32GB', 'free': 32061.625, 'total': 32480.5, 'card_id': 1}
10/14/2019 00:35:18 MainProcess MainThread pipeline _set_parallel_processing VERBOSE Tesla V100-PCIE-32GB - 32061MB free of 32480MB
10/14/2019 00:35:18 MainProcess MainThread gpu_stats __init__ DEBUG Initializing GPUStats
10/14/2019 00:35:18 MainProcess MainThread gpu_stats initialize DEBUG OS is not macOS. Using pynvml
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_device_count DEBUG GPU Device count: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_active_devices DEBUG Active GPU Devices: [0, 1, 2]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_handles DEBUG GPU Handles found: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_driver DEBUG GPU Driver: 410.104
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_devices DEBUG GPU Devices: ['Tesla V100-PCIE-32GB', 'Tesla V100-PCIE-32GB', 'Tesla V100-PCIE-32GB']
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_vram DEBUG GPU VRAM: [32480.5, 32480.5, 32480.5]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats __init__ DEBUG Initialized GPUStats
10/14/2019 00:35:18 MainProcess MainThread gpu_stats initialize DEBUG OS is not macOS. Using pynvml
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_device_count DEBUG GPU Device count: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_active_devices DEBUG Active GPU Devices: [0, 1, 2]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_handles DEBUG GPU Handles found: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_free DEBUG GPU VRAM free: [1143.625, 32061.625, 32061.625]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats initialize DEBUG OS is not macOS. Using pynvml
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_device_count DEBUG GPU Device count: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_active_devices DEBUG Active GPU Devices: [0, 1, 2]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_handles DEBUG GPU Handles found: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_free DEBUG GPU VRAM free: [1143.625, 32061.625, 32061.625]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats initialize DEBUG OS is not macOS. Using pynvml
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_device_count DEBUG GPU Device count: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_active_devices DEBUG Active GPU Devices: [0, 1, 2]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_handles DEBUG GPU Handles found: 3
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_free DEBUG GPU VRAM free: [1143.625, 32061.625, 32061.625]
10/14/2019 00:35:18 MainProcess MainThread gpu_stats get_card_most_free DEBUG Active GPU Card with most free VRAM: {'device': 'Tesla V100-PCIE-32GB', 'free': 32061.625, 'total': 32480.5, 'card_id': 1}
10/14/2019 00:35:18 MainProcess MainThread pipeline _set_extractor_batchsize DEBUG Plugin requirements within threshold: (plugin_required: 8192MB, vram_free: 32061MB)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager adding: (name: 'extract_detect_in', maxsize: 64)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager added: (name: 'extract_detect_in')
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager getting: 'extract_detect_in'
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager got: 'extract_detect_in'
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager adding: (name: 'extract_align_in', maxsize: 32)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager added: (name: 'extract_align_in')
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager getting: 'extract_align_in'
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager got: 'extract_align_in'
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager adding: (name: 'extract_align_out', maxsize: 32)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager added: (name: 'extract_align_out')
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager getting: 'extract_align_out'
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager got: 'extract_align_out'
10/14/2019 00:35:18 MainProcess MainThread pipeline _add_queues DEBUG Queues: {'extract_align_out': <queue.Queue object at 0x7f9b05530048>, 'extract_align_in': <queue.Queue object at 0x7f9b05525f28>, 'extract_detect_in': <queue.Queue object at 0x7f9b05525e10>}
10/14/2019 00:35:18 MainProcess MainThread pipeline __init__ DEBUG Initialized Extractor
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager getting: 'extract_save'
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager adding: (name: 'extract_save', maxsize: 0)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager added: (name: 'extract_save')
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager got: 'extract_save'
10/14/2019 00:35:18 MainProcess MainThread extract __init__ DEBUG Initialized Extract
10/14/2019 00:35:18 MainProcess MainThread extract process INFO Starting, this may take a while...
10/14/2019 00:35:18 MainProcess MainThread extract threaded_io DEBUG Threading task: (Task: 'load')
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initializing MultiThread: (target: 'load_images', thread_count: 1)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initialized MultiThread: 'load_images'
10/14/2019 00:35:18 MainProcess MainThread multithreading start DEBUG Starting thread(s): 'load_images'
10/14/2019 00:35:18 MainProcess MainThread multithreading start DEBUG Starting thread 1 of 1: 'load_images_0'
10/14/2019 00:35:18 MainProcess load_images_0 extract load_images DEBUG Load Images: Start
10/14/2019 00:35:18 MainProcess load_images_0 fsmedia load_video_frames DEBUG Input is video. Capturing frames
10/14/2019 00:35:18 MainProcess MainThread multithreading start DEBUG Started all threads 'load_images': 1
10/14/2019 00:35:18 MainProcess MainThread extract threaded_io DEBUG Threading task: (Task: 'save')
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initializing MultiThread: (target: 'save_faces', thread_count: 1)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initialized MultiThread: 'save_faces'
10/14/2019 00:35:18 MainProcess MainThread multithreading start DEBUG Starting thread(s): 'save_faces'
10/14/2019 00:35:18 MainProcess MainThread multithreading start DEBUG Starting thread 1 of 1: 'save_faces_0'
10/14/2019 00:35:18 MainProcess save_faces_0 extract save_faces DEBUG Save Faces: Start
10/14/2019 00:35:18 MainProcess MainThread multithreading start DEBUG Started all threads 'save_faces': 1
10/14/2019 00:35:18 MainProcess MainThread extract process_item_count DEBUG Items already processed: 0
10/14/2019 00:35:18 MainProcess MainThread extract process_item_count DEBUG Items to be Processed: 335
10/14/2019 00:35:18 MainProcess MainThread pipeline _launch_aligner DEBUG Launching Aligner
10/14/2019 00:35:18 MainProcess MainThread _base initialize DEBUG initialize Align: (args: (), kwargs: {'out_queue': <queue.Queue object at 0x7f9b05530048>, 'in_queue': <queue.Queue object at 0x7f9b05525f28>})
10/14/2019 00:35:18 MainProcess MainThread _base initialize INFO Initializing FAN Aligner...
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager getting: 'align_predict'
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager adding: (name: 'align_predict', maxsize: 1)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager added: (name: 'align_predict')
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager got: 'align_predict'
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager getting: 'align_post'
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager adding: (name: 'align_post', maxsize: 1)
10/14/2019 00:35:18 MainProcess MainThread queue_manager add_queue DEBUG QueueManager added: (name: 'align_post')
10/14/2019 00:35:18 MainProcess MainThread queue_manager get_queue DEBUG QueueManager got: 'align_post'
10/14/2019 00:35:18 MainProcess MainThread _base _compile_threads DEBUG Compiling align threads
10/14/2019 00:35:18 MainProcess MainThread _base _add_thread DEBUG Adding thread: (name: align_input, function: <bound method Align.process_input of <plugins.extract.align.fan.Align object at 0x7f9b055136a0>>, in_queue: <queue.Queue object at 0x7f9b05525f28>, out_queue: <queue.Queue object at 0x7f9b05525d68>)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initializing MultiThread: (target: 'align_input', thread_count: 1)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initialized MultiThread: 'align_input'
10/14/2019 00:35:18 MainProcess MainThread _base _add_thread DEBUG Added thread: align_input
10/14/2019 00:35:18 MainProcess MainThread _base _add_thread DEBUG Adding thread: (name: align_predict, function: <bound method Aligner._predict of <plugins.extract.align.fan.Align object at 0x7f9b055136a0>>, in_queue: <queue.Queue object at 0x7f9b05525d68>, out_queue: <queue.Queue object at 0x7f9b05525b38>)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initializing MultiThread: (target: 'align_predict', thread_count: 1)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initialized MultiThread: 'align_predict'
10/14/2019 00:35:18 MainProcess MainThread _base _add_thread DEBUG Added thread: align_predict
10/14/2019 00:35:18 MainProcess MainThread _base _add_thread DEBUG Adding thread: (name: align_output, function: <bound method Align.process_output of <plugins.extract.align.fan.Align object at 0x7f9b055136a0>>, in_queue: <queue.Queue object at 0x7f9b05525b38>, out_queue: <queue.Queue object at 0x7f9b05530048>)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initializing MultiThread: (target: 'align_output', thread_count: 1)
10/14/2019 00:35:18 MainProcess MainThread multithreading __init__ DEBUG Initialized MultiThread: 'align_output'
10/14/2019 00:35:18 MainProcess MainThread _base _add_thread DEBUG Added thread: align_output
10/14/2019 00:35:18 MainProcess MainThread _base _compile_threads DEBUG Compiled align threads: [<lib.multithreading.MultiThread object at 0x7f9b05525978>, <lib.multithreading.MultiThread object at 0x7f9b05530240>, <lib.multithreading.MultiThread object at 0x7f9b05530278>]
10/14/2019 00:35:18 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/face/lib/model/session.py:110: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n
10/14/2019 00:35:18 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/face/lib/model/session.py:113: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n
10/14/2019 00:35:20 MainProcess MainThread session _set_session DEBUG Creating tf.session: (graph: <tensorflow.python.framework.ops.Graph object at 0x7f9b05530588>, session: <tensorflow.python.client.session.Session object at 0x7f9b05530748>, config: )
10/14/2019 00:35:20 MainProcess MainThread session load_model VERBOSE Initializing plugin model: FAN
10/14/2019 00:35:20 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n
10/14/2019 00:35:20 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n
10/14/2019 00:35:20 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n
10/14/2019 00:35:21 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:3980: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n
10/14/2019 00:35:21 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n
10/14/2019 00:35:33 MainProcess MainThread deprecation_wrapper __getattr__ WARNING From /home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n
Traceback (most recent call last):
File "/home/tianze/face/lib/cli.py", line 128, in execute_script
process.process()
File "/home/tianze/face/scripts/extract.py", line 61, in process
self.run_extraction()
File "/home/tianze/face/scripts/extract.py", line 187, in run_extraction
self.extractor.launch()
File "/home/tianze/face/plugins/extract/pipeline.py", line 180, in launch
self._launch_aligner()
File "/home/tianze/face/plugins/extract/pipeline.py", line 326, in _launch_aligner
self._aligner.initialize(**kwargs)
File "/home/tianze/face/plugins/extract/_base.py", line 330, in initialize
self.init_model()
File "/home/tianze/face/plugins/extract/align/fan.py", line 41, in init_model
self.model.predict(placeholder)
File "/home/tianze/face/lib/model/session.py", line 66, in predict
return self._model.predict(feed, batch_size=batch_size)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/engine/training.py", line 1169, in predict
steps=steps)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/engine/training_arrays.py", line 294, in predict_loop
batch_outs = f(ins_batch)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
return self._call(inputs)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
fetched = self._callable_fn(*array_vals)
File "/home/tianze/anaconda3/envs/ftz/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1458, in __call__
run_metadata_ptr)
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv2d_1/convolution}}]]
[[conv2d_56/BiasAdd/_6051]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv2d_1/convolution}}]]
0 successful operations.
0 derived errors ignored.
============ System Information ============
encoding: UTF-8
git_branch: Not Found
git_commits: Not Found
gpu_cuda: 9.1
gpu_cudnn: 7.6.3
gpu_devices: GPU_0: Tesla V100-PCIE-32GB, GPU_1: Tesla V100-PCIE-32GB, GPU_2: Tesla V100-PCIE-32GB
gpu_devices_active: GPU_0, GPU_1, GPU_2
gpu_driver: 410.104
gpu_vram: GPU_0: 32480MB, GPU_1: 32480MB, GPU_2: 32480MB
os_machine: x86_64
os_platform: Linux-4.15.0-64-generic-x86_64-with-debian-buster-sid
os_release: 4.15.0-64-generic
py_command: /home/tianze/face/faceswap.py extract -i /home/tianze/face/src/nomask.mp4 -o /home/tianze/face/faces/nomask --serializer json -D s3fd -A fan -nm none -min 0 -l 0.4 -bt 0.0 -een 1 -sz 256 -si 0 -L INFO -gui
py_conda_version: conda 4.7.12
py_implementation: CPython
py_version: 3.5.6
py_virtual_env: True
sys_cores: 48
sys_processor: x86_64
sys_ram: Total: 514627MB, Available: 469354MB, Used: 40669MB, Free: 314539MB
=============== Pip Packages ===============
absl-py==0.8.1
astor==0.8.0
certifi==2016.9.26
cloudpickle==1.2.2
cycler==0.10.0
dask==2.5.2
decorator==4.4.0
fastcluster==1.1.25
ffmpy==0.2.2
gast==0.3.2
google-pasta==0.1.7
grpcio==1.24.1
h5py==2.9.0
imageio==2.4.1
imageio-ffmpeg==0.3.0
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.0.1
Markdown==3.1.1
matplotlib==2.2.2
networkx==2.3
numpy==1.16.2
nvidia-ml-py3==7.352.1
olefile==0.46
opencv-python==4.1.1.26
pathlib==1.0.1
Pillow==6.1.0
protobuf==3.10.0
psutil==5.4.7
pyparsing==2.4.2
python-dateutil==2.7.3
pytz==2019.3
PyWavelets==1.0.0
PyYAML==5.1.2
scikit-image==0.14.0
scikit-learn==0.20.0
scipy==1.1.0
six==1.11.0
tensorboard==1.14.0
tensorflow-estimator==1.14.0
tensorflow-gpu==1.14.0
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==5.1.1
tqdm==4.36.1
Werkzeug==0.16.0
wrapt==1.11.2
============== Conda Packages ==============
# packages in environment at /home/tianze/anaconda3/envs/ftz:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
absl-py 0.8.1 pypi_0 pypi
astor 0.8.0 pypi_0 pypi
blas 1.0 mkl
bzip2 1.0.8 h516909a_1 conda-forge
ca-certificates 2019.9.11 hecc5488_0 conda-forge
certifi 2016.9.26 py35_0 conda-forge
cloudpickle 1.2.2 py_0
cycler 0.10.0 py35hc4d5149_0
dask-core 2.5.2 py_0
dbus 1.13.6 h746ee38_0
decorator 4.4.0 py_0
expat 2.2.6 he6710b0_0
fastcluster 1.1.25 py35hf8a1672_0 conda-forge
ffmpeg 4.0 h04d0a96_0
ffmpy 0.2.2 pypi_0 pypi
fontconfig 2.12.6 h49f89f6_0
freetype 2.8 hab7d2ae_1
gast 0.3.2 pypi_0 pypi
glib 2.56.2 hd408876_0
google-pasta 0.1.7 pypi_0 pypi
grpcio 1.24.1 pypi_0 pypi
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.9.0 pypi_0 pypi
icu 58.2 h9c2bf20_1
imageio 2.4.1 py35_0
imageio-ffmpeg 0.3.0 py_0 conda-forge
intel-openmp 2019.4 243
jpeg 9b h024ee3a_2
keras 2.2.4 pypi_0 pypi
keras-applications 1.0.8 pypi_0 pypi
keras-preprocessing 1.1.0 pypi_0 pypi
kiwisolver 1.0.1 py35hf484d3e_0
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_0
libopus 1.3 h7b6447c_0
libpng 1.6.37 hbc83047_0
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.0.10 h2733197_2
libuuid 1.0.3 h1bed415_2
libvpx 1.7.0 h439df22_0
libxcb 1.13 h1bed415_1
libxml2 2.9.9 hea5a465_1
markdown 3.1.1 pypi_0 pypi
matplotlib 2.2.2 py35h0e671d2_1
mkl 2018.0.3 1
ncurses 6.1 he6710b0_1
networkx 2.3 py_0
numpy 1.16.2 pypi_0 pypi
nvidia-ml-py3 7.352.1 pypi_0 pypi
olefile 0.46 py35_0
opencv-python 4.1.1.26 pypi_0 pypi
openssl 1.0.2r h14c3975_0 conda-forge
pathlib 1.0.1 pypi_0 pypi
pcre 8.43 he6710b0_0
pillow 6.1.0 pypi_0 pypi
pip 10.0.1 py35_0
protobuf 3.10.0 pypi_0 pypi
psutil 5.4.7 py35h14c3975_0
pyparsing 2.4.2 py_0
pyqt 5.9.2 py35h751905a_0
python 3.5.6 hc3d631a_0
python-dateutil 2.7.3 py35_0
pytz 2019.3 py_0
pywavelets 1.0.0 py35hdd07704_0
pyyaml 5.1.2 pypi_0 pypi
qt 5.9.5 h7e424d6_0
readline 7.0 h7b6447c_5
scikit-image 0.14.0 py35hf484d3e_1
scikit-learn 0.20.0 py35h4989274_1
scipy 1.1.0 py35hd20e5f9_0
setuptools 41.4.0 pypi_0 pypi
sip 4.19.8 py35hf484d3e_0
six 1.11.0 py35_1
sqlite 3.30.0 h7b6447c_0
tensorboard 1.14.0 pypi_0 pypi
tensorflow-estimator 1.14.0 pypi_0 pypi
tensorflow-gpu 1.14.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
tk 8.6.8 hbc83047_0
toolz 0.10.0 py_0
toposort 1.5 py_3 conda-forge
tornado 5.1.1 py35h7b6447c_0
tqdm 4.36.1 py_0
werkzeug 0.16.0 pypi_0 pypi
wheel 0.31.1 py35_0
wrapt 1.11.2 pypi_0 pypi
xz 5.2.4 h14c3975_4
zlib 1.2.11 h7b6447c_3
zstd 1.3.7 h0b5b093_0
================= Configs ==================
--------- gui.ini ---------
[global]
fullscreen: False
tab: extract
options_panel_width: 30
console_panel_height: 20
font: default
font_size: 9
--------- train.ini ---------
[global]
coverage: 68.75
mask_type: none
mask_blur: False
icnr_init: False
conv_aware_init: False
subpixel_upscaling: False
reflect_padding: False
penalized_mask_loss: True
loss_function: mae
learning_rate: 5e-05
[model.dfl_sae]
input_size: 128
autoencoder_dims: 0
architecture: df
clipnorm: True
multiscale_decoder: False
decoder_dims: 21
encoder_dims: 42
[model.realface]
complexity_decoder: 512
input_size: 64
dense_nodes: 1536
output_size: 128
complexity_encoder: 128
[model.original]
lowmem: False
[model.dfl_h128]
lowmem: False
[model.villain]
lowmem: False
[model.unbalanced]
input_size: 128
complexity_decoder_b: 512
complexity_encoder: 128
lowmem: False
complexity_decoder_a: 384
clipnorm: True
nodes: 1024
[trainer.original]
rotation_range: 10
color_clahe_max_size: 4
flip_chance: 50
color_lightness: 30
color_ab: 8
color_clahe_chance: 50
preview_images: 14
zoom_amount: 5
shift_range: 5
--------- convert.ini ---------
[mask.box_blend]
radius: 5.0
distance: 11.0
type: gaussian
passes: 1
[mask.mask_blend]
erosion: 0.0
radius: 3.0
type: normalized
passes: 4
[color.color_transfer]
clip: True
preserve_paper: True
[color.manual_balance]
balance_3: 0.0
balance_1: 0.0
contrast: 0.0
colorspace: HSV
brightness: 0.0
balance_2: 0.0
[color.match_hist]
threshold: 99.0
[scaling.sharpen]
threshold: 5.0
radius: 0.3
method: unsharp_mask
amount: 150
[writer.ffmpeg]
profile: auto
container: mp4
level: auto
tune: none
preset: medium
crf: 23
codec: libx264
[writer.pillow]
optimize: False
png_compress_level: 3
tif_compression: tiff_deflate
jpg_quality: 75
format: png
draw_transparent: False
gif_interlace: True
[writer.gif]
subrectangles: False
loop: 0
fps: 25
palettesize: 256
[writer.opencv]
png_compress_level: 3
draw_transparent: False
jpg_quality: 75
format: png
--------- .faceswap ---------
backend: nvidia
--------- extract.ini ---------
[global]
allow_growth: False
[align.fan]
batch-size: 8
[detect.cv2_dnn]
confidence: 50
[detect.mtcnn]
batch-size: 8
threshold_1: 0.6
threshold_2: 0.7
minsize: 20
scalefactor: 0.709
threshold_3: 0.7
[detect.s3fd]
batch-size: 8
confidence: 50
``` | closed | 2019-10-13T13:56:34Z | 2020-02-03T10:26:09Z | https://github.com/deepfakes/faceswap/issues/904 | [] | Futtttz | 1 |
vitalik/django-ninja | pydantic | 1,384 | How to set CSRF_HEADER_NAME with CORS | Hi,
I'm having troubles to understand how to make CSRF protection work with CORS.
Using
```py
api = NinjaAPI(auth=SessionAuth(csrf=true))
```
and looking at the implementation, The `ninja.utils.check_csrf` method will be called for all requests. In particular, the method `CsrfViewMiddleware.process_view` will be called. For POST request with no formData, this method requires that the header `settings.CSRF_HEADER_NAME` is set to the correct value of the CSRF token/secret. In a CORS context, the CSRF token being in the cookie (or the session), we cannot access it to set the header.
How do you make this work ?
Thanks
| closed | 2025-01-08T14:12:39Z | 2025-01-08T17:13:30Z | https://github.com/vitalik/django-ninja/issues/1384 | [] | Quetute | 1 |
dunossauro/fastapi-do-zero | sqlalchemy | 210 | Revisar o ultimo erro da aula de CI | closed | 2024-07-19T18:50:05Z | 2024-07-19T23:28:39Z | https://github.com/dunossauro/fastapi-do-zero/issues/210 | [] | dunossauro | 0 |
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