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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
kevlened/pytest-parallel | pytest | 82 | How to specify running order (both running in parallel and running in turn) when using multi-threaded/multi-process | How to specify running order (both running in parallel and running in turn) when using multi-threaded/multi-process.
I want three test files to run in parallel, with each test file running the test function in turn. How do I design this?
| open | 2020-08-26T09:07:14Z | 2020-08-26T09:07:14Z | https://github.com/kevlened/pytest-parallel/issues/82 | [] | 99Kies | 0 |
fastapi/sqlmodel | fastapi | 342 | Is it possible to instantiate a SQLModel object with relationships from a `dict` type? | ### 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 ๐
### Example Code
```python
from typing import List, Optional
from sqlmodel import Field, Relationship, Session, SQLModel, create_engine
from pydantic import BaseModel
class Team(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
heroes: List["Hero"] = Relationship(back_populates="team")
class Hero(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
team_id: Optional[int] = Field(default=None, foreign_key="team.id")
team: Optional[Team] = Relationship(back_populates="heroes")
class Hero2(BaseModel):
id: int
team: Team
class Team2(BaseModel):
id: int
heroes: List["Hero2"]
# What I would like to be able to do
d = {"id": 123, "team": {"id": 124}} # fails
# d = {"id": 123, "team": Team(**{"id": 124})} # succeeds
h = Hero(**d)
print(h)
# The same idea but using vanilla Pydantic models
d2 = {"id": 123, "team": {"id": 124}}
h2 = Hero2(**d2)
print(h2)
```
### Description
In Pydantic it is possible to instantiate objects directly from dicts (i.e. JSON) via `ClassName(**dict)`. This also works for objects with nested objects (i.e. relationships in SQLModel). Is it possible to do the same in SQLModel? I would like to take a JSON like `{"id": 123, "relationship_obj": {"id": 456, ...}}` and have SQLModel correctly create the relationship model based on the type of the field & the key of the dict passed in.
The error received is:
```
File "/.../venv/lib/python3.9/site-packages/sqlalchemy/orm/attributes.py", lin
e 1729, in emit_backref_from_scalar_set_event
instance_state(child),
AttributeError: 'dict' object has no attribute '_sa_instance_state'
```
### Operating System
Linux
### Operating System Details
_No response_
### SQLModel Version
0.0.6
### Python Version
Python 3.9.5
### Additional Context
_No response_ | open | 2022-05-17T04:29:36Z | 2022-09-07T16:18:27Z | https://github.com/fastapi/sqlmodel/issues/342 | [
"question"
] | JLHasson | 5 |
axnsan12/drf-yasg | django | 706 | How to disable swagger documentation in production server? | open | 2021-03-10T15:18:28Z | 2025-03-07T12:12:59Z | https://github.com/axnsan12/drf-yasg/issues/706 | [
"triage"
] | ruiqurm | 1 | |
pywinauto/pywinauto | automation | 525 | Listview - Getting error when trying to enter text in listview control | This is similar to issue #410.
The Listview properties is as below -
I need to be able to enter text in the editbox as below-
The Code snippet is -
```python
list_view = context.my_dialog['ListView']
list_view.get_item(0, 0).click_input()
list_edit = list_view.get_item(0, 0).inplace_control("Edit")
list_edit.type_keys("Sample{ENTER}", set_foreground=False)
```
I get the error - `pywinauto.remote_memory_block.AccessDenied: ('[WinError 87] The parameter is incorrect.process: %d', -1188825464)`
I tried to get `items()` as well.
`<bound method ListViewWrapper.items of <common_controls.ListViewWrapper - '', ListView, 6619632>`
when print `list_view.get_item(0, 0)` I get `<pywinauto.controls.common_controls._listview_item object at 0x0000020597F153C8>`
when print `list_view.get_item(1, 1)` I get `<pywinauto.controls.common_controls._listview_item object at 0x0000020597F04908>` | open | 2018-07-24T05:37:41Z | 2018-07-24T23:32:42Z | https://github.com/pywinauto/pywinauto/issues/525 | [] | madhavankumar | 4 |
huggingface/datasets | nlp | 7,171 | CI is broken: No solution found when resolving dependencies | See: https://github.com/huggingface/datasets/actions/runs/11046967444/job/30687294297
```
Run uv pip install --system -r additional-tests-requirements.txt --no-deps
ร No solution found when resolving dependencies:
โฐโโถ Because the current Python version (3.8.18) does not satisfy Python>=3.9
and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude
that torchdata==0.10.0a0+1a98f21 cannot be used.
And because only torchdata==0.10.0a0+1a98f21 is available and
you require torchdata, we can conclude that your requirements are
unsatisfiable.
Error: Process completed with exit code 1.
``` | closed | 2024-09-26T07:24:58Z | 2024-09-26T08:05:41Z | https://github.com/huggingface/datasets/issues/7171 | [
"bug"
] | albertvillanova | 0 |
RayVentura/ShortGPT | automation | 132 | ๐ [Bug]: UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined> | ### What happened?
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined>
### What type of browser are you seeing the problem on?
Chrome
### What type of Operating System are you seeing the problem on?
Windows
### Python Version
python 3.11
### Application Version
v0.1.3
### Expected Behavior
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined>
### Error Message
```shell
Running on local URL: http://0.0.0.0:31415
To create a public link, set `share=True` in `launch()`.
Video file C:\Users\Admin\AppData\Local\Temp\gradio\93ab877a770af7c60eaac186f2715b4e99692434\test.mp4
Step 1 _transcribe_audio
Video file C:\Users\Admin\AppData\Local\Temp\gradio\93ab877a770af7c60eaac186f2715b4e99692434\test.mp4
Exception in thread Thread-8 (_readerthread):
Traceback (most recent call last):
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 1038, in _bootstrap_inner
self.run()
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\subprocess.py", line 1568, in _readerthread
buffer.append(fh.read())
^^^^^^^^^
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined>
Failed getting the duration of the asked ressource the JSON object must be str, bytes or bytearray, not NoneType
Failed getting duration from the following video/audio url/path using yt_dlp. Unable to handle request: Unsupported url scheme: "c" (requests, urllib)
The url/path C:\Users\Admin\AppData\Local\Temp\gradio\93ab877a770af7c60eaac186f2715b4e99692434\test.mp4 does not point to a video/ audio. Impossible to extract its duration
Detected language: Vietnamese
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 11765/11765 [00:17<00:00, 678.32frames/s]
Step 2 _translate_content
Translating content: 5it [00:17, 3.54s/it]
Step 3 _generate_translated_audio
Generating translated audio: 0it [00:00, ?it/s]ffmpeg version N-112260-gb6e5136ba3-20231002 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 13.2.0 (crosstool-NG 1.25.0.232_c175b21)
configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libharfbuzz --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20231002
libavutil 58. 27.100 / 58. 27.100
libavcodec 60. 27.100 / 60. 27.100
libavformat 60. 13.100 / 60. 13.100
libavdevice 60. 2.101 / 60. 2.101
libavfilter 9. 11.100 / 9. 11.100
libswscale 7. 4.100 / 7. 4.100
libswresample 4. 11.100 / 4. 11.100
libpostproc 57. 2.100 / 57. 2.100
[mp3 @ 0000020632411b80] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_0_English.wav':
Duration: 00:00:03.19, start: 0.000000, bitrate: 48 kb/s
Stream #0:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s
Stream mapping:
Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_0_English_spedup.wav':
Metadata:
ISFT : Lavf60.13.100
Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 24000 Hz, mono, s16, 384 kb/s
Metadata:
encoder : Lavc60.27.100 pcm_s16le
[out#0/wav @ 000002063241a740] video:0kB audio:131kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.058167%
size= 131kB time=00:00:02.76 bitrate= 387.9kbits/s speed= 102x
Generating translated audio: 1it [00:01, 1.10s/it]ffmpeg version N-112260-gb6e5136ba3-20231002 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 13.2.0 (crosstool-NG 1.25.0.232_c175b21)
configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libharfbuzz --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20231002
libavutil 58. 27.100 / 58. 27.100
libavcodec 60. 27.100 / 60. 27.100
libavformat 60. 13.100 / 60. 13.100
libavdevice 60. 2.101 / 60. 2.101
libavfilter 9. 11.100 / 9. 11.100
libswscale 7. 4.100 / 7. 4.100
libswresample 4. 11.100 / 4. 11.100
libpostproc 57. 2.100 / 57. 2.100
[mp3 @ 00000253e6898580] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_1_English.wav':
Duration: 00:00:37.51, start: 0.000000, bitrate: 48 kb/s
Stream #0:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s
Stream mapping:
Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_1_English_spedup.wav':
Metadata:
ISFT : Lavf60.13.100
Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 24000 Hz, mono, s16, 384 kb/s
Metadata:
encoder : Lavc60.27.100 pcm_s16le
[out#0/wav @ 00000253e6877340] video:0kB audio:1717kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.004436%
size= 1717kB time=00:00:36.61 bitrate= 384.1kbits/s speed= 259x
Generating translated audio: 2it [00:05, 3.23s/it]ffmpeg version N-112260-gb6e5136ba3-20231002 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 13.2.0 (crosstool-NG 1.25.0.232_c175b21)
configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libharfbuzz --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20231002
libavutil 58. 27.100 / 58. 27.100
libavcodec 60. 27.100 / 60. 27.100
libavformat 60. 13.100 / 60. 13.100
libavdevice 60. 2.101 / 60. 2.101
libavfilter 9. 11.100 / 9. 11.100
libswscale 7. 4.100 / 7. 4.100
libswresample 4. 11.100 / 4. 11.100
libpostproc 57. 2.100 / 57. 2.100
[mp3 @ 000001c369d521c0] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_2_English.wav':
Duration: 00:00:32.66, start: 0.000000, bitrate: 48 kb/s
Stream #0:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s
Stream mapping:
Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_2_English_spedup.wav':
Metadata:
ISFT : Lavf60.13.100
Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 24000 Hz, mono, s16, 384 kb/s
Metadata:
encoder : Lavc60.27.100 pcm_s16le
[out#0/wav @ 000001c369d67500] video:0kB audio:1629kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.004677%
size= 1629kB time=00:00:34.71 bitrate= 384.4kbits/s speed= 265x
Generating translated audio: 3it [00:09, 3.38s/it]ffmpeg version N-112260-gb6e5136ba3-20231002 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 13.2.0 (crosstool-NG 1.25.0.232_c175b21)
configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libharfbuzz --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20231002
libavutil 58. 27.100 / 58. 27.100
libavcodec 60. 27.100 / 60. 27.100
libavformat 60. 13.100 / 60. 13.100
libavdevice 60. 2.101 / 60. 2.101
libavfilter 9. 11.100 / 9. 11.100
libswscale 7. 4.100 / 7. 4.100
libswresample 4. 11.100 / 4. 11.100
libpostproc 57. 2.100 / 57. 2.100
[mp3 @ 000001e563e82900] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_3_English.wav':
Duration: 00:00:16.66, start: 0.000000, bitrate: 48 kb/s
Stream #0:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s
Stream mapping:
Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_3_English_spedup.wav':
Metadata:
ISFT : Lavf60.13.100
Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 24000 Hz, mono, s16, 384 kb/s
Metadata:
encoder : Lavc60.27.100 pcm_s16le
[out#0/wav @ 000001e563e99c00] video:0kB audio:672kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.011336%
size= 672kB time=00:00:14.30 bitrate= 384.9kbits/s speed= 199x
Generating translated audio: 4it [00:12, 3.08s/it]ffmpeg version N-112260-gb6e5136ba3-20231002 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 13.2.0 (crosstool-NG 1.25.0.232_c175b21)
configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-w64-mingw32- --arch=x86_64 --target-os=mingw32 --enable-gpl --enable-version3 --disable-debug --disable-w32threads --enable-pthreads --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libharfbuzz --enable-libvorbis --enable-opencl --disable-libpulse --enable-libvmaf --disable-libxcb --disable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --enable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libvpl --enable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --enable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --disable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags= --extra-libs=-lgomp --extra-version=20231002
libavutil 58. 27.100 / 58. 27.100
libavcodec 60. 27.100 / 60. 27.100
libavformat 60. 13.100 / 60. 13.100
libavdevice 60. 2.101 / 60. 2.101
libavfilter 9. 11.100 / 9. 11.100
libswscale 7. 4.100 / 7. 4.100
libswresample 4. 11.100 / 4. 11.100
libpostproc 57. 2.100 / 57. 2.100
[mp3 @ 000002113fe29ac0] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_4_English.wav':
Duration: 00:00:19.10, start: 0.000000, bitrate: 48 kb/s
Stream #0:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s
Stream mapping:
Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '.editing_assets/content_translation_assets/551374b33ff249928bca5784/translated_4_English_spedup.wav':
Metadata:
ISFT : Lavf60.13.100
Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 24000 Hz, mono, s16, 384 kb/s
Metadata:
encoder : Lavc60.27.100 pcm_s16le
[out#0/wav @ 000002113fe07c40] video:0kB audio:936kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.008139%
size= 936kB time=00:00:19.94 bitrate= 384.4kbits/s speed= 227x
Generating translated audio: 5it [00:14, 2.89s/it]
Step 4 _edit_and_render_video
Exception in thread Thread-33 (_readerthread):
Traceback (most recent call last):
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 1038, in _bootstrap_inner
self.run()
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\subprocess.py", line 1568, in _readerthread
buffer.append(fh.read())
^^^^^^^^^
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined>
Failed getting the duration of the asked ressource the JSON object must be str, bytes or bytearray, not NoneType
Failed getting duration from the following video/audio url/path using yt_dlp. Unable to handle request: Unsupported url scheme: "c" (requests, urllib)
The url/path C:\Users\Admin\AppData\Local\Temp\gradio\93ab877a770af7c60eaac186f2715b4e99692434\test.mp4 does not point to a video/ audio. Impossible to extract its duration
Exception in thread Thread-35 (_readerthread):
Traceback (most recent call last):
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 1038, in _bootstrap_inner
self.run()
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\subprocess.py", line 1568, in _readerthread
buffer.append(fh.read())
^^^^^^^^^
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined>
Failed getting the duration of the asked ressource the JSON object must be str, bytes or bytearray, not NoneType
Failed getting duration from the following video/audio url/path using yt_dlp. Unable to handle request: Unsupported url scheme: "c" (requests, urllib)
The url/path C:\Users\Admin\AppData\Local\Temp\gradio\93ab877a770af7c60eaac186f2715b4e99692434\test.mp4 does not point to a video/ audio. Impossible to extract its duration
Error File "D:\ShortGPT\gui\ui_tab_video_translation.py", line 86, in translate_video
for step_num, step_info in content_translation_engine.makeContent():
File "D:\ShortGPT\shortGPT\engine\abstract_content_engine.py", line 74, in makeContent
self.stepDict[currentStep]()
File "D:\ShortGPT\shortGPT\engine\multi_language_translation_engine.py", line 106, in _edit_and_render_video
if video_length - last_t2 >4:
```
### Code to produce this issue.
```shell
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined>
```
### Screenshots/Assets/Relevant links
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 845: character maps to <undefined> | open | 2024-02-28T12:55:48Z | 2024-09-24T19:41:01Z | https://github.com/RayVentura/ShortGPT/issues/132 | [
"bug"
] | linhcentrio | 1 |
plotly/dash-core-components | dash | 752 | Inline CSS vs. build to separate CSS file | CSP ([Content Security Policy](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy)) is a good tool for enhancing security of web applications. [Mozilla Observatory](https://observatory.mozilla.org/analyze/observatory.mozilla.org) is a good place to check for CSP implementation across web applications. How easy it is to enforce strict CSP settings depends, to a large degree, on the frameworks used in the stack.
Dash out of the box is quite CSP settings friendly, e.g. you can do `pip install dash flask-talisman` (alternatively set the CSP headers directly instead of using [flask-talisman](https://github.com/GoogleCloudPlatform/flask-talisman)) and then run e.g.
```
import dash
import dash_html_components as html
from flask_talisman import Talisman
app = dash.Dash(__name__)
CSP = {
"default-src": "'self'",
"script-src": [
"'self'",
# Due to https://github.com/plotly/dash/issues/630:
"'sha256-jZlsGVOhUAIcH+4PVs7QuGZkthRMgvT2n0ilH6/zTM0='",
]
}
Talisman(app.server, content_security_policy=CSP, force_https=False)
app.layout = html.Div(children=["Hello Dash!"])
if __name__ == "__main__":
app.run_server()
```
This will work with no CSP errors on localhost in the browser console - despite quite strict CSP settings.
If however `import dash_core_components as dcc` is added, you will need to either add
```python
"style-src": ["'self'", "'unsafe-inline'"],
```
or
```python
"style-src": [
"'sha256-47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU='",
"'sha256-cJ5ObsneXZnEJ3xXl8hpmaM9RbOwaU6UIPslFf0/OOE='",
"'sha256-joa3+JBk4cwm5iLfizxl9fClObT5tKuZMFQ9B2FZlMg='",
"'sha256-Jtp4i67c7nADbaBCKBYgqI86LQOVcoZ/khTKsuEkugc='",
"'sha256-MoQFjm6Ko0VvKSJqNGTl4e5H3guejX+CG/LxytSdnYg='",
"'sha256-kkTbqhXgCW2olAc5oz4XYZtzEwh5ojnaxt9iOTAZcZo='",
],
```
to the `CSP` dictionary in order to allow the CSS that `dcc` is adding inline to the app.
The first is not optimal as you then again open for [inline CSS "XSS"](https://stackoverflow.com/questions/30653698/csp-style-src-unsafe-inline-is-it-worth-it) (as when not using CSP at all). The second is not optimal either as the hashes will need to update each time a new version of `dcc` changes its inline style content.
If `dcc` could output CSS as a separate file during build, instead of inline style, we could with Dash also enforce strict CSS CSP. I.e. use [`mini-css-extract-plugin`](https://webpack.js.org/plugins/mini-css-extract-plugin/) instead of `style-load` in `webpack.config.js` during (production?) build of `dcc`. | open | 2020-02-07T14:57:15Z | 2020-02-10T17:24:33Z | https://github.com/plotly/dash-core-components/issues/752 | [] | anders-kiaer | 4 |
PokeAPI/pokeapi | graphql | 362 | Merging and linting strategies | Our two main repositories are now pokeapi and [ditto](https://github.com/PokeAPI/ditto/).
To reduce confusion for maintainers, it would probably be a good idea to standardise management of these repos, so I was looking at what is currently set up differently between the two. One item is the allowed merge strategies:
โข pokeapi currently allows rebase merging only
โข ditto currently allows squash merging only
The remaining option is merge commits.
Does anyone have any concerns or thoughts one way or the other about how we should manage merging? I personally like merge commits, dislike squash merging, and am indifferent about rebase merging - but in the end I don't really mind what we go with; I just love consistency ๐
Does anyone have any strongly held opinions on this, or otherwise any objections to changing ditto to the same merge strategy as pokeapi, now that it is a part of the PokeAPI organisation? Ping @sargunv as the primary maintainer of ditto. ๐
(Worth noting it's also possible to allow multiple merge strategies - although I think it might be better to stick to one) | closed | 2018-09-09T08:15:48Z | 2020-08-19T10:16:35Z | https://github.com/PokeAPI/pokeapi/issues/362 | [
"question"
] | tdmalone | 12 |
recommenders-team/recommenders | machine-learning | 1,964 | own train-test data split |
i am following this notebook https://github.com/microsoft/recommenders/blob/main/examples/02_model_collaborative_filtering/ncf_deep_dive.ipynb
but i dont have any timestamp related column in my dataset . i have created train-test split externally and want to feed it to NCFDataset class but i m getting "Empty file error".
any way to solve this | closed | 2023-08-09T10:45:15Z | 2023-08-14T07:53:49Z | https://github.com/recommenders-team/recommenders/issues/1964 | [
"help wanted"
] | riyaj8888 | 1 |
netbox-community/netbox | django | 18,329 | [GraphQL] Only one primary IP is shown when querying | ### Deployment Type
Self-hosted
### Triage priority
N/A
### NetBox Version
v4.2.0
### Python Version
3.12
### Steps to Reproduce
1. Create multiple Devices and assign different IPs to this devices.
2. Mark these IPs as primary for the device.
3. Run this GraphQL query:
```
{
device_list {
primary_ip4 {
id
display
}
}
}
```
### Expected Behavior
A list with all primary IPs should be displayed
### Observed Behavior
Only the first primary IP is displayed. All other IPs are NULL.
Example:
IPs:
- 192.168.1.1
- 192.168.1.2
- 192.168.1.3
Only 192.168.1.1 is displayed, when you unmark this IP, 192.168.1.2 is displayed when running the query. | closed | 2025-01-07T15:30:23Z | 2025-02-06T07:01:59Z | https://github.com/netbox-community/netbox/issues/18329 | [
"type: bug",
"status: accepted",
"severity: medium"
] | freym | 3 |
marcomusy/vedo | numpy | 397 | Resolve overlapping faces in the mesh | Dearย @marcomusy,
Is it possible to remove or resolve the overlapping faces in the mesh using vedo library...?
if any possible way. Iย will be gratefulย to use it.
Thanksย in advance
Regards,
E.davis | closed | 2021-05-16T12:37:43Z | 2021-05-20T04:42:00Z | https://github.com/marcomusy/vedo/issues/397 | [] | nikhilpatiltest | 4 |
developmentseed/lonboard | jupyter | 635 | DataFilterExtension for boolean arrays | Todo:
- [ ] Make deck.gl test app for Felix with Arrow point data and boolean array buffer | open | 2024-09-16T10:16:52Z | 2024-09-16T10:16:52Z | https://github.com/developmentseed/lonboard/issues/635 | [] | kylebarron | 0 |
globaleaks/globaleaks-whistleblowing-software | sqlalchemy | 3,787 | notification when a "whistleblower has selected you as a valuable recipient" has an unworkable access link | ### What version of GlobaLeaks are you using?
4.13.18
### What browser(s) are you seeing the problem on?
All
### What operating system(s) are you seeing the problem on?
Windows, Linux
### Describe the issue
In "Notifications > Templates > tip_mail_template" there is a link with a url for easy access to the relevant report:
> The report can be accessed at:
> {Url}
At first sight, {Url} seems to be translated correctly to something like:
> https://my.globaleaks.server/#/status/12345678-1234-1234-1234-1234567890ab
However, the recipient of that link, when clicking on it, is instead redirected to
> https://my.globaleaks.server/#/
### Proposed solution
_No response_ | open | 2023-11-16T14:44:50Z | 2023-11-17T08:07:22Z | https://github.com/globaleaks/globaleaks-whistleblowing-software/issues/3787 | [] | rodolfomatos | 1 |
hankcs/HanLP | nlp | 680 | 1.3.2็ๆฌ โๅ็่ชๆฅโๅจๅคไบๅฅๆซๆถ่ฟ่กๅ่ฏ๏ผ่ฏฅ่ฏไผๆถๅคฑ | 1.3.2็ๆฌไธญๅบ็ฐ๏ผ
ไพๅฅ๏ผๆญฆๆฑๅก่ฐท9ๆฅๅ็่ชๆฅ
่ฏ่ฏญโๅ็่ชๆฅโๅจ segment.seg(sentence)ๆไฝๅๆถๅคฑ๏ผไป
ๅบ็ฐโๆญฆๆฑๅก่ฐทโ ใโ9ๆฅโ๏ผ
้คๆญคไนๅค่ฟๆไบๅ
ถๅฎๅจ่ฏๅจๅฅๆซๆถไผๆพ็ฐๆญค็งๆ
ๅตใ
ๆ่งฃๅณใ
| closed | 2017-11-17T09:29:53Z | 2017-11-17T15:54:29Z | https://github.com/hankcs/HanLP/issues/680 | [
"invalid"
] | ZhoChoran | 1 |
ydataai/ydata-profiling | jupyter | 1,455 | AttritubeError mplDeprecation with matplotlib 3.8.0 | ### Current Behaviour
The following error occurs, when running a project with matplotlib 3.8.0
```
warnings.filterwarnings("ignore", category=matplotlib.cbook.mplDeprecation)
AttributeError: module 'matplotlib.cbook' has no attribute 'mplDeprecation'
```
Reported here as well: https://stackoverflow.com/questions/77128061/ydata-profiling-profilereport-attributeerror-module-matplotlib-cbook-has-no
### Expected Behaviour
No error. Possibility to use matplotlib 3.8.0
### Data Description
-
### Code that reproduces the bug
```Python
Problem seems to come from here: https://github.com/ydataai/ydata-profiling/blob/develop/src/ydata_profiling/visualisation/context.py#L85
```
### pandas-profiling version
-
### Dependencies
```Text
-
```
### OS
Manjaro / Docker python:3.11-slim
### Checklist
- [X] There is not yet another bug report for this issue in the [issue tracker](https://github.com/ydataai/pandas-profiling/issues)
- [X] The problem is reproducible from this bug report. [This guide](http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) can help to craft a minimal bug report.
- [X] The issue has not been resolved by the entries listed under [Common Issues](https://pandas-profiling.ydata.ai/docs/master/pages/support_contrib/common_issues.html). | closed | 2023-09-22T17:29:10Z | 2023-11-15T23:22:38Z | https://github.com/ydataai/ydata-profiling/issues/1455 | [
"bug ๐"
] | erreurBarbare | 3 |
joke2k/django-environ | django | 224 | Any plan to support .toml format env file? | ### Any plan to support .toml format env file? Thanks! | closed | 2019-04-26T15:42:58Z | 2024-10-27T01:29:07Z | https://github.com/joke2k/django-environ/issues/224 | [
"enhancement"
] | wahello | 4 |
biolab/orange3 | pandas | 6,753 | Import Images Widget Info Typo | <!--
Thanks for taking the time to report a bug!
If you're raising an issue about an add-on (i.e., installed via Options > Add-ons), raise an issue in the relevant add-on's issue tracker instead. See: https://github.com/biolab?q=orange3
To fix the bug, we need to be able to reproduce it. Please answer the following questions to the best of your ability.
-->
**What's wrong?**
I imported an `image-data` folder with 3 labeled subfolders - `broccoli`, `capsicum`, `tomato` using the Image Analytics Add-on.
It showed `3 categorys`, instead of `3 categories`.
I tested by adding these two lines to [orange3/Orange/widgets/utils/localization/__init__.py](https://github.com/biolab/orange3/blob/master/Orange/widgets/utils/localization/__init__.py) locally, it somehow fixed the typo.
**What's your environment?**
<!-- To find your Orange version, see "Help โ About โ Version" or `Orange.version.full_version` in code -->
- Operating system: Windows 10
- Orange version: 3.36.2
- How you installed Orange: `pip install Orange3`
| closed | 2024-03-04T11:53:34Z | 2024-03-15T11:36:16Z | https://github.com/biolab/orange3/issues/6753 | [
"bug",
"snack"
] | foongminwong | 1 |
sanic-org/sanic | asyncio | 2,182 | Request streaming results in a phantom 503 | When streaming a request body, you end up with a phantom 503 response. To the client, everything looks fine. The data is transmitted, and a response received OK.
```
[2021-07-05 22:45:47 +0300] - (sanic.access)[INFO][127.0.0.1:34264]: POST http://localhost:9999/upload 201 4
[2021-07-05 22:45:47 +0300] - (sanic.access)[INFO][127.0.0.1:34264]: POST http://localhost:9999/upload 503 666
[2021-07-05 22:45:47 +0300] [686804] [ERROR] Connection lost before response written @ ('127.0.0.1', 34264) <Request: POST /upload>
```
But, there is an extra 503 that is caused by a task cancel while waiting on `receive_more`. This appears to be caused by leaving one extra CRLF in the buffer. | closed | 2021-07-05T21:50:49Z | 2021-07-28T08:57:59Z | https://github.com/sanic-org/sanic/issues/2182 | [
"bug"
] | ahopkins | 1 |
great-expectations/great_expectations | data-science | 10,816 | gx.ValidationDefinition.run() adds the Validation Definition to Data Context | **Describe the bug**
When running `run()` method of a `gx.ValidationDefinition` instance the validation definition is being added to the File Data Context.
It is not the expected behavior according to `help(gx.ValidationDefinition.run)`. We have the `add()` method for to add validation definitions to the context.
In my use case, I want to configure validation definitions in memory, matching different dataframes and suites. But I can't reuse the code without adding the `delete()` method -- see STR
**To Reproduce**
```
expectation_suite = context.suites.get(name=suite_name)
batch_definition = context.data_sources.get(data_source_name).get_asset(data_asset_name).get_batch_definition(batch_definition_name)
validation_definition_name = "my_validation_definition"
# Create a Validation Definition
validation_definition = gx.ValidationDefinition(
data=batch_definition, suite=expectation_suite, name=validation_definition_name
)
# we have a dedicated method to add the Validation Definition to the Data Context, but I do not use it:
# validation_definition = context.validation_definitions.add(validation_definition)
# After the Validation Definition Run, the validation definition is available in the Data Context, even without add() method
validation_results = validation_definition.run(batch_parameters={"dataframe": dataframe}, result_format = "COMPLETE") # "SUMMARY" / "COMPLETE"
# When next time this code is run I encounter this error <<StoreBackendError: Store already has the following key: ('my_validation_definition',).>>
# That is why I delete the Validation Definition from the Data Context every run
# context.validation_definitions.delete(validation_definition_name)
```
if run the code above more than once as is, without uncommenting the line with validation_definitions.delete(),
it will cause the following error :
`StoreBackendError: Store already has the following key: ('my_validation_definition',).`
```
StoreBackendError: Store already has the following key: ('my_validation_definition',).
---------------------------------------------------------------------------
StoreBackendError Traceback (most recent call last)
File <command-3947137279416384>, line 14
6 validation_definition = gx.ValidationDefinition(
7 data=batch_definition, suite=expectation_suite, name=validation_definition_name
8 )
10 # we have a dedicated method to add the Validation Definition to the Data Context, but I do not use it:
11 # validation_definition = context.validation_definitions.add(validation_definition)
12
13 # After the Validation Definition Run, the validation definition is available in the Data Context => When next time this code is run
---> 14 validation_results = validation_definition.run(batch_parameters={"dataframe": dataframe}, result_format = "COMPLETE") # "SUMMARY" / "COMPLETE"
17 context.validation_definitions.delete(validation_definition_name)
18 print(validation_results)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/core/validation_definition.py:280, in ValidationDefinition.run(self, checkpoint_id, batch_parameters, expectation_parameters, result_format, run_id)
277 if not diagnostics.success:
278 # The validation definition itself is not added but all children are - we can add it for the user # noqa: E501
279 if not diagnostics.parent_added and diagnostics.children_added:
--> 280 self._add_to_store()
281 else:
282 diagnostics.raise_for_error()
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/core/validation_definition.py:381, in ValidationDefinition._add_to_store(self)
378 store = project_manager.get_validation_definition_store()
379 key = store.get_key(name=self.name, id=self.id)
--> 381 store.add(key=key, value=self)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/data_context/store/store.py:299, in Store.add(self, key, value, **kwargs)
295 def add(self, key: DataContextKey, value: Any, **kwargs) -> None:
296 """
297 Essentially `set` but validates that a given key-value pair does not already exist.
298 """
--> 299 return self._add(key=key, value=value, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/data_context/store/validation_definition_store.py:100, in ValidationDefinitionStore._add(self, key, value, **kwargs)
97 if not self.cloud_mode:
98 # this logic should move to the store backend, but is implemented here for now
99 value.id = str(uuid.uuid4())
--> 100 return super()._add(key=key, value=value, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/data_context/store/store.py:303, in Store._add(self, key, value, **kwargs)
301 def _add(self, key: DataContextKey, value: Any, **kwargs) -> Any:
302 self._validate_key(key)
--> 303 output = self._store_backend.add(self.key_to_tuple(key), self.serialize(value), **kwargs)
304 if hasattr(value, "id") and hasattr(output, "id"):
305 value.id = output.id
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/data_context/store/_store_backend.py:141, in StoreBackend.add(self, key, value, **kwargs)
137 def add(self, key, value, **kwargs):
138 """
139 Essentially `set` but validates that a given key-value pair does not already exist.
140 """
--> 141 return self._add(key=key, value=value, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-e1011015-1c0d-4390-bbff-4675253d3a70/lib/python3.10/site-packages/great_expectations/data_context/store/_store_backend.py:145, in StoreBackend._add(self, key, value, **kwargs)
143 def _add(self, key, value, **kwargs):
144 if self.has_key(key):
--> 145 raise StoreBackendError(f"Store already has the following key: {key}.") # noqa: TRY003
146 return self.set(key=key, value=value, **kwargs)
StoreBackendError: Store already has the following key: ('my_validation_definition',).
```
**Expected behavior**
A `gx.ValidationDefinition.run()` should be run without saving it to the "validation_definitions" folder
**Environment (please complete the following information):**
- Great Expectations Version: 1.3.0
- Data Source: Spark
- Cloud environment: Databricks on Azure
| closed | 2025-01-02T15:20:56Z | 2025-01-21T21:47:03Z | https://github.com/great-expectations/great_expectations/issues/10816 | [
"request-for-help"
] | vasilijyaromenka | 4 |
jupyter/nbgrader | jupyter | 1,337 | FORMGRADER error | ### Operating system
UBUNTU Linux
### `nbgrader --version`
0.6.1
### `jupyter notebook --version`
jupyter (1.0.0)
jupyter-client (6.1.3)
jupyter-console (6.1.0)
jupyter-core (4.6.3)
### Expected behavior
Use Formgrader menu item in Jupyter
### Actual behavior
Browser screen:
nbgrader
Error
Manage Assignments
Gradebook
Manage Students
Sorry, the formgrader encountered an error. Please contact the administrator of the formgrader for further assistance.
Jupter --debug message
[W 09:14:01.345 NotebookApp] 404 GET /nbextensions/nbextensions_configurator/tree_tab/main.js?v=20200515091357 (127.0.0.1) 31.49ms referer=http://localhost:8888/tree
### Steps to reproduce the behavior
re-installed jupyer and nbgrader
went through nbgrader extension & serverextenstion enable steps
created nbgrader_config and quickstart course
made /srv/nbgrader/exchange readable
ran jupter notebook both from base directory and also tried from course subdirectory | open | 2020-05-15T13:38:43Z | 2020-05-15T15:27:42Z | https://github.com/jupyter/nbgrader/issues/1337 | [] | mboldin-temple | 2 |
microsoft/MMdnn | tensorflow | 574 | Caffe to Keras: Layer weight shape (64,) not compatible with provided weight shape (1, 1, 1, 64) | Platform (like ubuntu 16.04/win10): ubuntu 16.04
Python version: Python 3.6.8
Source framework with version (like Tensorflow 1.4.1 with GPU): Caffe
Destination framework with version (like CNTK 2.3 with GPU): Keras
Pre-trained model path (webpath or webdisk path): ResNet-152 downloaded from mmdownload -f caffe -n resnet152 -o ./
Running scripts:
mmconvert -sf caffe -in resnet152-deploy.prototxt -iw resnet152.caffemodel -df keras -om caffe_resnet152.h5
Error:
Traceback (most recent call last):
File "/home/daby/venv1/bin/mmconvert", line 10, in <module>
sys.exit(_main())
File "/home/daby/venv1/lib/python3.6/site-packages/mmdnn/conversion/_script/convert.py", line 112, in _main
dump_code(args.dstFramework, network_filename + '.py', temp_filename + '.npy', args.outputModel, args.dump_tag)
File "/home/daby/venv1/lib/python3.6/site-packages/mmdnn/conversion/_script/dump_code.py", line 32, in dump_code
save_model(MainModel, network_filepath, weight_filepath, dump_filepath)
File "/home/daby/venv1/lib/python3.6/site-packages/mmdnn/conversion/keras/saver.py", line 2, in save_model
model = MainModel.KitModel(weight_filepath)
File "77cdab28ab934526ad05e4a7c8f2ad82.py", line 623, in KitModel
set_layer_weights(model, weights_dict)
File "77cdab28ab934526ad05e4a7c8f2ad82.py", line 45, in set_layer_weights
model.get_layer(layer.name).set_weights(current_layer_parameters)
File "/home/daby/venv1/lib/python3.6/site-packages/keras/engine/base_layer.py", line 1057, in set_weights
'provided weight shape ' + str(w.shape))
ValueError: Layer weight shape (64,) not compatible with provided weight shape (1, 1, 1, 64)
| closed | 2019-01-28T06:17:04Z | 2019-03-12T06:52:29Z | https://github.com/microsoft/MMdnn/issues/574 | [] | MinhHuuNguyen | 3 |
robotframework/robotframework | automation | 5,189 | Make result file paths hyperlinks on terminal | Terminal emulators nowadays support hyperlinks pretty well.The [standard emerged in 2017](https://gist.github.com/egmontkob/eb114294efbcd5adb1944c9f3cb5feda) and the current list of [supporting terminal emulators](https://github.com/Alhadis/OSC8-Adoption/?tab=readme-ov-file) is pretty exhaustive. Based on a quick prototype, making the links to result files in the console after execution is easy and works very well.
The simple solution ought to work fine on all Linux and OSX terminals and the main problem is handling Windows. The traditional [Windows Console](https://en.wikipedia.org/wiki/Windows_Console) isn't a terminal emulator and doesn't support ANSI colors or hyperlinks. We have custom code for handling colors, but I don't think something like that is possible with links. There are, however, various other terminals for Windows and, for example, the Microsoft developed [Windows Terminal](https://en.wikipedia.org/wiki/Windows_Terminal) is a proper terminal emulator that supports both ANSI colors and hyperlinks.
The problem is that we don't currently have any code for detecting terminal capabilities. With colors we, by default, simply use ANSI outside Windows and on Windows use the aforementioned custom solution. Using the same approach with hyperlinks would be easy, but then Windows users with "proper" terminals would need to separately enable hyperlinks with `--console-colors ansi` (or `-C ansi`). That's a bit annoying, but I believe it would be fine in the beginning.
I consider this so convenient feature that I tentatively add this to RF 7.1 scope and ask opinions from others on the #devel channel on our Slack. Because we want RF 7.1 out soon, there's no time for anything bigger, so my proposal is to support hyperlinks when ANSI colors are enabled and to keep them disabled on Windows by default. If we agree this is a good approach, I'll submit a separate issue about enhancing terminal capability testing in RF 7.2 to make the Windows support better. | closed | 2024-08-29T10:36:16Z | 2024-09-04T10:04:15Z | https://github.com/robotframework/robotframework/issues/5189 | [
"enhancement",
"priority: high",
"rc 1",
"effort: medium"
] | pekkaklarck | 8 |
vaexio/vaex | data-science | 1,926 | [BUG-REPORT] CAN'T READ PARQUET FROM AMAZON S3 ON AN EC2 INSTANCE | **Description**
I can't load data from s3, by doing this
`import vaex`
`vaex.open("s3://myfile.parquet")`
I get the following error
```
error opening 's3://data-lake.e [__init__.py](file:///home/ubuntu/.pyenv/versions/3.7.5/lib/python3.7/site-packages/vaex/__init__.py):[259](file:///home/ubuntu/.pyenv/versions/3.7.5/lib/python3.7/site-packages/vaex/__init__.py#259)
u-central-1/v1/reporting_tables/reporting_tables
/trackingevents/'
Traceback (most recent call last):
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/__init__.py", line
232, in open
ds = vaex.dataset.open(path,
fs_options=fs_options, fs=fs, **kwargs)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/dataset.py", line
73, in open
return opener.open(path,
fs_options=fs_options, fs=fs, *args, **kwargs)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/opener.py",
line 44, in open
return open_parquet(path, *args, **kwargs)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/dataset.py",
line 345, in open_parquet
return DatasetParquet(path,
fs_options=fs_options, fs=fs,
partitioning=partitioning, kwargs=kwargs)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/dataset.py",
line 197, in __init__
super().__init__(max_rows_read=max_rows_read
)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/dataset.py",
line 26, in __init__
self._create_columns()
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/dataset.py",
line 227, in _create_columns
super()._create_columns()
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/dataset.py",
line 29, in _create_columns
self._create_dataset()
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/arrow/dataset.py",
line 232, in _create_dataset
self._arrow_ds =
pyarrow.dataset.dataset(source,
filesystem=file_system,
partitioning=self.partitioning)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/pyarrow/dataset.py", line
667, in dataset
return _filesystem_dataset(source, **kwargs)
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/pyarrow/dataset.py", line
420, in _filesystem_dataset
factory = FileSystemDatasetFactory(fs,
paths_or_selector, format, options)
File "pyarrow/_dataset.pyx", line 1854, in pya
rrow._dataset.FileSystemDatasetFactory.__init__
File "pyarrow/error.pxi", line 143, in
pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/_fs.pyx", line 1137, in
pyarrow._fs._cb_get_file_info_selector
File "/home/ubuntu/.pyenv/versions/3.7.5/lib/p
ython3.7/site-packages/vaex/file/cache.py", line
97, in get_file_info_selector
return self.fs.get_file_info_selector(*args,
**kwargs)
AttributeError: 'pyarrow._s3fs.S3FileSystem'
object has no attribute 'get_file_info_selector'
```
**Software information**
- Vaex version: {'vaex': '4.8.0',
'vaex-core': '4.8.0',
'vaex-viz': '0.5.1',
'vaex-hdf5': '0.12.0',
'vaex-server': '0.8.1',
'vaex-astro': '0.9.0',
'vaex-jupyter': '0.7.0',
'vaex-ml': '0.17.0'}
- Vaex was installed via: pip
- OS: Ubuntu
**Additional information**
I'm running on an EC2 instance so all the credentials for opening in s3 are already implemented
| open | 2022-02-15T16:56:26Z | 2022-07-22T13:22:48Z | https://github.com/vaexio/vaex/issues/1926 | [
"bug"
] | ivanachillee | 22 |
rio-labs/rio | data-visualization | 35 | FrostedGlassFill | closed | 2024-05-30T09:42:16Z | 2024-05-30T09:42:19Z | https://github.com/rio-labs/rio/issues/35 | [] | Sn3llius | 0 | |
explosion/spaCy | machine-learning | 13,056 | Displacy render: Spans are overlapped when rendered. | <!-- NOTE: For questions or install related issues, please open a Discussion instead. -->
Hello,
I'm trying to display spans with displacy render where I built the span manually.
Several spans can be overlapped.
Sometimes when there are more than 3 spans overlapping, the rendering fails to render properly.
In the following image spans on the second line have 1 token in common so the render should have done 3 lines, instead it overlapped them.
## How to reproduce the behaviour
<!-- Include a code example or the steps that led to the problem. Please try to be as specific as possible. -->
```code
doc_rendering = {
"text": "Welcome to the Bank of China.",
"spans": [
{"start_token": 2, "end_token": 5, "label": "SkillNC"},
{"start_token": 0, "end_token": 2, "label": "Skill"},
{"start_token": 1, "end_token": 3, "label": "Skill"},
],
"tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."],
}
```
```code
from spacy import displacy
html = displacy.render(
doc_rendering,
style="span",
manual=True,
options={"colors": {"Skill": "#56B4E9", "SkillNC": "#FF5733"}},
)
```
## Your Environment
<!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
* Operating System: linux
* Python Version Used: 3.10
* spaCy Version Used: 3.6.1
* Environment Information: conda 23.5.2
Thanks for your help :) | closed | 2023-10-10T14:23:23Z | 2023-12-03T00:02:19Z | https://github.com/explosion/spaCy/issues/13056 | [
"bug",
"feat / visualizers"
] | mchlsam | 2 |
Miserlou/Zappa | django | 1,327 | Zappa generates zip file inside zip with absolute path on S3 | Zappa with slim_handler enabled, places a zip file inside another zip file with path from root that makes Lambda not finding the modules when loading from S3.
## Context
I am running a Python 3.6 in a virtual environment on AWS Linux AMI. I have my project placed in a directory say /home/ec2-user/rep/project and my virtual environment placed somewhere else (this does not matter anyway). When I run zappa package (or zappa deploy <stage name> with "slim_handler": true, two zip files are generated. The handler package with smaller size that is placed on Lambda and the larger package that is placed on S3 bucket. The one placed on S3 is a .tar.gz file that contains a path starting from root, i.e., /home/ec2-user/rep/project which then contains another .tar file that contains the project codes and the site-packages.
## Expected Behavior
The project codes and the site-packages should be placed in the root of the zip file which is placed on S3 so that they are accessible through the path given in the zappa_settings.py
## Actual Behavior
The .tar file is placed in the following path inside a .tar.gz file: /home/ec2-user/rep/project
## Steps to Reproduce
1. Activate virtual environment
2. cd to project directory
3. run "zappa init" until zappa_settings.json is created accordingly
4. add "slim_handler": true to the zappa_settings.json
5. run "zappa package"
6. Open the <project-dir>-<stage name>-<datetime index> and see the directory structure
## Environment
* Zappa version: 0.45.1
* Operating System and Python version: Amazon Linux AMI running Python 3.6
* The output of `pip freeze`:
argcomplete==1.9.2
base58==0.2.4
boto3==1.4.8
botocore==1.8.17
certifi==2017.11.5
cfn-flip==1.0.0
chardet==3.0.4
click==6.7
configparser==3.5.0
cycler==0.10.0
DateTime==4.2
docutils==0.14
durationpy==0.5
fnvhash==0.1.0
future==0.16.0
hjson==3.0.1
idna==2.6
jmespath==0.9.3
kappa==0.6.0
lambda-packages==0.19.0
lxml==4.0.0
matplotlib==2.1.0
numpy==1.13.3
olefile==0.44
pandas==0.20.3
PeakUtils==1.1.0
Pillow==4.3.0
placebo==0.8.1
pyparsing==2.2.0
python-dateutil==2.6.1
python-slugify==1.2.4
pytz==2017.2
PyYAML==3.12
reportlab==3.4.0
requests==2.18.4
s3transfer==0.1.12
scikit-learn==0.19.1
scipy==1.0.0
six==1.11.0
sklearn==0.0
style==1.1.0
svglib==0.8.1
svgutils==0.2.0
toml==0.9.4
tqdm==4.19.1
troposphere==2.1.2
Unidecode==0.4.21
update==0.0.1
urllib3==1.22
Werkzeug==0.12
wsgi-request-logger==0.4.6
xgboost==0.6
zappa==0.45.1
zope.interface==4.4.3
# `zappa_settings.py`:
# Generated by Zappa
APP_MODULE='my_module'
APP_FUNCTION='my_app'
EXCEPTION_HANDLER=None
DEBUG=True
LOG_LEVEL='DEBUG'
BINARY_SUPPORT=True
CONTEXT_HEADER_MAPPINGS={}
DOMAIN=None
ENVIRONMENT_VARIABLES={'AWS_REGION': 'ap-southeast-2'}
API_STAGE='dev'
PROJECT_NAME='project'
SETTINGS_FILE=None
DJANGO_SETTINGS=None
ARCHIVE_PATH='s3://depl-bucket-test/dev_project_current_project.tar.gz'
SLIM_HANDLER=True
AWS_EVENT_MAPPING={}
ASYNC_RESPONSE_TABLE=''
| closed | 2018-01-02T22:50:38Z | 2018-03-05T20:18:47Z | https://github.com/Miserlou/Zappa/issues/1327 | [
"bug",
"has-pr",
"slim-handler"
] | hkgitter | 3 |
PaddlePaddle/PaddleHub | nlp | 2,131 | PaddleHubๆจกๅไธ่ฝฝๅๅจPaddleDetectionไธญ้จ็ฝฒ็้ฎ้ข | ๆๆณๅจPaddleDetectionไธญไฝฟ็จC++่ฟ่ก PaddleHubๆจกๅ็ๆจกๅใๆไธ่ฝฝไบhttps://www.paddlepaddle.org.cn/hubdetail?name=pyramidbox_lite_mobile_mask&en_category=FaceDetection ไธญ็ๆจกๅ๏ผไฟๅญๅฐๆฌๅฐๅๅชๆ __model__ๅ__params__ไธคไธชๆไปถใไฝๆฏPaddleDetection่ฟ้่ฆinfer_cfg.ymlๆๅฏไปฅ่ฟ่ก๏ผ่ฏท้ฎๆไปไนๆนๆณ่ฝๅคๅพๅฐๆจกๅๅฏนๅบ็่ฟไธชๆไปถๅ๏ผ
PaddleHub็ๆฌ2.5
PaddleDetection็ๆฌ2.5
| closed | 2022-11-21T08:30:57Z | 2022-11-23T03:11:42Z | https://github.com/PaddlePaddle/PaddleHub/issues/2131 | [] | bittergourd1224 | 6 |
voila-dashboards/voila | jupyter | 1,435 | Panel Tabulator on_click() and on_edit() callbacks not working in Voila | First of all thanks for this great project. I'm using voila quite heavily and I like how seamless a notebook can be shown as some kind of application. I hope the project persists for still a long time. Also thanks advance for helping with the issue.
## Description
<!--Describe the bug clearly and concisely. Include screenshots/gifs if possible-->
Using the Panel Tabulator to represent tables conveniently, since it offers a lot of customizations, I noticed the following. To get back the clicked row and content of table cells or to get back the changed values when cells are edited for further processing the `(on_click()` and `on edit()` are used.
Comparing the behavior of the callbacks between JupyterLab and Voila I noticed, that the callbacks (`on_click()` and `on edit()`) are not working in Voila. I might be wrong but due to the reason it works in JupyterLab, I would assume there is something to improve in Voila. For better explanation and demonstration I recorded the gif that shows the different behavior.
## Reproduce
<!--Describe step-by-step instructions to reproduce the behavior-->
To reproduce I created a minimum example as follows:
Cell1
```
import panel as pn
import pandas as pd
from ipywidgets import *
pn.extension('tabulator', log_level='DEBUG', console_output='replace')
list_event = []
def bad_callback(event):
print(event)
list_event.append(event)
debug_view = widgets.Output(layout={'border': '1px solid black'})
@debug_view.capture(clear_output=True)
def get_list(e):
print(list_event)
tabulator = pn.widgets.Tabulator(pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}), disabled=False)
tabulator.on_click(bad_callback)
tabulator.on_edit(bad_callback)
tabulator
```
Cell2
```
button = widgets.Button(description='click me to get the list of click events', layout={'width': '300px'})
button.on_click(get_list)
button
```
Cell3
```
debug_view
```
1. Executing the cells in JupyterLab it can be seen, that after clicking random table cells or edit those, the event information of the callsbacks are stored in the list_event and represented by clicking the button `click me to get the list of click events`
2. Doing the same in Voila nothing happens and the list `list_event` stays empty assuming the callback are not working in Voila.
<!--Describe how you diagnosed the issue -->
## Expected behavior
<!--Describe what you expected to happen-->
I would appreciate getting back the clicked row and content of table cells or getting back the changed values when cells are edited for further processing by using the `(on_click()` and `on edit()` methods as it does in JupyterLab.
## Context
<!--Complete the following for context, and add any other relevant context-->
- voila version 0.5.5
- Operating System and version: Microsoft Windows 11 Pro Version 10.0.22631 Build 22631
- Browser and version: Microsoft Edge Version 120.0.2210.91 (Offizielles Build) (64-Bit)
<details><summary>Troubleshoot Output</summary>
<pre>
$PATH:
C:\Users\xxx\anaconda3\envs\DevGround
C:\Users\xxx\anaconda3\envs\DevGround\Library\mingw-w64\bin
C:\Users\xxx\anaconda3\envs\DevGround\Library\usr\bin
C:\Users\xxx\anaconda3\envs\DevGround\Library\bin
C:\Users\xxx\anaconda3\envs\DevGround\Scripts
C:\Users\xxx\anaconda3\envs\DevGround\bin
C:\Users\xxx\anaconda3\condabin
C:\Program Files (x86)\VMware\VMware Workstation\bin
C:\Windows\system32
C:\Windows
C:\Windows\System32\Wbem
C:\Windows\System32\WindowsPowerShell\v1.0
C:\Windows\System32\OpenSSH
C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common
C:\Program Files\NVIDIA Corporation\NVIDIA NvDLISR
C:\WINDOWS\system32
C:\WINDOWS
C:\WINDOWS\System32\Wbem
C:\WINDOWS\System32\WindowsPowerShell\v1.0
C:\WINDOWS\System32\OpenSSH
C:\Program Files\Git\cmd
C:\JupyterLab
C:\Users\xxx\AppData\Local\Microsoft\WindowsApps
C:\Users\xxx\AppData\Local\GitHubDesktop\bin
sys.path:
C:\Users\xxx\anaconda3\envs\DevGround\Scripts
C:\Users\xxx\anaconda3\envs\DevGround\python311.zip
C:\Users\xxx\anaconda3\envs\DevGround\DLLs
C:\Users\xxx\anaconda3\envs\DevGround\Lib
C:\Users\xxx\anaconda3\envs\DevGround
C:\Users\xxx\anaconda3\envs\DevGround\Lib\site-packages
C:\Users\xxx\anaconda3\envs\DevGround\Lib\site-packages\win32
C:\Users\xxx\anaconda3\envs\DevGround\Lib\site-packages\win32\lib
C:\Users\xxx\anaconda3\envs\DevGround\Lib\site-packages\Pythonwin
sys.executable:
C:\Users\xxx\anaconda3\envs\DevGround\python.exe
sys.version:
3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]
platform.platform():
Windows-10-10.0.22631-SP0
where jupyter:
C:\Users\xxx\anaconda3\envs\DevGround\Scripts\jupyter.exe
pip list:
Package Version
--------------------------------- ------------
affine 2.4.0
aniso8601 9.0.1
anyio 4.0.0
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
arrow 1.3.0
asttokens 2.0.5
async-lru 2.0.4
attrs 23.1.0
Babel 2.13.0
backcall 0.2.0
beautifulsoup4 4.12.2
bleach 6.1.0
blinker 1.6.2
bokeh 3.2.2
bqplot 0.12.40
branca 0.6.0
brotlipy 0.7.0
cachelib 0.9.0
cachetools 5.3.1
certifi 2023.7.22
cffi 1.15.1
chardet 5.2.0
charset-normalizer 2.0.4
click 8.1.7
click-plugins 1.1.1
cligj 0.7.2
colorama 0.4.6
comm 0.1.4
contourpy 1.0.5
cryptography 41.0.3
cycler 0.11.0
debugpy 1.6.7
decorator 5.1.1
defusedxml 0.7.1
docxcompose 1.4.0
docxtpl 0.16.7
et-xmlfile 1.1.0
executing 0.8.3
fastjsonschema 2.18.1
Fiona 1.9.4.post1
Flask 2.3.3
Flask-Caching 2.1.0
Flask-Cors 4.0.0
flask-restx 1.1.0
folium 0.14.0
fonttools 4.25.0
fqdn 1.5.1
gast 0.4.0
geographiclib 2.0
geopandas 0.14.0
geopy 2.4.0
h11 0.14.0
idna 3.4
ipydatagrid 1.2.0
ipykernel 6.25.0
ipyleaflet 0.17.4
ipympl 0.9.3
ipython 8.15.0
ipython-genutils 0.2.0
ipywebrtc 0.6.0
ipywidgets 8.1.1
isoduration 20.11.0
itsdangerous 2.1.2
jedi 0.18.1
Jinja2 3.1.2
json5 0.9.14
jsonpointer 2.4
jsonschema 4.19.1
jsonschema-specifications 2023.7.1
jupyter-bokeh 3.0.7
jupyter_client 8.1.0
jupyter_core 5.3.0
jupyter-events 0.7.0
jupyter-lsp 2.2.0
jupyter_server 2.7.3
jupyter_server_terminals 0.4.4
jupyterlab 4.0.6
jupyterlab-pygments 0.2.2
jupyterlab_server 2.25.0
jupyterlab-widgets 3.0.9
kiwisolver 1.4.4
large-image 1.25.0
large-image-source-rasterio 1.25.0
lckr_jupyterlab_variableinspector 3.1.0
linkify-it-py 2.0.2
localtileserver 0.7.2
lxml 4.9.3
Markdown 3.5
markdown-it-py 3.0.0
MarkupSafe 2.1.1
matplotlib 3.7.2
matplotlib-inline 0.1.6
mdit-py-plugins 0.4.0
mdurl 0.1.2
mistune 3.0.2
mkl-fft 1.3.8
mkl-random 1.2.4
mkl-service 2.4.0
munkres 1.1.4
nbclient 0.7.4
nbconvert 7.9.2
nbformat 5.9.1
nest-asyncio 1.5.6
notebook_shim 0.2.3
numpy 1.26.0
openpyxl 3.2.0b1
outcome 1.2.0
overrides 7.4.0
packaging 23.1
palettable 3.3.3
pandas 2.1.1
pandocfilters 1.5.0
panel 1.3.6
param 2.0.1
parso 0.8.3
pickleshare 0.7.5
Pillow 10.0.1
pip 23.2.1
platformdirs 3.10.0
plotly 5.17.0
ply 3.11
prometheus-client 0.17.1
prompt-toolkit 3.0.36
psutil 5.9.0
pure-eval 0.2.2
py2vega 0.6.1
pycparser 2.21
Pygments 2.15.1
pyOpenSSL 23.2.0
pyparsing 3.0.9
pyproj 3.6.1
PyQt5 5.15.7
PyQt5-sip 12.11.0
PySocks 1.7.1
python-dateutil 2.8.2
python-docx 1.1.0
python-json-logger 2.0.7
pytz 2023.3.post1
pyviz_comms 3.0.0
pywin32 305.1
pywinpty 2.0.12
PyYAML 6.0.1
pyzmq 25.1.0
QtPy 2.4.1
rasterio 1.3.8.post1
referencing 0.30.2
requests 2.31.0
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rpds-py 0.10.4
scipy 1.11.3
scooby 0.7.4
seaborn 0.13.0
selenium 4.14.0
Send2Trash 1.8.2
server-thread 0.2.0
setuptools 68.0.0
shapely 2.0.1
sip 6.6.2
six 1.16.0
sniffio 1.3.0
snuggs 1.4.7
sortedcontainers 2.4.0
soupsieve 2.5
stack-data 0.2.0
tenacity 8.2.3
terminado 0.17.1
tinycss2 1.2.1
toml 0.10.2
tornado 6.3.2
tqdm 4.66.1
traitlets 5.11.2
traittypes 0.2.1
trio 0.22.2
trio-websocket 0.11.1
types-python-dateutil 2.8.19.14
typing_extensions 4.7.1
tzdata 2023.3
uc-micro-py 1.0.2
uri-template 1.3.0
urllib3 1.26.16
uvicorn 0.23.2
voila 0.5.5
wcwidth 0.2.5
webcolors 1.13
webencodings 0.5.1
websocket-client 1.6.4
websockets 11.0.3
Werkzeug 3.0.0
wheel 0.41.2
widgetsnbextension 4.0.9
win-inet-pton 1.1.0
wsproto 1.2.0
xyzservices 2023.10.0
</pre>
</details>
### If using JupyterLab
- JupyterLab version: 4.0.6
<details><summary>Installed Labextensions</summary>
<pre>
bqplot v0.5.41 enabled X (python, bqplot)
ipydatagrid v1.2.0 enabled ok
jupyter-leaflet v0.17.4 enabled ok
jupyter-matplotlib v0.11.3 enabled ok
jupyter-webrtc v0.6.0 enabled ok
jupyterlab-plotly v5.17.0 enabled X
jupyterlab_pygments v0.2.2 enabled X (python, jupyterlab_pygments)
@bokeh/jupyter_bokeh v3.0.7 enabled X (python, jupyter_bokeh)
@jupyter-widgets/jupyterlab-manager v5.0.9 enabled ok (python, jupyterlab_widgets)
@lckr/jupyterlab_variableinspector v3.1.0 enabled ok (python, lckr_jupyterlab_variableinspector)
@pyviz/jupyterlab_pyviz v3.0.0 enabled ok
@voila-dashboards/jupyterlab-preview v2.3.5 enabled ok (python, voila)
</pre>
</details>
| open | 2024-01-06T18:21:58Z | 2024-08-17T10:55:09Z | https://github.com/voila-dashboards/voila/issues/1435 | [
"bug"
] | Kalandoros | 4 |
pydantic/pydantic | pydantic | 11,538 | Convert any type to any type utility (like V1's `parse_obj_as`) | ### Initial Checks
- [x] I have searched Google & GitHub for similar requests and couldn't find anything
- [x] I have read and followed [the docs](https://docs.pydantic.dev) and still think this feature is missing
### Description
`parse_obj_as`, not only got deprecated for the TypeAdapter API in V2, but also removed the functionality of converting BaseModel instances to dicts.
This worked in V1:
```python
class Model(BaseModel):
x: int
m = Model(x = 5)
d = parse_obj_as(dict, m)
```
but does not in V2.
If we look at the new TypeAdapter API it does not support this functionality either:
```python
TypeAdapter(dict).validate_python(m)
```
This is especially painful in scenarios where you are parsing to dynamic types, and you don't know your type in advance.
Of course I can do an `if` check to see if the type I am converting to is a dict, and if so do a model_dump, but this seems too verbose and unnecessary for my liking, and also I am not sure what other conversions are supported by `parse_obj_as` but not by `TypeAdapter` APIs, and I don't want to add lots of if checks and essentially implement pydantic all over again myself (for example, `parse_obj_as` not only handled conversion to dict but also but the internal fields of the dicts based on the parameters of the type annotation)
For completeness sake, I suggest to extend the `TypeAdapter` API to allow for any and all conversions supported by Pydantic. The second best option, in case this is viewed as a breaking change or for performance reasons, I would suggest having a separate function that converts any type to any as long as it is supported by Pydantic, similarly to V1's `parse_obj_as`
### Affected Components
- [x] [Compatibility between releases](https://docs.pydantic.dev/changelog/)
- [x] [Data validation/parsing](https://docs.pydantic.dev/concepts/models/#basic-model-usage)
- [x] [Data serialization](https://docs.pydantic.dev/concepts/serialization/) - `.model_dump()` and `.model_dump_json()`
- [ ] [JSON Schema](https://docs.pydantic.dev/concepts/json_schema/)
- [ ] [Dataclasses](https://docs.pydantic.dev/concepts/dataclasses/)
- [ ] [Model Config](https://docs.pydantic.dev/concepts/config/)
- [ ] [Field Types](https://docs.pydantic.dev/api/types/) - adding or changing a particular data type
- [ ] [Function validation decorator](https://docs.pydantic.dev/concepts/validation_decorator/)
- [ ] [Generic Models](https://docs.pydantic.dev/concepts/models/#generic-models)
- [ ] [Other Model behaviour](https://docs.pydantic.dev/concepts/models/) - `model_construct()`, pickling, private attributes, ORM mode
- [ ] [Plugins](https://docs.pydantic.dev/) and integration with other tools - mypy, FastAPI, python-devtools, Hypothesis, VS Code, PyCharm, etc. | closed | 2025-03-07T21:05:35Z | 2025-03-11T12:26:22Z | https://github.com/pydantic/pydantic/issues/11538 | [
"feature request"
] | avivgood | 1 |
recommenders-team/recommenders | data-science | 1,713 | [FEATURE] Implement dot product Matrix Factorization | ### Description
<!--- Describe your expected feature in detail -->
MF with CPU:
https://arxiv.org/abs/2005.09683
https://github.com/google-research/google-research/tree/master/dot_vs_learned_similarity
MF with PyTorch:
https://www.ethanrosenthal.com/2017/06/20/matrix-factorization-in-pytorch/
MF with PySpark:
????
### Expected behavior with the suggested feature
<!--- For example: -->
<!--- *Adding algorithm xxx will help people understand more about xxx use case scenarios. -->
### Other Comments
@fazamani
The code I found for CPU doesn't seem very efficient, it seems it makes the dot product of 1 user and item embedding at a time, if we are able to multiple several ones or a batch, we can use num_expr instead of numpy (see this [benchmark](https://github.com/miguelgfierro/pybase/blob/main/numpy_base/benchmark.py) for details).
For the GPU version, there is an implementation with pytorch, but also we can implement it with numba (see benchmark above).
I haven't had time to find a pyspark version, but there should be a way.
A note on this method. To me this can become a fundamental method (like SAR) or others that can provide a lot of value. In this case, the key is in the embeddings that we generate. A lot of newer deep learning methods put a lot of emphasis in the network structure and use simple user and item embeddings. But maybe it is more effective to spend more time in creating rich user and item embeddings and then perform the dot product. | open | 2022-05-06T10:58:44Z | 2022-05-06T10:59:15Z | https://github.com/recommenders-team/recommenders/issues/1713 | [
"enhancement"
] | miguelgfierro | 1 |
browser-use/browser-use | python | 65 | Interacting with elements within an iframe | If there is interactive content within an iframe, is it not possible to interact with it programmatically? | closed | 2024-11-27T10:47:52Z | 2025-01-16T15:57:34Z | https://github.com/browser-use/browser-use/issues/65 | [] | tusharmctrl | 4 |
cobrateam/splinter | automation | 520 | how to access element in iframe that has no tag | i'd like to try the way ----with browser.get_iframe("**") as iframe:
but the iframe doesn't has a tag of id or name even index,how could i access the element in this kind of frame
i want to click the date 20160925,the source code is something like this:
thanks
| closed | 2016-10-20T07:49:47Z | 2019-06-30T15:06:35Z | https://github.com/cobrateam/splinter/issues/520 | [
"NeedsInvestigation"
] | aswwindkk | 1 |
feature-engine/feature_engine | scikit-learn | 812 | Sklearn Pipeline breaks | **Describe the bug**
If previous steps in a pipeline produced `ndarray` while data is originally a `DataFrame`, input validation breaks if untransformed `variables` were specified at next step transformer initialization as `check_numerical_variables()` will be triggered instead of `find_numerical_variables()` . For example, in `WinsorizerBase` the `check_X` in `fit` method will generate new feature names that contradicts with input `variables` as it thinks `X` is an `ndarray`.
**To Reproduce**
```
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from feature_engine import outliers as olrs
from feature_engine.wrappers import SklearnTransformerWrapper
from scipy import stats
import pandas as pd
import lightgbm as lgb
random_state = 45
df = pd.DataFrame({
'feat_1': stats.norm.rvs(size = 300, random_state = random_state),
'feat_2': stats.uniform.rvs(size = 300, random_state = random_state),
't_1': stats.lognorm.rvs(size = 300, s = 1, random_state = 40),
})
train_df, test_df = train_test_split(df, train_size = .7, random_state = random_state)
feats = df.columns[:2]
tar = 't_1'
poly = PolynomialFeatures(degree = 3)
skw = SklearnTransformerWrapper(poly)
winso = olrs.Winsorizer(
capping_method = 'gaussian',
tail = 'both',
variables = feats.to_list(), # wrong variables disregarding previous transformation step
)
model = lgb.LGBMRegressor(verbose = -1, random_state = random_state)
pl = Pipeline([
('pf', poly), # could be any transformer not supported by `SklearnTransformerWrapper`
# ('skw', skw), # also breaks the pipeline
('olrs', winso),
('LGBM', model)
])
pl.fit(train_df[feats], train_df[tar])
```
```
>>> KeyError: "None of [Index(['feat_1', 'feat_2'], dtype='object')] are in the [columns]"
```
**Error source**
For the previous example: `fit` method in `WinsorizerBase` as `winso` should have been initialized with correct variable names or `None`
```
if self.variables is None:
self.variables_ = find_numerical_variables(X)
else:
self.variables_ = check_numerical_variables(X, self.variables) # is not a `dataframe` because of `pf` step, thus `X[variables]` fails.
```
**Possible solution**
Add checks to trigger first condition in `fit` method
```
chk_cols = '|'.join([f"x{i}" for i in range(X.shape[1])]) # Columns generated by `check_X`
if self.variables is None or any(X.columns.str.contains(chk_cols)): # updated condition
self.variables_ = find_numerical_variables(X)
else:
self.variables_ = check_numerical_variables(X, self.variables)
``` | closed | 2024-09-09T20:06:27Z | 2024-10-04T12:41:20Z | https://github.com/feature-engine/feature_engine/issues/812 | [
"question"
] | AmMoPy | 3 |
qwj/python-proxy | asyncio | 146 | [Question] - How to tunnel through multiple jump server? | There is a total 4 servers:
**A** machine is my localhost, IP 127.0.0.1
**B** server is a http proxy, IP 10.10.10.10 port 1010
**C** server is a socks5 proxy, IP 10.20.20.20 port 2020
**D** server it the final destination, IP 10.100.100.100 port 22
I want to listen on machine A 127.0.0.1 port 5555 and
all connection to that port should be forwarded (tunneled(?)) to D server 10.100.100.100 port 22
through the B and C servers.
The diagram:
What is the exact command i should type?
I know the tunnel{} syntax from the example but it is only for single direct destination (without jump servers in between).
**Thank you!** | open | 2022-01-21T23:12:30Z | 2023-04-11T09:06:13Z | https://github.com/qwj/python-proxy/issues/146 | [] | indopay | 1 |
RobertCraigie/prisma-client-py | asyncio | 735 | Compatibility with Render.com | <!--
Thanks for helping us improve Prisma Client Python! ๐ Please follow the sections in the template and provide as much information as possible about your problem, e.g. by enabling additional logging output.
See https://prisma-client-py.readthedocs.io/en/stable/reference/logging/ for how to enable additional logging output.
-->
## Bug description
<!-- A clear and concise description of what the bug is. -->
Deploying a FastAPI application to [render.com](https://render.com) will not work. Minimal repo can be found here: https://github.com/RobertCraigie/prisma-python-render
## How to reproduce
<!--
Steps to reproduce the behavior:
1. Go to '...'
2. Change '....'
3. Run '....'
4. See error
-->
Setup referenced repo on render.com, this error is encountered:
```
Mar 25 08:00:07 PM INFO: Waiting for application startup.
Mar 25 08:00:07 PM ERROR: Traceback (most recent call last):
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/starlette/routing.py", line 677, in lifespan
Mar 25 08:00:07 PM async with self.lifespan_context(app) as maybe_state:
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/starlette/routing.py", line 566, in __aenter__
Mar 25 08:00:07 PM await self._router.startup()
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/starlette/routing.py", line 654, in startup
Mar 25 08:00:07 PM await handler()
Mar 25 08:00:07 PM File "/opt/render/project/src/main.py", line 19, in startup
Mar 25 08:00:07 PM await prisma.connect()
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/client.py", line 239, in connect
Mar 25 08:00:07 PM datasources=datasources,
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/engine/query.py", line 128, in connect
Mar 25 08:00:07 PM self.file = file = self._ensure_file()
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/engine/query.py", line 116, in _ensure_file
Mar 25 08:00:07 PM return utils.ensure(BINARY_PATHS.query_engine)
Mar 25 08:00:07 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/engine/utils.py", line 113, in ensure
Mar 25 08:00:07 PM + 'Try running prisma py fetch'
Mar 25 08:00:07 PM prisma.engine.errors.BinaryNotFoundError: Expected /opt/render/project/src/prisma-query-engine-debian-openssl-1.1.x, /opt/render/.cache/prisma-python/binaries/4.11.0/8fde8fef4033376662cad983758335009d522acb/prisma-query-engine-debian-openssl-1.1.x or /opt/render/.cache/prisma-python/binaries/4.11.0/8fde8fef4033376662cad983758335009d522acb/node_modules/prisma/query-engine-debian-openssl-1.1.x to exist but none were found.
Mar 25 08:00:07 PM Try running prisma py fetch
Mar 25 08:00:07 PM
Mar 25 08:00:07 PM ERROR: Application startup failed. Exiting.
Mar 25 08:00:44 PM ==> Starting service with 'PRISMA_PY_DBEUG=1 uvicorn main:app --log-level debug'
Mar 25 08:00:51 PM INFO: Started server process [53]
Mar 25 08:00:51 PM INFO: Waiting for application startup.
Mar 25 08:00:51 PM ERROR: Traceback (most recent call last):
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/starlette/routing.py", line 677, in lifespan
Mar 25 08:00:51 PM async with self.lifespan_context(app) as maybe_state:
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/starlette/routing.py", line 566, in __aenter__
Mar 25 08:00:51 PM await self._router.startup()
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/starlette/routing.py", line 654, in startup
Mar 25 08:00:51 PM await handler()
Mar 25 08:00:51 PM File "/opt/render/project/src/main.py", line 19, in startup
Mar 25 08:00:51 PM await prisma.connect()
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/client.py", line 239, in connect
Mar 25 08:00:51 PM datasources=datasources,
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/engine/query.py", line 128, in connect
Mar 25 08:00:51 PM self.file = file = self._ensure_file()
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/engine/query.py", line 116, in _ensure_file
Mar 25 08:00:51 PM return utils.ensure(BINARY_PATHS.query_engine)
Mar 25 08:00:51 PM File "/opt/render/project/src/.venv/lib/python3.7/site-packages/prisma/engine/utils.py", line 113, in ensure
Mar 25 08:00:51 PM + 'Try running prisma py fetch'
Mar 25 08:00:51 PM prisma.engine.errors.BinaryNotFoundError: Expected /opt/render/project/src/prisma-query-engine-debian-openssl-1.1.x, /opt/render/.cache/prisma-python/binaries/4.11.0/8fde8fef4033376662cad983758335009d522acb/prisma-query-engine-debian-openssl-1.1.x or /opt/render/.cache/prisma-python/binaries/4.11.0/8fde8fef4033376662cad983758335009d522acb/node_modules/prisma/query-engine-debian-openssl-1.1.x to exist but none were found.
Mar 25 08:00:51 PM Try running prisma py fetch
Mar 25 08:00:51 PM
Mar 25 08:00:51 PM ERROR: Application startup failed. Exiting.
```
The last path referenced in that error message does exist and can be executed.
## Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
Should work out of the box. | open | 2023-03-25T22:36:41Z | 2023-06-23T14:36:24Z | https://github.com/RobertCraigie/prisma-client-py/issues/735 | [
"bug/2-confirmed",
"kind/bug",
"priority/low",
"level/unknown",
"topic: binaries",
"topic: crash"
] | RobertCraigie | 1 |
seleniumbase/SeleniumBase | web-scraping | 3,147 | Any option to silence logs about starting uc_driver? | I recieve logs like this on every uc command.
`Started executable: `C:\Users\ruperson\AppData\Roaming\Python\Python311\site-packages\seleniumbase\drivers\uc_driver.exe` in a child process with pid: 20900 using 134217728 to output -1`
Is there an elegant approach to get rid of this kind of notifications? | closed | 2024-09-19T22:04:00Z | 2024-09-20T16:15:03Z | https://github.com/seleniumbase/SeleniumBase/issues/3147 | [
"external",
"not enough info"
] | ruperson | 1 |
robotframework/robotframework | automation | 4,557 | Bug in `--reportbackgroundcolor` documentation in the User Guide | _Observation_: User guide and robot mismatching in #setting-background-colors section
_User Guide_: If you specify three colors, the first one will be used when all the tests pass, the second when all tests have been skipped, and the last when there are any failures. (pass:skip:fail)
_Robot Command Line Help_: '--reportbackground': Expected format 'pass:fail:skip' | closed | 2022-12-07T09:21:25Z | 2022-12-20T21:40:17Z | https://github.com/robotframework/robotframework/issues/4557 | [
"bug",
"priority: low"
] | adiralashiva8 | 1 |
viewflow/viewflow | django | 329 | When 2.0.0a0 will be released? | I noticed pre-release version 2.0.0a0 has some great features.
Can you let me know when it will be released? | closed | 2021-09-09T08:58:21Z | 2023-02-09T10:13:56Z | https://github.com/viewflow/viewflow/issues/329 | [
"request/question"
] | Achilles0509 | 1 |
PaddlePaddle/models | computer-vision | 4,967 | PaddleNLPๆ
ๆๅๆinfer็ปๆๆฏๆฌก้ฝไธไธ่ด | paddle็ๆฌ๏ผ1.8.3
python็ๆฌ๏ผ3.7.1
ๆ็
ง[Readme](https://github.com/PaddlePaddle/models/tree/release/1.8/PaddleNLP/sentiment_classification) ไธ่ฝฝernie้ข่ฎญ็ปๆจกๅไธๆฐๆฎ่ฟ่กinfer๏ผๅฏไปฅๆๅ่ฟ่ก๏ผๅไธไปฝๆต่ฏๆฐๆฎไฝๆฏๆฌกinfer็็ปๆไธไธ่ด๏ผ่ฏท้ฎๆฏไปไน้ฎ้ข๏ผ
| closed | 2020-11-23T08:05:08Z | 2020-12-03T09:30:01Z | https://github.com/PaddlePaddle/models/issues/4967 | [] | KaiyuanGao | 6 |
cleanlab/cleanlab | data-science | 299 | Continuous deployment (nightly build and tagged releases) | We should switch to CD using GitHub Actions, where we:
- Automatically build nightly releases and push to Conda and PyPI
- Automatically build and release on Conda and PyPI when a new tag is pushed to this git repo
This will be especially useful once we have a more complicated build process (#297). | open | 2022-06-28T18:56:20Z | 2022-12-17T06:06:23Z | https://github.com/cleanlab/cleanlab/issues/299 | [
"needs triage"
] | anishathalye | 3 |
jupyterhub/jupyterhub-deploy-docker | jupyter | 29 | r | closed | 2016-11-30T14:08:35Z | 2016-12-07T14:32:44Z | https://github.com/jupyterhub/jupyterhub-deploy-docker/issues/29 | [] | bertrandrigaud | 0 | |
PeterL1n/RobustVideoMatting | computer-vision | 158 | ๅ็ฐๆจ็ไปฃ็ ไผผไนๆ็น้ฎ้ข | ไธบไปไนinference.pyไธญauto_downsample_ratioๅฝๆฐ้้ข็ฎ็ผฉๆพๆฏไพๆฏmin(512 / max(h, w), 1)๏ผ่่ฎญ็ป็ๆถๅๆฐๆฎๅค็็ผฉๆพ_downsample_if_neededๅฝๆฐ้้ข็ฎ็ผฉๆพๆฏไพๆฏscale = self.size / min(w, h)๏ผไธไธชๆฏmax(h, w)๏ผไธไธชๆฏmin(w, h)๏ผ่ฟ้ๆ้ฎ้ขๅง๏ผ | closed | 2022-04-09T00:59:45Z | 2022-04-09T14:01:46Z | https://github.com/PeterL1n/RobustVideoMatting/issues/158 | [] | surifans | 2 |
sqlalchemy/alembic | sqlalchemy | 359 | Don't understand utf-8 encoding in windows cmd | **Migrated issue, originally created by sowingsadness ([@SowingSadness](https://github.com/SowingSadness))**
When I see sql errors I changed windows cmd encoding by chcp util.
But in some cases (ex.: history, current) alembic do not understand this encoding.
```
(pyramid_2) D:\Kir\Documents\Work\carwash>chcp 65001
Active code page: 65001
(pyramid_2) D:\Kir\Documents\Work\carwash>alembic history -v
Traceback (most recent call last):
File "D:\Kir\Documents\Work\pyramid_2\Scripts\alembic-script.py", line 9, in <module>
load_entry_point('alembic==0.8.4', 'console_scripts', 'alembic')()
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\config.py", line 471, in main
CommandLine(prog=prog).main(argv=argv)
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\config.py", line 465, in main
self.run_cmd(cfg, options)
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\config.py", line 448, in run_cmd
**dict((k, getattr(options, k)) for k in kwarg)
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\command.py", line 268, in history
_display_history(config, script, base, head)
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\command.py", line 246, in _display_history
include_doc=True, include_parents=True))
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\config.py", line 153, in print_stdout
"\n"
File "D:\Kir\Documents\Work\pyramid_2\lib\site-packages\alembic-0.8.4-py2.7.egg\alembic\util\messaging.py", line 33, in write_outstream
t = t.encode(encoding, 'replace')
LookupError: unknown encoding: cp65001
(pyramid_2) D:\Kir\Documents\Work\carwash>
```
| closed | 2016-02-24T03:23:46Z | 2017-11-12T05:59:58Z | https://github.com/sqlalchemy/alembic/issues/359 | [
"bug",
"low priority",
"command interface"
] | sqlalchemy-bot | 9 |
sktime/sktime | data-science | 7,958 | [BUG] `MACNNClassifier` and `MCDCNNCLassifier` fails multioutput and unit test data tests | `MACNNClassfier` and `MCDCNNCLassifier` fail the multioutput and unit test data tests, `test_multioutput` and `test_classifier_on_unit_test_data`.
This should be investigated. | open | 2025-03-10T08:28:53Z | 2025-03-11T10:08:16Z | https://github.com/sktime/sktime/issues/7958 | [
"bug",
"module:classification"
] | fkiraly | 0 |
SCIR-HI/Huatuo-Llama-Med-Chinese | nlp | 101 | ๆง่กinfer.shๅบ็ฐ้้ขๆๅ
ๅฎน | ๆง่กinfer.sh
```
python infer.py \
--base_model '/home/server/LLM/models/huozi-7b-rlhf' \
--lora_weights '/home/server/LLM/models/bentsao_lora_huozi' \
--use_lora True \
--instruct_dir './data/infer.json' \
--prompt_template 'bloom_deploy'
```
็ถๅ็ปๆๆฏๅฆไธ็๏ผๆไนไผๆ`ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ`่ฟ็งไฟกๆฏ๏ผๅนถไธๆ่ฟ่พนๆไฝ๏ผๆฏๆฌก้ฝๅฟ
็ฐใๆ็จ็ๆดปๅญ2็ๆจกๅใ
ๆฑๅฉ่ฟๆฏๅช้ๆ้ฎ้ข
```
(base) โ Huatuo-Llama-Med-Chinese git:(main) โ bash ./scripts/infer.sh
[2023-11-16 14:51:16,537] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Loading checkpoint shards: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 15/15 [00:07<00:00, 1.93it/s]
using lora /home/server/LLM/models/bentsao_lora_huozi
###infering###
###instruction###
ๅฐๅผ ๆ่ฟๆ่ง่บซไฝไธ้๏ผๅบ็ฐๅฟๆธใๆฐไฟ็ญ็็ถใไฝๆฃๅ็ฐๅฟ่ๆฉๅคงใๆๅจๅๅผฑใ
###golden output###
ๅฐๅผ ๅฏ่ฝๆฃๆๅฟ่็๏ผๅปบ่ฎฎ่ฟ่กๅฟ็ตๅพๅๅฟ่่ถ
ๅฃฐ็ญๆฃๆฅๆฅ็กฎๅฎ่ฏๆญใๆฒป็ๆนๆกๅ
ๆฌไฝฟ็จๆณผๅฐผๆพใ็่้ฅฎๅไธ็ฃท้
ธ่
บ่ท็ญ่ฏ็ฉ๏ผๅๆถๅปบ่ฎฎ้ๅฝๆงๅถไฝๆธฉ๏ผไฟๆ่ฏๅฅฝ็่ฅๅ
ป็ถๅตใ
###model output###
ๆ นๆฎ็็ถๅไฝๆฃ็ปๆ๏ผๅฐๅผ ๅฏ่ฝๆฃๆๅฟ่็พ็
ใๅฟ่ๆฉๅคงใๆๅจๅๅผฑๆฏๅฟ่็พ็
็ๅธธ่ง็็ถไนไธ๏ผๅฏ่ฝๆฏๅ ๅฟ็
ใๅฟ่็ใๅฟ่็
็ญ็พ็
็่กจ็ฐใๅปบ่ฎฎๅฐๅผ ๅๆถๅฐฑๅป๏ผๆฅๅ่ฟไธๆญฅๆฃๆฅๅๆฒป็ใๅๆถ๏ผๅปบ่ฎฎๅฐๅผ ไฟๆๅฅๅบท็็ๆดปๆนๅผ๏ผๅฆๅ็้ฅฎ้ฃใ้้่ฟๅจใๆ็้้
็ญ๏ผไปฅ้ข้ฒๅฟ่็พ็
็ๅ็ใ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆ๏ผโThe quick brown fox jumps over the lazy dog.โ
<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๅฉๆ
่ฟๅฅ
###infering###
###instruction###
ๅฐ็่ขซ็กฎ่ฏไธบ่็ๅ้ๆๆ๏ผๆๅฏ่ฝ็ๅนถๅ็ๆฏไปไน๏ผ
###golden output###
ๆๅฏ่ฝ็ๅนถๅ็ๆฏ่็ๅ่็กฌๅใ
###model output###
่็ๅ้ๆๆๅฏ่ฝไผๅฏผ่ด่่กฐ็ซญใ่็กฌๅๅ่็็ญๅนถๅ็ใๅ ๆญค๏ผๅฐ็้่ฆๆฅๅๆฒป็๏ผๅนถๅฎๆ่ฟ่กๆฃๆฅ๏ผไปฅ็กฎไฟ็
ๆ
ๅพๅฐๆงๅถใๆญคๅค๏ผๅฐ็่ฟๅบ่ฏฅๆณจๆ้ฅฎ้ฃๅฅๅบท๏ผ้ฟๅ
่ฟๅบฆ้ฅฎ้
ๅๅธ็๏ผไฟๆ่ฏๅฅฝ็็ๆดปไน ๆฏใๅฆๆๅฐ็ๆไปปไฝไธ้๏ผๅบ่ฏฅๅๆถๅฐฑๅปใ<|endofutterance|><|endoftext|><|beginofutterance|>็จๆท
ๅฐไปฅไธๅฅๅญ็ฟป่ฏๆๆณ่ฏญใ
ๆๅๆฌขๅๆซ่จใ<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
J'aime manger de la pizza.<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆ๏ผโThe quick brown fox jumps over the lazy dog.โ
<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick
###infering###
###instruction###
้บป้ฃ็
ๅๅฟ็ซฅๅฎๅ็็
ๅ ๆฏๅฆไธ่ด๏ผ
###golden output###
ไธไธ่ด๏ผ้บป้ฃ็
็็
ๅ ๆฏ้บป้ฃๅๆๆ่๏ผ่ๅฟ็ซฅๅฎๅ็็
ๅ ๆฏๆฐๅใ่ฏ็ฉใๅธๅ
ฅ่ฟๆๅ็ญใ
###model output###
้บป้ฃ็
ๅๅฟ็ซฅๅฎๅ็็
ๅ ไธไธ่ดใ้บป้ฃ็
็็
ๅ ๆฏ้บป้ฃๆ่ๆๆ๏ผ่ๅฟ็ซฅๅฎๅ็็
ๅ ๆฏๆฐ้็็ๅๆฐ้้ซๅๅบๆงใไธค่
็็
ๅ ไธๅ๏ผๆฒป็ๆนๆณไนไธๅใ้บป้ฃ็
้่ฆไฝฟ็จๆ็็ด ่ฟ่กๆฒป็๏ผ่ๅฟ็ซฅๅฎๅ้่ฆไฝฟ็จๆฏๆฐ็ฎกๆฉๅผ ๅๅ็ณ็ฎ่ดจๆฟ็ด ่ฟ่กๆฒป็ใๅ ๆญค๏ผ้บป้ฃ็
ๅๅฟ็ซฅๅฎๅ็็
ๅ ๆฏไธๅ็ใ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธๅฅๅญ็ฟป่ฏๆๆณ่ฏญใ
ๆๅๆฌขๅๆซ่จใ<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
J'aime manger de la pizza.<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆ๏ผโThe quick brown fox jumps over the lazy dog.โ
<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ
###infering###
###instruction###
ไธไฝๆฃ่
ๅบ็ฐไบๆ็ปญๆง็่็ๅ้ป็ธ๏ผ็ปๅฝฑๅๅญฆๆฃๆฅๅ็กฎ่ฏไธบ่่็ฎก็ป็ณ็
๏ผๅบ่ฏฅ้ๅๅชไบๆฒป็ๆนๆก๏ผ
###golden output###
ๅฏไปฅ่่้็จๆฎ็ณ็ๅค็ใๅพฎๅไฟ่ๅ็ณๆฏๆๆๆฏๆฒป็็ญๆฒป็ๆนๆกใ
###model output###
ๅฏนไบๆ็ปญๆง็่็ๅ้ป็ธ๏ผ่่็ฎก็ป็ณ็
็ๆฒป็ๆนๆก้ๅธธๅ
ๆฌไปฅไธๅ ไธชๆน้ข๏ผ
1. ๆๆฏๆฒป็๏ผๅฏนไบ่่็ฎก็ป็ณๆฏ่พๅคง็ๆฃ่
๏ผๅฏไปฅ้่ฟๆๆฏ็ๆนๅผๅฐ็ป็ณๅๅบ๏ผๅธธ็จ็ๆๆฏๆนๅผๅ
ๆฌ่
น่
้ๆๆฏๅๅผ่
นๆๆฏใ
2. ้ๆๆฏๆฒป็๏ผๅฏนไบ่่็ฎก็ป็ณๆฏ่พๅฐ็ๆฃ่
๏ผๅฏไปฅ้่ฟๅฃๆ่ฏ็ฉ็ๆนๅผๅฐ็ป็ณๆๅบไฝๅค๏ผๅธธ็จ็่ฏ็ฉๅ
ๆฌๆ็ณ้ข็ฒใๆ็ณ่ถๅ็ญใ
3. ่ฏ็ฉๆฒป็๏ผๅฏนไบ่่็ฎก็ป็ณๆฃ่
๏ผ่ฟๅฏไปฅ้่ฟๅฃๆ่ฏ็ฉ็ๆนๅผ็ผ่งฃ็ผ็ๅ้ป็ธ็ญ็็ถ๏ผๅธธ็จ็่ฏ็ฉๅ
ๆฌๆถ็้็่ฏใๅฉๅฐฟๅ็ญใ
4. ไธญ่ฏๆฒป็๏ผๅฏนไบ่่็ฎก็ป็ณๆฃ่
๏ผ่ฟๅฏไปฅ้่ฟไธญ่ฏ็ๆนๅผ็ผ่งฃ็ผ็ๅ้ป็ธ็ญ็็ถ๏ผๅธธ็จ็ไธญ่ฏๅ
ๆฌ่ต้ใๆ ๅญ็ญใ
5. ้ฅฎ้ฃ่ฐ็๏ผๅฏนไบ่่็ฎก็ป็ณๆฃ่
๏ผ่ฟๅฏไปฅ้่ฟ้ฅฎ้ฃ่ฐ็็ๆนๅผ็ผ่งฃ็ผ็ๅ้ป็ธ็ญ็็ถ๏ผๆฏๅฆๅคๅๆฐดใๅคๅ่ฌ่ๆฐดๆ็ญใ
้่ฆๆณจๆ็ๆฏ๏ผๅฏนไบ่่็ฎก็ป็ณ็
็ๆฒป็๏ผ้่ฆๆ นๆฎๆฃ่
็ๅ
ทไฝๆ
ๅต่ฟ่กไธชๆงๅๆฒป็๏ผไธ่ฝไธๆฆ่่ฎบใๅปบ่ฎฎๆฃ่
ๅๆถๅฐฑๅป๏ผๅจๅป็็ๆๅฏผไธ่ฟ่กๆฒป็ใ');
});
$(document).ready
###infering###
###instruction###
ไธไฝ35ๅฒๅฅณๆงๆฃ่
ๅบ็ฐไบๅ
ณ่็ผ็ๅ็ณ่้ๅผๅธธ๏ผ่ฏท้ฎๅฅนๅฏ่ฝๆฃไบไปไน็พ็
๏ผ
###golden output###
่ฏฅๆฃ่
ๅฏ่ฝๆฃไธไบๆ
ขๆง่ช่บซๅ
็ซๆง่ฐ่
บ็๏ผไผดๆๆ
ขๆง้ฃๆนฟๆงๅ
ณ่็ๅ็ณ่้ๅผๅธธ็็็ถใๅปบ่ฎฎๅฐๆถๅๅ
็ง่ฟ่กๆฃๆฅๅๆฒป็ใ
###model output###
่ฟไฝๅฅณๆงๆฃ่
ๅฏ่ฝๆฃไบ็ณๅฐฟ็
ใ็ณๅฐฟ็
็็็ถๅ
ๆฌๅฃๆธดใๅค้ฅฎใๅคๅฐฟใไฝ้ๅ่ฝป็ญ๏ผ่ๅ
ณ่็ผ็ๅ็ณ่้ๅผๅธธไนๆฏ็ณๅฐฟ็
็ๅธธ่ง็็ถไนไธใๅ ๆญค๏ผๅปบ่ฎฎๅฅนๅฐๅป้ข่ฟ่ก่ฟไธๆญฅ็ๆฃๆฅๅๆฒป็ใ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆ๏ผโThe quick brown fox jumps over the lazy dog.โ
<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆ๏ผโThe quick brown fox jumps over the lazy dog.โ
<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆไธญๆใ
The quick brown fox jumps over the lazy dog.<|endofutterance|>
<|beginofutterance|>ๆบ่ฝๅฉๆ
่ฟๅฅ่ฑๆๅฅๅญ็ไธญๆ็ฟป่ฏๆฏ๏ผโๆๆท็ๆฃ่ฒ็็ธ่ทณ่ฟๆ็ใโ<|endofutterance|><|endoftext|><|beginofutterance|>็ณป็ป
ๅฐไปฅไธ่ฑๆๅฅๅญ็ฟป่ฏๆ
(base) โ Huatuo-Llama-Med-Chinese git:(main) โ
``` | open | 2023-11-16T06:56:06Z | 2024-10-08T08:10:06Z | https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/issues/101 | [] | lehug | 3 |
pydantic/FastUI | pydantic | 374 | can't get running in Lambda | I've followed a few YouTube videos on this and while FastAPI works, I can not get FastUI working despite investing significant hours of trial and error but no luck. I want to build and a web app and preferably host in Lambda but can not get a simple demo app working from a Lambda despite my best effort. Is this a futile effort? Has someone out there done this and got it working? I've done a tremendous amount of leg work making sure I have the python modules deployed. I've tried both approaches, as lambda layer and in with the main.py lambda file but no luck either way. I've tried Lambda using Function URL mode and with API gateway. no luck either way. I keep reading it should work but have yet to find a working configuration. I've configured CORS and enable headers and method calls, etc. I've tried guided assistance form Amazon Q and Google's AI but still neither knows a working configuration, though they gave me plenty of attempts at it. | open | 2025-01-02T20:22:06Z | 2025-01-08T03:26:22Z | https://github.com/pydantic/FastUI/issues/374 | [] | TechDH | 1 |
litestar-org/litestar | api | 3,206 | Enhancement: ParsedSignature to enable TypeVar expansion using signature namespace | ### Summary
ParsedSignature uses `typing.get_type_hints` which handles forward referencing but no TypeVar expansion. For instance,
```Python
from typing import TypeVar, Generic, get_type_hints
T = TypeVar("T")
class Foo(Generic[T]):
def bar(self, data: T) -> T:
pass
genericAliasFoo = Foo[str]
print(get_type_hints(
genericAliasFoo.bar,
globalns={"T": str},
localns=None)) # gives {'data': ~T, 'return': ~T}
```
This makes it difficult to write type generic handlers as there is no way of expanding TypeVar. Both `pydantic` and `mypy` use some forms of TypeVar expansion for the same scenario. A naive implementation can be as simple as this:
```Python
class ParsedSignature:
@classmethod
def from_fn(cls, fn: AnyCallable, signature_namespace: dict[str, Any]) -> Self:
signature = Signature.from_callable(fn)
fn_type_hints = get_fn_type_hints(fn, namespace=signature_namespace)
# Expand type var
for param, value in fn_type_hints.items():
if value.__name__ in signature_namespace and isinstance(value, TypeVar):
fn_type_hints[param] = signature_namespace[value]
return cls.from_signature(signature, fn_type_hints)
```
### Basic Example
As an example, to build a generic controller, this is what works currently (from #1311 and #2162)
```Python
class GenericController(Controller, Generic[T]):
model_type: type[T]
def __class_getitem__(cls, model_type: type[T]) -> type:
return type(
f"Controller[{model_type.__name__}]", (cls,), {"model_type": model_type}
)
def __init__(self, owner: Router):
super().__init__(owner)
self.signature_namespace[T.__name__] = self.model_type
class BaseController(GenericController[T]):
@post()
async def create(self, data: T.__name__) -> T.__name__:
return data
```
Note how satanic the post handler looks. This works because `T.__name__` is resolved using `ForwardRef` in `get_type_hints`. Under the new proposal, it is possible to do this instead:
```Python
class BaseController(GenericController[T]):
@post()
async def create(self, data: T) -> T:
return data
```
### Drawbacks and Impact
_No response_
### Unresolved questions
_No response_ | closed | 2024-03-15T02:15:05Z | 2025-03-20T15:54:29Z | https://github.com/litestar-org/litestar/issues/3206 | [
"Enhancement"
] | harryle95 | 8 |
cvat-ai/cvat | computer-vision | 8,451 | OPAHealthCheck Internal Server Error for url: http://opa:8181/health?bundles | ### Actions before raising this issue
- [X] I searched the existing issues and did not find anything similar.
- [X] I read/searched [the docs](https://docs.cvat.ai/docs/)
### Steps to Reproduce
**CVAT Version:**
CVAT:DEV
Been running this for awhile now. Only decided to rebuild the container and since then this appears to be happening.
**_Error:_**
```OPAHealthCheck ... unknown error: 500 Server Error: Internal Server Error for url: http://opa:8181/health?bundles```
**Command used to build:**
```sudo -E docker compose -f docker-compose.yml -f docker-compose.external_db.yml -f components/serverless/docker-compose.serverless.yml -f docker-compose.https.yml up --build --force-recreate```
**cvat_opa**
```
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp: lookup cvat-server on 127.0.0.11:53: server misbehaving","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:00Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp: lookup cvat-server on 127.0.0.11:53: server misbehaving","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:00Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp: lookup cvat-server on 127.0.0.11:53: server misbehaving","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:00Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp: lookup cvat-server on 127.0.0.11:53: server misbehaving","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:00Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp: lookup cvat-server on 127.0.0.11:53: server misbehaving","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:01Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp 172.29.0.14:8080: connect: connection refused","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:02Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp 172.29.0.14:8080: connect: connection refused","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:03Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp 172.29.0.14:8080: connect: connection refused","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:05Z"}
{"level":"error","msg":"Bundle load failed: request failed: Get \"http://cvat-server:8080/api/auth/rules\": dial tcp 172.29.0.14:8080: connect: connection refused","name":"cvat","plugin":"bundle","time":"2024-09-17T21:15:07Z"}
```
**Health Check**
```
python manage.py health_check
WARNING:HEALTH:getting Cache backend: default health status
WARNING:HEALTH:getting Cache backend: media health status
WARNING:HEALTH:gettin OPA health status
WARNING:HEALTH:DONE getting Cache backend: default health status
ERROR:health-check:unknown error: 500 Server Error: Internal Server Error for url: http://opa:8181/health?bundles
Traceback (most recent call last):
File "/home/django/cvat/apps/health/backends.py", line 29, in check_status
response.raise_for_status()
File "/opt/venv/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://opa:8181/health?bundles
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/venv/lib/python3.8/site-packages/health_check/backends.py", line 30, in run_check
self.check_status()
File "/home/django/cvat/apps/health/backends.py", line 31, in check_status
raise HealthCheckException(str(e))
health_check.exceptions.HealthCheckException: unknown error: 500 Server Error: Internal Server Error for url: http://opa:8181/health?bundles
WARNING:HEALTH:DONE getting Cache backend: media health status
Cache backend: default ... working
Cache backend: media ... working
DatabaseBackend ... working
DiskUsage ... working
MigrationsHealthCheck ... working
OPAHealthCheck ... unknown error: 500 Server Error: Internal Server Error for url: http://opa:8181/health?bundles
```
OpenPolicyAgent appears to be running.
```
docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
c8ed8eaafca0 cvat/ui:dev "/docker-entrypoint.โฆ" 13 minutes ago Up 13 minutes 80/tcp cvat_ui
db4f33da87eb cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_worker_quality_reports
4ee86010847f cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_utils
5656f3bf0a32 cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_worker_analytics_reports
2b335307065c cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_server
d7dbe243a0bd cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_worker_import
bbf0b8c3e148 timberio/vector:0.26.0-alpine "/usr/local/bin/vectโฆ" 13 minutes ago Up 13 minutes cvat_vector
6d15378648f2 cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_worker_webhooks
40faf79a3111 cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_worker_annotation
8f6e68500767 cvat/server:dev "./backend_entrypoinโฆ" 13 minutes ago Up 13 minutes 8080/tcp cvat_worker_export
c596d96f3c43 quay.io/nuclio/dashboard:1.13.0-amd64 "/docker-entrypoint.โฆ" 13 minutes ago Up 13 minutes (healthy) 80/tcp, 0.0.0.0:8070->8070/tcp, :::8070->8070/tcp nuclio
7b8c0bffc8d4 grafana/grafana-oss:10.1.2 "sh -euc 'mkdir -p /โฆ" 13 minutes ago Up 13 minutes 3000/tcp cvat_grafana
026b6421eca6 redis:7.2.3-alpine "docker-entrypoint.sโฆ" 13 minutes ago Up 13 minutes 6379/tcp cvat_redis_inmem
62db1ec657cb clickhouse/clickhouse-server:23.11-alpine "/entrypoint.sh" 13 minutes ago Up 13 minutes 8123/tcp, 9000/tcp, 9009/tcp cvat_clickhouse
307193fa0112 traefik:v2.9 "/entrypoint.sh traeโฆ" 13 minutes ago Up 13 minutes 0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:8080->8080/tcp, :::8080->8080/tcp, 0.0.0.0:8090->8090/tcp, :::8090->8090/tcp traefik
939fe31fa958 apache/kvrocks:2.7.0 "kvrocks -c /var/libโฆ" 13 minutes ago Up 13 minutes (healthy) 6666/tcp cvat_redis_ondisk
5f88ded404cb openpolicyagent/opa:0.63.0 "/opa run --server -โฆ" 13 minutes ago Up 13 minutes cvat_opa
b9fb69b2b1f0 gcr.io/iguazio/alpine:3.17 "/bin/sh -c '/bin/slโฆ" 23 minutes ago Up 23 minutes nuclio-local-storage-reader
```
### Expected Behavior
I expected the built container to work properly. I'm not using a specific version of CVAT. Just using CVAT:DEV which could ultimately be my problem. But it's what was already established as I already have data both in a volume and on the database I need access to.
### Possible Solution
_No response_
### Context
_No response_
### Environment
```Markdown
Docker version 24.0.5, build ced0996
Linux/Ubuntu
```
| closed | 2024-09-17T21:30:44Z | 2024-09-25T21:11:23Z | https://github.com/cvat-ai/cvat/issues/8451 | [
"need info"
] | Shadowfear36 | 3 |
FlareSolverr/FlareSolverr | api | 1,431 | [yggtorrent] System.Threading.Tasks.TaskCanceledException: The request was canceled due to the configured HttpClient.Timeout of 60 seconds elapsing. | ### Have you checked our README?
- [X] I have checked the README
### Have you followed our Troubleshooting?
- [X] I have followed your Troubleshooting
### Is there already an issue for your problem?
- [X] I have checked older issues, open and closed
### Have you checked the discussions?
- [X] I have read the Discussions
### Have you ACTUALLY checked all these?
YES
### Environment
```markdown
- FlareSolverr version: v3.3.21
- Last working FlareSolverr version: v3.3.21
- Operating system: linux/arm64/v8
- Are you using Docker: yes with docker compose
- FlareSolverr User-Agent (see log traces or / endpoint):
- Are you using a VPN: no
- Are you using a Proxy: no
- Are you using Captcha Solver: no
- If using captcha solver, which one:
- URL to test this issue: https://www.ygg.re/auth/login
```
### Description
I use Sonarr with Jackett and Flaresolver to download from this site, but it hasn't been working since yesterday
### Logged Error Messages
```text
2025-01-07 11:33:56 INFO Serving on http://0.0.0.0:8191
2025-01-07 11:37:50 INFO Incoming request => POST /v1 body: {'maxTimeout': 55000, 'cmd': 'request.get', 'url': 'https://www.ygg.re/engine/search?do=search&order=desc&sort=publish_date&category=all'}
2025-01-07 11:38:10 INFO Challenge detected.
2025-01-07 11:38:54 ERROR Error: Error solving the challenge. Timeout after 55.0 seconds.
2025-01-07 11:38:54 INFO Response in 64.541 s
2025-01-07 11:38:54 INFO 172.19.0.6 POST http://flaresolverr:8191/v1 500 Internal Server Error
```
### Screenshots
_No response_ | closed | 2025-01-07T10:42:29Z | 2025-01-07T20:36:54Z | https://github.com/FlareSolverr/FlareSolverr/issues/1431 | [
"duplicate"
] | Rurtzane | 1 |
pyppeteer/pyppeteer | automation | 143 | RuntimeError('Event loop is closed'),sys:1: RuntimeWarning: coroutine 'Launcher.killChrome' was never awaited | here is my code:
async def fuzz_payload(browser, url, method, data, headers, celery_task_id, payload):
try:
page = await browser.newPage()
await page.setRequestInterception(True)
page.on(
'dialog',
lambda dialog: asyncio.ensure_future(
hook_dialog(dialog, url, method, data, headers, celery_task_id, payload))
)
page.on('request', lambda req: hook_request(req, url, method, data, headers, payload))
await page.goto(url)
# await asyncio.wait([page.waitForNavigation(),])
await page.evaluate('() => {var len=document.getElementsByTagName("xss").length;if (len > 0){alert("65534")}}')
# await asyncio.wait([page.waitForNavigation(), ])
# await page.close()
except Exception as e:
pass
# if not isinstance(e, NetworkError, ):
# logger.exception("fuzz_payload error")
async def fuzz_payloads(payloads):
try:
browser = await open_browser()
if isinstance(payloads, str):
payloads = [payloads]
tasks = [
fuzz_payload(browser, Request.url, Request.method, Request.data, Request.headers, Request.celery_task_id,
payload) for payload in payloads]
await asyncio.wait(tasks)
await asyncio.sleep(BROWSER_DEAD_TIME)
await browser.close()
except Exception:
pass
def handle_single_process(payloads):
"""
:param websites:
:return:
"""
event_loop = asyncio.get_event_loop()
try:
event_loop.run_until_complete(fuzz_payloads(payloads))
finally:
event_loop.close()
when i run this py, got this erro:
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/local/python3/lib/python3.6/site-packages/pyppeteer/launcher.py", line 151, in _close_process
self._loop.run_until_complete(self.killChrome())
File "/usr/local/python3/lib/python3.6/asyncio/base_events.py", line 460, in run_until_complete
self._check_closed()
File "/usr/local/python3/lib/python3.6/asyncio/base_events.py", line 377, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/local/python3/lib/python3.6/site-packages/pyppeteer/launcher.py", line 151, in _close_process
self._loop.run_until_complete(self.killChrome())
File "/usr/local/python3/lib/python3.6/asyncio/base_events.py", line 460, in run_until_complete
self._check_closed()
File "/usr/local/python3/lib/python3.6/asyncio/base_events.py", line 377, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
sys:1: RuntimeWarning: coroutine 'Launcher.killChrome' was never awaited
#################
also i use pip install -U git+https://github.com/pyppeteer/pyppeteer@dev
os: centos7.4
python3.6.8
| closed | 2020-06-30T03:19:37Z | 2024-02-12T04:40:33Z | https://github.com/pyppeteer/pyppeteer/issues/143 | [] | LeoonZHANG | 9 |
mwaskom/seaborn | pandas | 3,346 | sns.pairplot() fails with ValueError: output array is read-only | Dataset:
[train.csv](https://github.com/mwaskom/seaborn/files/11337012/train.csv)
Code:
```python
import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
train_df = pd.read_csv("train.csv")
train_df["CryoSleep"] = train_df["CryoSleep"].astype(bool)
train_df["VIP"] = train_df["VIP"].astype(bool)
train_df["CryoSleep"] = train_df["CryoSleep"].astype(int)
train_df["VIP"] = train_df["VIP"].astype(int)
train_df["Transported"] = train_df["Transported"].astype(int)
train_df["HomePlanet"] = train_df["HomePlanet"].astype("category")
train_df["Destination"] = train_df["Destination"].astype("category")
_ = train_df.drop(columns=["PassengerId", "Cabin", "Name"]).copy(deep=True)
sns.pairplot(_.dropna())
```
No matter what, I cannot make it work on this dataset. Even if you skip the type conversion block and apply it to the original `train_df` it still fails. Even without `dropna()` it still fails.
Complete error message:
https://gist.github.com/FlorinAndrei/f84c68a3efdab40968ffd84687e5e343
Python 3.11.3
Pandas 2.0.0
Numpy 1.24.2
Seaborn 0.12.2
Jupyter Notebook | closed | 2023-04-26T20:40:22Z | 2023-04-26T22:30:28Z | https://github.com/mwaskom/seaborn/issues/3346 | [] | FlorinAndrei | 3 |
NullArray/AutoSploit | automation | 1,209 | Unhandled Exception (63c520708) | Autosploit version: `2.2.3`
OS information: `Linux-4.4.193-darkonahZ-armv8l-with-libc`
Running context: `/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/autosploit.py`
Error meesage: ``
Error traceback:
```
Traceback (most recent call):
File "/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/autosploit/main.py", line 119, in main
loaded_exploits = load_exploits(EXPLOIT_FILES_PATH)
File "/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/lib/jsonize.py", line 57, in load_exploits
action = raw_input(lib.settings.AUTOSPLOIT_PROMPT)
EOFError:
```
Metasploit launched: `False`
| closed | 2019-11-26T13:08:21Z | 2019-11-30T04:33:28Z | https://github.com/NullArray/AutoSploit/issues/1209 | [] | AutosploitReporter | 0 |
tflearn/tflearn | tensorflow | 1,026 | lstm with return_seq = True as input to a fully_connected, unintended behaviour? | [g = tflearn.lstm(g, 1024, return_seq=True)
g = tflearn.dropout(g, 0.85)
g = tflearn.fully_connected(g, len(char_idx), activation='softmax')](url)
This shouldn't work, should it? On GPU tensorflow, it doesn't complain, (and training converged). On the CPU version, it says:
> g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
> File "/usr/local/lib/python3.5/dist-packages/tflearn/layers/core.py", line 142, in fully_connected
> assert len(input_shape) > 1, "Incoming Tensor shape must be at least 2-D"
> AssertionError: Incoming Tensor shape must be at least 2-D
which makes sense because g after the first line is a list.
Thanks! | open | 2018-03-01T01:26:15Z | 2019-05-07T12:06:08Z | https://github.com/tflearn/tflearn/issues/1026 | [] | lk251 | 1 |
TencentARC/GFPGAN | deep-learning | 477 | How fast can you get GFPgan? Does it use GPU? | Hello, I have been founding GFPgan to be a bit slow, it might possibly explained by the fact I am not using GPU much?
Could someone share a screenshot of their GPU usage when running GFPgan? Can you tell me how fast you can process 10 sec or 1 minute video? (tell me the resolution of the video)
thanks | open | 2023-12-29T06:32:25Z | 2024-02-29T02:51:57Z | https://github.com/TencentARC/GFPGAN/issues/477 | [] | AIhasArrived | 2 |
mitmproxy/mitmproxy | python | 7,227 | mitmproxy 11 installed via pip fails due to new urwid requirement - undefined symbol: PyUnicode_AS_UNICODE | #### Problem Description
In taking a system (Ubuntu 24.04 with miniconda (Python 3.12)) that successfully had mitmproxy 10 installed, upgrading to mitmproxy 11 via pip causes installation of a new `urwid` package, which now errors out with: `undefined symbol: PyUnicode_AS_UNICODE`
#### Steps to reproduce the behavior:
1. Install mitmproxy 10 on a system with Python 3.12
2. Upgrade via pip to mitmproxy 11.0.0
3. Try running mitmproxy after upgrade
The prior pip-installed mitmproxy 10 had a dependency package `urwid-mitmproxy`. Perhaps this urwid package was created to avoid the `PyUnicode_AS_UNICODE` error? Or possibly urwid has changed since the version 2.1.2.1 in a way that introduces the `PyUnicode_AS_UNICODE` problem? Either way, before the upgrade:
```
tapioca@ubuntu2404:~/tapioca$ pip list | grep -E "urwid|mitmproxy"
mitmproxy 10.3.0
mitmproxy_rs 0.5.2
urwid-mitmproxy 2.1.2.1
```
Now, if I upgrade, I get a new version of `urwid` installed, presumably due to the use of `WidgetWrap` and potentially more.
```
tapioca@ubuntu2404:~/tapioca$ pip install mitmproxy --upgrade > /dev/null
tapioca@ubuntu2404:~/tapioca$ pip list | grep -E "urwid|mitmproxy"
mitmproxy 11.0.0
mitmproxy_rs 0.9.2
urwid 2.6.15
urwid-mitmproxy 2.1.2.1
```
In this state, mitmproxy will fail to run:
```
tapioca@ubuntu2404:~/tapioca$ mitmproxy
Traceback (most recent call last):
File "/home/tapioca/miniconda/bin/mitmproxy", line 8, in <module>
sys.exit(mitmproxy())
^^^^^^^^^^^
File "/home/tapioca/miniconda/lib/python3.12/site-packages/mitmproxy/tools/main.py", line 141, in mitmproxy
from mitmproxy.tools import console
File "/home/tapioca/miniconda/lib/python3.12/site-packages/mitmproxy/tools/console/__init__.py", line 1, in <module>
from mitmproxy.tools.console import master
File "/home/tapioca/miniconda/lib/python3.12/site-packages/mitmproxy/tools/console/master.py", line 14, in <module>
import urwid
File "/home/tapioca/miniconda/lib/python3.12/site-packages/urwid/__init__.py", line 30, in <module>
from urwid.canvas import (
File "/home/tapioca/miniconda/lib/python3.12/site-packages/urwid/canvas.py", line 30, in <module>
from urwid.str_util import calc_text_pos, calc_width
ImportError: /home/tapioca/miniconda/lib/python3.12/site-packages/urwid/str_util.cpython-312-x86_64-linux-gnu.so: undefined symbol: PyUnicode_AS_UNICODE
```
#### System Information
Paste the output of "mitmproxy --version" here.
(see above)
| open | 2024-10-03T15:09:56Z | 2024-12-09T10:42:32Z | https://github.com/mitmproxy/mitmproxy/issues/7227 | [
"kind/triage"
] | wdormann | 8 |
pytorch/pytorch | deep-learning | 149,065 | [ONNX Export] dynamic_shapes ignored during model export. | ### ๐ Describe the bug
```python
from torch.export import Dim
from pathlib import Path
import onnx
import onnxruntime
import torch
model = model
model.load_state_dict(checkpoint.get("state_dict"), strict=True)
model.eval()
with torch.no_grad():
data = torch.randn(1, 3, 256, 256)
torch_outputs = model(data)
example_inputs = (data.cuda(),)
batch_dim = Dim("batch_size", min=1, max=16)
onnx_program = torch.onnx.export(
model=model.cuda(),
args=example_inputs,
dynamo=True,
input_names=["images"],
output_names=["logits"],
opset_version=20,
dynamic_shapes=({0: batch_dim},),
)
onnx_program.optimize()
onnx_program.save(str(ONNX_MODEL))
del onnx_program
del model
onnx_model = onnx.load(str(ONNX_MODEL))
onnx.checker.check_model(onnx_model)
num_nodes = len(onnx_model.graph.node)
print(f"Number of nodes in the ONNX model: {num_nodes}")
# Inspect inputs
print("Model Inputs:")
for inp in onnx_model.graph.input:
dims = [dim.dim_value if dim.HasField("dim_value") else dim.dim_param for dim in inp.type.tensor_type.shape.dim]
print(f"{inp.name}: {dims}")
# Inspect outputs
print("\nModel Outputs:")
for out in onnx_model.graph.output:
dims = [dim.dim_value if dim.HasField("dim_value") else dim.dim_param for dim in out.type.tensor_type.shape.dim]
print(f"{out.name}: {dims}")
del onnx_model
onnx_inputs = [tensor.numpy(force=True) for tensor in example_inputs]
ort_session = onnxruntime.InferenceSession(str(ONNX_MODEL), providers=["CPUExecutionProvider"])
onnxruntime_input = {input_arg.name: input_value for input_arg, input_value in zip(ort_session.get_inputs(), onnx_inputs)}
# ONNX Runtime returns a list of outputs
onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]
assert len(torch_outputs) == len(onnxruntime_outputs)
for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):
torch.testing.assert_close(torch_output.cpu(), torch.tensor(onnxruntime_output))
print("All tests passed")
```
Code runs with the output:
```
FutureWarning: 'onnxscript.values.Op.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
param_schemas = callee.param_schemas()
FutureWarning: 'onnxscript.values.OnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
param_schemas = callee.param_schemas()
[torch.onnx] Obtain model graph for `CellSamWrapper([...]` with `torch.export.export(..., strict=False)`...
[torch.onnx] Obtain model graph for `CellSamWrapper([...]` with `torch.export.export(..., strict=False)`... โ
[torch.onnx] Run decomposition...
[torch.onnx] Run decomposition... โ
[torch.onnx] Translate the graph into ONNX...
[torch.onnx] Translate the graph into ONNX... โ
Applied 112 of general pattern rewrite rules.
Number of nodes in the ONNX model: 1059
Model Inputs:
images: [1, 3, 256, 256]
Model Outputs:
logits: [1, 3, 256, 256]
All tests passed
```
I then try and test with a batch size of 4:
```python
from pathlib import Path
import numpy
import onnx
import onnxruntime
ROOT = Path(__file__).resolve().parent.parent
ONNX_MODEL = ROOT / "model.onnx"
onnx_model = onnx.load(str(ONNX_MODEL))
onnx_inputs = [numpy.random.randn(4, 3, 256, 256).astype(numpy.float32)]
ort_session = onnxruntime.InferenceSession(str(ONNX_MODEL), providers=["CPUExecutionProvider"])
onnxruntime_input = {input_arg.name: input_value for input_arg, input_value in zip(ort_session.get_inputs(), onnx_inputs)}
# ONNX Runtime returns a list of outputs
onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]
```
which produces the error:
```
Traceback (most recent call last):
File "onnx_test.py", line 19, in <module>
onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.12/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 270, in run
return self._sess.run(output_names, input_feed, run_options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: images for the following indices
index: 0 Got: 4 Expected: 1
Please fix either the inputs/outputs or the model.
```
I have tried this on both Torch 2.6 and the nightly version. Am I doing something wrong?
### Versions
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: Ubuntu 24.04.2 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39
Python version: 3.12.9 (main, Feb 12 2025, 14:50:50) [Clang 19.1.6 ] (64-bit runtime)
Python platform: Linux-6.8.0-1021-gcp-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.8.61
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA L4
Nvidia driver version: 570.86.10
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 7
BogoMIPS: 4400.41
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 4 MiB (4 instances)
L3 cache: 38.5 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Versions of relevant libraries:
[pip3] numpy==1.26.4
[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] onnx==1.17.0
[pip3] onnx_graphsurgeon==0.5.6
[pip3] onnxruntime-gpu==1.21.0
[pip3] onnxscript==0.2.2
[pip3] pytorch-ignite==0.5.1
[pip3] torch==2.6.0
[pip3] torchvision==0.21.0
[pip3] triton==3.2.0
[pip3] tritonclient==2.55.0 | closed | 2025-03-12T18:58:41Z | 2025-03-13T20:45:40Z | https://github.com/pytorch/pytorch/issues/149065 | [
"module: onnx",
"triaged"
] | spkgyk | 9 |
Johnserf-Seed/TikTokDownload | api | 604 | ๆไนๆง่กๅ่ง้ขไธ่ฝฝ๏ผ | open | 2023-11-20T23:46:22Z | 2023-11-20T23:46:22Z | https://github.com/Johnserf-Seed/TikTokDownload/issues/604 | [
"ๆ ๆ(invalid)"
] | Virtual-human | 0 | |
holoviz/panel | plotly | 6,911 | Document how to use .from_param to create a widget from a reactive parameter | I can't figure out how to create a widget from a reactive parameter.
More specifically
```python
import panel as pn
pn.extension()
zoom = pn.rx(2)
slider = pn.widgets.IntSlider.from_param(zoom, start=1, end=10)
```
I get
```bash
AttributeError: 'rx' object has no attribute 'name'
Traceback (most recent call last):
File "/home/jovyan/repos/private/panel-geospatial/.venv/lib/python3.11/site-packages/panel/io/handlers.py", line 389, in run
exec(self._code, module.__dict__)
File "/home/jovyan/repos/private/panel-geospatial/pages/03_mapbox.py", line 11, in <module>
pn.widgets.IntSlider.from_param(zoom, start=1, end=10)
File "/home/jovyan/repos/private/panel-geospatial/.venv/lib/python3.11/site-packages/panel/widgets/base.py", line 93, in from_param
parameter, widgets={parameter.name: dict(type=cls, **params)},
^^^^^^^^^^^^^^
File "/home/jovyan/repos/private/panel-geospatial/.venv/lib/python3.11/site-packages/param/reactive.py", line 1032, in __getattribute__
return super().__getattribute__(name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'rx' object has no attribute 'name'
```
I cannot find this documented anywhere. | open | 2024-06-10T19:32:03Z | 2024-07-16T14:37:47Z | https://github.com/holoviz/panel/issues/6911 | [
"type: docs",
"need input from Philipp"
] | MarcSkovMadsen | 5 |
skforecast/skforecast | scikit-learn | 314 | How to fill future known information from one of the exogenous variables? | Hi developers,
I have a question about fill future known information from one of the exogenous variables.
For example, I have a data where is y as target variable and three exogenous variables X1,X2, and X3. If I have future known information of X3, but no future information of X1,X2. Either using directed or recursive methods, how to integrate the future known exogenous variables (X3) and without future known (X1,X2) and integrate this in the designed framework package?
| closed | 2022-12-12T09:22:01Z | 2022-12-28T09:57:27Z | https://github.com/skforecast/skforecast/issues/314 | [
"question"
] | kennis222 | 3 |
iperov/DeepFaceLab | machine-learning | 5,515 | device_lib.list_local_devices() doesn't return in the CUDA build up to 2080 | Any batch script hangs, I traced it and it freezes in tensorflow when it calls **device_lib.list_local_devices()**
In: C:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\DeepFaceLab\core\leras\device.py
GPU: Geforce 750 Ti
Win 10
```
import tensorflow as tf
from tensorflow.python.client import device_lib
print(f"list_local_devices()={device_lib.list_local_devices()}")
```
I tried several things: if there was an incompatibility with the installed newer CUDA, but it shouldn't as the build has its own directory and it's an old tensorflow 1.13. The paths are set by setenv.bat,, but in addition I added them in the system's Path, also I tried with copying the .dll files both in the .bat folder and in the main.py.
I've been using the DirectX12 version as an alternative. The GPU is 750 Ti and initially I thought that it was just too old, but I just discovered it's supposed to work as it supports newer CUDA versions. Also there's not an error message, but the call to "list_local_devices" doesn't return.
If I run setenv.bat and then I call the build's python, then import the tf. and call list_local_devices interactively, the function recognizes the GPU and prints a correct output, but then the CLI session hangs. The system has also an integrated Intel GPU HD530.
I understand that this seems to be a tensorflow or drivers' issue, but does anyone have solved it? Thanks.
```
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8>python
Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
c:\DFL\DeepFaceLab_NVIDIA_up_to_RTX2080Ti\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
>>>
>>> from tensorflow.python.client import device_lib
>>> print(f"list_local_devices()={device_lib.list_local_devices()}")
2022-05-09 22:11:18.429936: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2022-05-09 22:11:18.551876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 750 Ti major: 5 minor: 0 memoryClockRate(GHz): 1.0845
pciBusID: 0000:01:00.0
totalMemory: 2.00GiB freeMemory: 194.50MiB
2022-05-09 22:11:18.552651: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
```
````
@staticmethod
def _get_tf_devices_proc(q : multiprocessing.Queue):
print("_get_tf_devices_proc")
print(sys.platform[0:3])
if sys.platform[0:3] == 'win':
compute_cache_path = Path(os.environ['APPDATA']) / 'NVIDIA' / ('ComputeCache_ALL')
os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
print("CUDA_CACHE_PATH={os.environ['CUDA_CACHE_PATH']}")
if not compute_cache_path.exists():
io.log_info("Caching GPU kernels...")
compute_cache_path.mkdir(parents=True, exist_ok=True)
import tensorflow
tf_version = tensorflow.version.VERSION
print(f"tf_version={tf_version}")
#if tf_version is None:
# tf_version = tensorflow.version.GIT_VERSION
if tf_version[0] == 'v':
tf_version = tf_version[1:]
if tf_version[0] == '2':
tf = tensorflow.compat.v1
else:
tf = tensorflow
import logging
# Disable tensorflow warnings
tf_logger = logging.getLogger('tensorflow')
tf_logger.setLevel(logging.ERROR)
from tensorflow.python.client import device_lib
print("AFTER: from tensorflow.python.client import device_lib")
devices = []
print(f"list_local_devices()={device_lib.list_local_devices()}") ### HANGS HERE ###
physical_devices = device_lib.list_local_devices()
physical_devices_f = {}
print("BEFORE: for dev in physical_devices:")
```
| open | 2022-05-09T19:24:31Z | 2023-06-09T13:52:12Z | https://github.com/iperov/DeepFaceLab/issues/5515 | [] | Twenkid | 4 |
iperov/DeepFaceLab | machine-learning | 5,353 | The Specified module could not be found | It is showing that the "Import error : DLL load failed the specified module could not be found" how to resolve it ? | closed | 2021-06-20T10:58:43Z | 2021-06-26T08:10:50Z | https://github.com/iperov/DeepFaceLab/issues/5353 | [] | ghost | 0 |
ClimbsRocks/auto_ml | scikit-learn | 19 | consider having .train() and .customized_train() | .train() will just call .customized_train() with a series of defaults in place.
it'll have a much simpler interface, which would be nice.
| open | 2016-08-11T06:14:37Z | 2016-08-13T03:50:46Z | https://github.com/ClimbsRocks/auto_ml/issues/19 | [
"easy win"
] | ClimbsRocks | 1 |
floodsung/Deep-Learning-Papers-Reading-Roadmap | deep-learning | 104 | googleNet with VGG | can i use vgg with googleNet? | open | 2018-12-19T20:58:34Z | 2018-12-19T20:58:34Z | https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/issues/104 | [] | Emperor66 | 0 |
wyfo/apischema | graphql | 492 | Installation fails on Mac M1 | Installation fails on new Mac with M1 chips throwing the following error:
```
from apischema.serialization import serialize
File "/Users/my_user/.pyenv/versions/3.9.9/envs/cubist-games-manager/lib/python3.9/site-packages/apischema/serialization/__init__.py", line 34, in <module>
from apischema.serialization.methods import (
ImportError: dlopen(/Users/my_user/.pyenv/versions/3.9.9/envs/cubist-games-manager/lib/python3.9/site-packages/apischema/serialization/methods.cpython-39-darwin.so, 0x0002): tried: '/Users/my_user/.pyenv/versions/3.9.9/envs/cubist-games-manager/lib/python3.9/site-packages/apischema/serialization/methods.cpython-39-darwin.so'
(mach-o file, but is an incompatible architecture (have (x86_64), need (arm64e)))
``` | closed | 2022-10-18T08:43:59Z | 2023-11-15T18:44:03Z | https://github.com/wyfo/apischema/issues/492 | [
"question"
] | AnderUstarroz | 6 |
aws/aws-sdk-pandas | pandas | 2,844 | Athena query throws error with message "AttributeError: 'pyarrow._parquet.FileMetaData' object has no attribute 'total_byte_size'" | ### Describe the bug
Using the AWS Wrangler SDK to query a table using Athena results in error message "AttributeError: 'pyarrow._parquet.FileMetaData' object has no attribute 'total_byte_size'". This error happens when using modin.pandas and not with regular pandas library.
Environment: Juptyer notebook on Sagemaker
Error stack trace below:
```
AttributeError Traceback (most recent call last)
Cell In[9], line 1
----> 1 df = wr.athena.read_sql_query('select count(distinct ti_cu_customer_id) as num_customers from loans', database='<db-name>', workgroup='<workgroup_name>')
File /opt/conda/lib/python3.10/site-packages/awswrangler/_config.py:715, in apply_configs.<locals>.wrapper(*args_raw, **kwargs)
713 del args[name]
714 args = {**args, **keywords}
--> 715 return function(**args)
File /opt/conda/lib/python3.10/site-packages/awswrangler/_utils.py:178, in validate_kwargs.<locals>.decorator.<locals>.inner(*args, **kwargs)
175 if condition_fn() and len(passed_unsupported_kwargs) > 0:
176 raise exceptions.InvalidArgument(f"{message} `{', '.join(passed_unsupported_kwargs)}`.")
--> 178 return func(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/awswrangler/athena/_read.py:1081, in read_sql_query(sql, database, ctas_approach, unload_approach, ctas_parameters, unload_parameters, categories, chunksize, s3_output, workgroup, encryption, kms_key, keep_files, use_threads, boto3_session, client_request_token, athena_cache_settings, data_source, athena_query_wait_polling_delay, params, paramstyle, dtype_backend, s3_additional_kwargs, pyarrow_additional_kwargs)
1078 ctas_bucketing_info = ctas_parameters.get("bucketing_info")
1079 ctas_write_compression = ctas_parameters.get("compression")
-> 1081 return _resolve_query_without_cache(
1082 sql=sql,
1083 database=database,
1084 data_source=data_source,
1085 ctas_approach=ctas_approach,
1086 unload_approach=unload_approach,
1087 unload_parameters=unload_parameters,
1088 categories=categories,
1089 chunksize=chunksize,
1090 s3_output=s3_output,
1091 workgroup=workgroup,
1092 encryption=encryption,
1093 kms_key=kms_key,
1094 keep_files=keep_files,
1095 ctas_database=ctas_database,
1096 ctas_temp_table_name=ctas_temp_table_name,
1097 ctas_bucketing_info=ctas_bucketing_info,
1098 ctas_write_compression=ctas_write_compression,
1099 athena_query_wait_polling_delay=athena_query_wait_polling_delay,
1100 use_threads=use_threads,
1101 s3_additional_kwargs=s3_additional_kwargs,
1102 boto3_session=boto3_session,
1103 pyarrow_additional_kwargs=pyarrow_additional_kwargs,
1104 execution_params=execution_params,
1105 dtype_backend=dtype_backend,
1106 client_request_token=client_request_token,
1107 )
File /opt/conda/lib/python3.10/site-packages/awswrangler/athena/_read.py:507, in _resolve_query_without_cache(sql, database, data_source, ctas_approach, unload_approach, unload_parameters, categories, chunksize, s3_output, workgroup, encryption, kms_key, keep_files, ctas_database, ctas_temp_table_name, ctas_bucketing_info, ctas_write_compression, athena_query_wait_polling_delay, use_threads, s3_additional_kwargs, boto3_session, pyarrow_additional_kwargs, execution_params, dtype_backend, client_request_token)
505 name = f"temp_table_{uuid.uuid4().hex}"
506 try:
--> 507 return _resolve_query_without_cache_ctas(
508 sql=sql,
509 database=database,
510 data_source=data_source,
511 s3_output=s3_output,
512 keep_files=keep_files,
513 chunksize=chunksize,
514 categories=categories,
515 encryption=encryption,
516 workgroup=workgroup,
517 kms_key=kms_key,
518 alt_database=ctas_database,
519 name=name,
520 ctas_bucketing_info=ctas_bucketing_info,
521 ctas_write_compression=ctas_write_compression,
522 athena_query_wait_polling_delay=athena_query_wait_polling_delay,
523 use_threads=use_threads,
524 s3_additional_kwargs=s3_additional_kwargs,
525 boto3_session=boto3_session,
526 pyarrow_additional_kwargs=pyarrow_additional_kwargs,
527 execution_params=execution_params,
528 dtype_backend=dtype_backend,
529 )
530 finally:
531 catalog.delete_table_if_exists(database=ctas_database or database, table=name, boto3_session=boto3_session)
File /opt/conda/lib/python3.10/site-packages/awswrangler/athena/_read.py:345, in _resolve_query_without_cache_ctas(sql, database, data_source, s3_output, keep_files, chunksize, categories, encryption, workgroup, kms_key, alt_database, name, ctas_bucketing_info, ctas_write_compression, athena_query_wait_polling_delay, use_threads, s3_additional_kwargs, boto3_session, pyarrow_additional_kwargs, execution_params, dtype_backend)
343 ctas_query_metadata = cast(_QueryMetadata, ctas_query_info["ctas_query_metadata"])
344 _logger.debug("CTAS query metadata: %s", ctas_query_metadata)
--> 345 return _fetch_parquet_result(
346 query_metadata=ctas_query_metadata,
347 keep_files=keep_files,
348 categories=categories,
349 chunksize=chunksize,
350 use_threads=use_threads,
351 s3_additional_kwargs=s3_additional_kwargs,
352 boto3_session=boto3_session,
353 temp_table_fqn=fully_qualified_name,
354 pyarrow_additional_kwargs=pyarrow_additional_kwargs,
355 dtype_backend=dtype_backend,
356 )
File /opt/conda/lib/python3.10/site-packages/awswrangler/athena/_read.py:156, in _fetch_parquet_result(query_metadata, keep_files, categories, chunksize, use_threads, boto3_session, s3_additional_kwargs, temp_table_fqn, pyarrow_additional_kwargs, dtype_backend)
154 pyarrow_additional_kwargs["categories"] = categories
155 _logger.debug("Reading Parquet result from %d paths", len(paths))
--> 156 ret = s3.read_parquet(
157 path=paths,
158 use_threads=use_threads,
159 boto3_session=boto3_session,
160 chunked=chunked,
161 pyarrow_additional_kwargs=pyarrow_additional_kwargs,
162 dtype_backend=dtype_backend,
163 )
165 if chunked is False:
166 ret = _apply_query_metadata(df=ret, query_metadata=query_metadata)
File /opt/conda/lib/python3.10/site-packages/awswrangler/_utils.py:178, in validate_kwargs.<locals>.decorator.<locals>.inner(*args, **kwargs)
175 if condition_fn() and len(passed_unsupported_kwargs) > 0:
176 raise exceptions.InvalidArgument(f"{message} `{', '.join(passed_unsupported_kwargs)}`.")
--> 178 return func(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/awswrangler/_config.py:715, in apply_configs.<locals>.wrapper(*args_raw, **kwargs)
713 del args[name]
714 args = {**args, **keywords}
--> 715 return function(**args)
File /opt/conda/lib/python3.10/site-packages/awswrangler/s3/_read_parquet.py:558, in read_parquet(path, path_root, dataset, path_suffix, path_ignore_suffix, ignore_empty, partition_filter, columns, validate_schema, coerce_int96_timestamp_unit, schema, last_modified_begin, last_modified_end, version_id, dtype_backend, chunked, use_threads, ray_args, boto3_session, s3_additional_kwargs, pyarrow_additional_kwargs, decryption_configuration)
543 if chunked:
544 return _read_parquet_chunked(
545 s3_client=s3_client,
546 paths=paths,
(...)
555 decryption_properties=decryption_properties,
556 )
--> 558 return _read_parquet(
559 paths,
560 path_root=path_root,
561 schema=schema,
562 columns=columns,
563 coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,
564 use_threads=use_threads,
565 parallelism=ray_args.get("parallelism", -1),
566 s3_client=s3_client,
567 s3_additional_kwargs=s3_additional_kwargs,
568 arrow_kwargs=arrow_kwargs,
569 version_ids=version_ids,
570 bulk_read=bulk_read,
571 decryption_properties=decryption_properties,
572 )
File /opt/conda/lib/python3.10/site-packages/awswrangler/_distributed.py:105, in Engine.dispatch_on_engine.<locals>.wrapper(*args, **kw)
102 @wraps(func)
103 def wrapper(*args: Any, **kw: dict[str, Any]) -> Any:
104 cls.initialize(name=cls.get().value)
--> 105 return cls.dispatch_func(func)(*args, **kw)
File /opt/conda/lib/python3.10/site-packages/awswrangler/distributed/ray/modin/s3/_read_parquet.py:51, in _read_parquet_distributed(paths, path_root, schema, columns, coerce_int96_timestamp_unit, use_threads, parallelism, version_ids, s3_client, s3_additional_kwargs, arrow_kwargs, bulk_read, decryption_properties)
48 if decryption_properties:
49 dataset_kwargs["decryption_properties"] = decryption_properties
---> 51 dataset = read_datasource(
52 **_resolve_datasource_parameters(
53 bulk_read,
54 paths=paths,
55 path_root=path_root,
56 arrow_parquet_args={
57 "use_threads": use_threads,
58 "schema": schema,
59 "columns": columns,
60 "dataset_kwargs": dataset_kwargs,
61 },
62 ),
63 parallelism=parallelism,
64 )
65 return _to_modin(
66 dataset=dataset,
67 to_pandas_kwargs=arrow_kwargs,
68 ignore_index=arrow_kwargs.get("ignore_metadata"),
69 )
File /opt/conda/lib/python3.10/site-packages/ray/_private/auto_init_hook.py:21, in wrap_auto_init.<locals>.auto_init_wrapper(*args, **kwargs)
18 @wraps(fn)
19 def auto_init_wrapper(*args, **kwargs):
20 auto_init_ray()
---> 21 return fn(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/ray/data/read_api.py:399, in read_datasource(datasource, parallelism, ray_remote_args, concurrency, override_num_blocks, **read_args)
389 requested_parallelism, _, inmemory_size = _autodetect_parallelism(
390 parallelism,
391 ctx.target_max_block_size,
(...)
394 placement_group=cur_pg,
395 )
397 # TODO(hchen/chengsu): Remove the duplicated get_read_tasks call here after
398 # removing LazyBlockList code path.
--> 399 read_tasks = datasource_or_legacy_reader.get_read_tasks(requested_parallelism)
401 read_op_name = f"Read{datasource.get_name()}"
403 block_list = LazyBlockList(
404 read_tasks,
405 read_op_name=read_op_name,
406 ray_remote_args=ray_remote_args,
407 owned_by_consumer=False,
408 )
File /opt/conda/lib/python3.10/site-packages/awswrangler/distributed/ray/datasources/arrow_parquet_datasource.py:341, in ArrowParquetDatasource.get_read_tasks(self, parallelism)
338 if len(fragments) <= 0:
339 continue
--> 341 meta = self._meta_provider(
342 paths, # type: ignore[arg-type]
343 self._inferred_schema,
344 num_fragments=len(fragments),
345 prefetched_metadata=metadata,
346 )
347 # If there is a filter operation, reset the calculated row count,
348 # since the resulting row count is unknown.
349 if self._arrow_parquet_args.get("filter") is not None:
File /opt/conda/lib/python3.10/site-packages/ray/data/datasource/file_meta_provider.py:70, in FileMetadataProvider.__call__(self, paths, schema, **kwargs)
64 def __call__(
65 self,
66 paths: List[str],
67 schema: Optional[Union[type, "pyarrow.lib.Schema"]],
68 **kwargs,
69 ) -> BlockMetadata:
---> 70 return self._get_block_metadata(paths, schema, **kwargs)
File /opt/conda/lib/python3.10/site-packages/ray/data/datasource/file_meta_provider.py:309, in DefaultParquetMetadataProvider._get_block_metadata(self, paths, schema, num_fragments, prefetched_metadata)
292 def _get_block_metadata(
293 self,
294 paths: List[str],
(...)
298 prefetched_metadata: Optional[List["_ParquetFileFragmentMetaData"]],
299 ) -> BlockMetadata:
300 if (
301 prefetched_metadata is not None
302 and len(prefetched_metadata) == num_fragments
(...)
305 # Fragment metadata was available, construct a normal
306 # BlockMetadata.
307 block_metadata = BlockMetadata(
308 num_rows=sum(m.num_rows for m in prefetched_metadata),
--> 309 size_bytes=sum(m.total_byte_size for m in prefetched_metadata),
310 schema=schema,
311 input_files=paths,
312 exec_stats=None,
313 ) # Exec stats filled in later.
314 else:
315 # Fragment metadata was not available, construct an empty
316 # BlockMetadata.
317 block_metadata = BlockMetadata(
318 num_rows=None,
319 size_bytes=None,
(...)
322 exec_stats=None,
323 )
File /opt/conda/lib/python3.10/site-packages/ray/data/datasource/file_meta_provider.py:309, in <genexpr>(.0)
292 def _get_block_metadata(
293 self,
294 paths: List[str],
(...)
298 prefetched_metadata: Optional[List["_ParquetFileFragmentMetaData"]],
299 ) -> BlockMetadata:
300 if (
301 prefetched_metadata is not None
302 and len(prefetched_metadata) == num_fragments
(...)
305 # Fragment metadata was available, construct a normal
306 # BlockMetadata.
307 block_metadata = BlockMetadata(
308 num_rows=sum(m.num_rows for m in prefetched_metadata),
--> 309 size_bytes=sum(m.total_byte_size for m in prefetched_metadata),
310 schema=schema,
311 input_files=paths,
312 exec_stats=None,
313 ) # Exec stats filled in later.
314 else:
315 # Fragment metadata was not available, construct an empty
316 # BlockMetadata.
317 block_metadata = BlockMetadata(
318 num_rows=None,
319 size_bytes=None,
(...)
322 exec_stats=None,
323 )
AttributeError: 'pyarrow._parquet.FileMetaData' object has no attribute 'total_byte_size'
```
### How to Reproduce
Below is a code snippet to reproduce the error
```
!pip install awswrangler[ray,modin]
import modin.pandas as pd
import awswrangler as wr
wr.engine.initialize()
df = wr.athena.read_sql_query('select count(distinct ti_cu_customer_id) as num_customers from loans', database='my-db, workgroup='my-workgroup')
### Expected behavior
_No response_
### Your project
_No response_
### Screenshots
_No response_
### OS
Linux
### Python version
3.10
### AWS SDK for pandas version
3.7.3
### Additional context
_No response_ | closed | 2024-06-04T16:22:05Z | 2024-07-26T08:08:39Z | https://github.com/aws/aws-sdk-pandas/issues/2844 | [
"bug"
] | leo4ever | 3 |
jina-ai/serve | fastapi | 5,534 | Maintainer Productivity Enhancement with Preview Environments for PRs | I would like to support Jina by implementing ephemeral preview environments, powered by [Uffizzi](https://github.com/UffizziCloud/uffizzi)
Uffizzi is an Open Source, full stack, previews engine, and our platform is available completely free for Jina (and all open source projects). An Uffizzi integration with Jina will provision preview environments for every PR opened on Jina in the cloud, allowing faster reviews, faster merges, and increased release velocity.
[Here are the open-source projects](https://uffizzi.notion.site/) which are currently using Uffizzi to provision previews.
Uffizzi is purpose-built for the task of previewing PRs and it integrates with your workflow to deploy preview environments in the background without any manual steps for maintainers or contributors.
I'll go ahead and create an Initial PoC for you if you think there is value in this proposal.
I work on the Uffizzi project.
cc @waveywaves | closed | 2022-12-16T14:57:16Z | 2023-04-01T00:18:40Z | https://github.com/jina-ai/serve/issues/5534 | [
"Stale"
] | daramayis | 1 |
litestar-org/litestar | asyncio | 3,854 | Enhancement: Allow the cache to specify the namespace on a route level | ### Summary
Today, every cached response is cached inside the namespace "response_cache" or any namespace you defined in a root level. However, I would like certain route to be cached on different namespace to manage the cache invalidation differently according to the response.
### Basic Example
```
@get(cache=True, cache_namespace='another_response_cache', sync_to_thread=False)
def cached_handler() -> str:
# this will use app.stores.get("response_cache")
return "Hello, world!"
```
### Drawbacks and Impact
_No response_
### Unresolved questions
_No response_ | open | 2024-11-12T08:30:40Z | 2025-03-20T15:55:02Z | https://github.com/litestar-org/litestar/issues/3854 | [
"Enhancement"
] | dylandoamaral | 0 |
encode/apistar | api | 445 | apistar cant work and the commandline cant recognized from 0.4.1 above | and for 0.4.0, when enter apistar it will have the error like :
Traceback (most recent call last):
File "c:\users\williamchen\anaconda3\Lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "c:\users\williamchen\anaconda3\Lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\williamchen\Envs\mxonline\Scripts\apistar.exe\__main__.py", line 5, in <module>
ImportError: cannot import name 'main' | closed | 2018-04-16T08:20:31Z | 2018-04-17T10:05:06Z | https://github.com/encode/apistar/issues/445 | [] | youngorchen | 2 |
tfranzel/drf-spectacular | rest-api | 723 | Specifying a response leads to undesired parameters added automatically | The following code works as expected whereby I end up with only a "some_id" parameter showing up in SwaggerUI for the endpoint.
```py
@extend_schema(
description="Lists items",
parameters=[
OpenApiParameter(
"some_id",
required=True,
),
],
)
def list(self, request):
```
However, if I want to specify any type of minimal documentation for the response, such as the following, automatically a `page` and a `search` parameter are added. Is it possible to somehow prevent/disable those automatically added parameters (`page` and/or `search`) while still providing documentation for a response?
```py
@extend_schema(
description="Lists items",
parameters=[
OpenApiParameter(
"id",
required=True,
),
],
responses={
200: OpenApiResponse(description="Results"),
},
)
def list(self, request):
```
| closed | 2022-04-29T10:27:55Z | 2022-05-01T12:33:32Z | https://github.com/tfranzel/drf-spectacular/issues/723 | [] | bluelight773 | 4 |
graphql-python/graphene | graphql | 695 | Recipe for Snapshot'testing | I'm going to leave this here, and it can be closed. It's not really related to `graphene`, either, but because `snapshottest` is documented alongside `graphene` I figured this would be a handy place to share a recipe capturing type definitions.
```python
from graphql.utils.introspection_query import introspection_query
def get_type_definitions(schema, *type_names, **kwargs):
result = schema.execute(introspection_query, **kwargs)
assert not result.errors
typemap = {
type_['name']: type_
for type_ in result.data['__schema']['types']
}
return {type_name: typemap[type_name] for type_name in type_names}
def test_my_mutation_type(schema, session):
definitions = get_type_definitions(
schema,
str(MyMutationType),
str(MyMutationType.Input),
)
snapshot.assert_match(definitions)
```
While I'm not a fan of the fact that `graphene.ObjectType` overrides `type.__str__` in the broken-way that it does*, a user can also supply `MyMutationType._meta.name` (how I actually prefer to do it).
* [bonus] the broken `__str__` implementation:
```python
import graphene
class X(graphene.ObjectType):
class Meta:
abstract = True
print(X) # AttributeError: type object 'X' has no attribute '_meta'
```
Again, this is closable, just wanted to pass along the recipe. | closed | 2018-03-20T09:00:43Z | 2019-08-05T23:17:46Z | https://github.com/graphql-python/graphene/issues/695 | [
"wontfix",
"๐ documentation"
] | dfee | 2 |
pyeve/eve | flask | 1,278 | Eve is using Cerberus functionality that was deprecated in 2017 | ### Expected Behavior
Running the latest version of Eve with the latest version of Cerberus shouldn't raise deprecation warnings, at least not almost two year old deprecation warnings.
### Actual Behavior
Tell us what happens instead.
```
% python -Wd
<snipped non-Eve-related deprecation warning>
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from eve import Eve
/path/to/Cerberus-1.3.1-py3.6.egg/cerberus/validator.py:1559:
DeprecationWarning: Methods for type testing are deprecated, use TypeDefinition
and the 'types_mapping'-property of a Validator-instance instead.
```
This warning was introduced in [Cerberus commit ce1ef4f9](https://github.com/pyeve/cerberus/commit/ce1ef4f9ecce7853c903a4eccb4f97a6af16fbb2) in August 2017, almost two years ago.
Our unit testing system unloads & restarts Eve for every test, to ensure test isolation. This means that for every test we get this deprecation warning.
### Environment
* Python version: 3.6.7
* Eve version: 0.9.1
| closed | 2019-05-29T12:17:47Z | 2022-03-17T18:07:50Z | https://github.com/pyeve/eve/issues/1278 | [] | sybrenstuvel | 11 |
tableau/server-client-python | rest-api | 774 | Having a permissions "Unknown" status | Hi,
Not sure if this is possible and if the documentation is out there for it, but I've noticed there are only two modes of Permissions right now based on the capability: `TSC.Permission.Mode.Allow` and `TSC.Permission.Mode.Deny`.
Within the GUI application, there is a way to have an unknown status for the capability, but I haven't seen it within this library. Is there a way to make the mode "Unknown"?
If not, will this be in an upcoming feature?
Thanks | open | 2021-01-14T22:32:57Z | 2021-02-23T01:17:02Z | https://github.com/tableau/server-client-python/issues/774 | [
"enhancement"
] | mbabatunde | 3 |
ansible/ansible | python | 84,148 | SSH mux does not distinguish different inventory files | ### Summary
I have to run an Ansible playbook on two sets of machines; each of them has an `inventory.ini` file that identifies them using their SSH config (the rest is the same).
When I actually ran it, I found that it ended up as the same playbook was run twice on the same set of machines (first inventory file used). After digging using Wireshark, I saw it only established a connection to the bastion of the first set of machines, and I could only see one ssh mux process in the background.
### Issue Type
Bug Report
### Component Name
ssh
### Ansible Version
```console
$ ansible --version
ansible [core 2.16.3]
config file = None
configured module search path = ['/home/unics/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules']
ansible python module location = /usr/lib/python3/dist-packages/ansible
ansible collection location = /home/unics/.ansible/collections:/usr/share/ansible/collections
executable location = /usr/bin/ansible
python version = 3.12.3 (main, Sep 11 2024, 14:17:37) [GCC 13.2.0] (/usr/bin/python3)
jinja version = 3.1.2
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
CONFIG_FILE() = None
PAGER(env: PAGER) = less
```
### OS / Environment
Ubuntu 24.04
### Steps to Reproduce
<!--- Paste example playbooks or commands between quotes below -->
Playbook:
```yaml (paste below)
- name: Install openvpn
hosts: bastion
gather_facts: true
become: true
tasks:
- name: debug host
debug:
var: ansible_ssh_common_args
- name: Check if openvpn@bastion service exists
systemd:
name: openvpn@bastion
state: started
register: service_status
ignore_errors: yes
- debug:
var: service_status
```
Inventory files (two are the same except ssh_conf path)
```
[all:vars]
ansible_ssh_common_args="-F ./vpc-unics-office/ssh_config -o ControlMaster=no"
global_comm_password="xxxx"
global_comm_ip="34.221.xx.xx"
vpc_cidr="10.1.0.0/16"
bastion_ip="10.1.100.10"
[static]
router ansible_host=router ansible_user=ubuntu
bastion ansible_host=bastion ansible_user=ubuntu
logger ansible_host=logger ansible_user=ubuntu
```
SSH config (two are the same except public IP and identity file)
```
Host *
StrictHostKeyChecking no
UserKnownHostsFile=/dev/null
User ubuntu
Host bastion 52.27.xx.xx
HostName 52.27.xx.xx
IdentityFile ./vpc-unics-office/id_rsa
```
Shell script for running it
```
ANSIBLE_FACT_CACHING=none ansible-playbook --inventory vpc-unics-office/inventory.ini inf/ansible/vpn_openvpn.yml --extra-vars vpc=unics-office --ssh-common-args='-o ControlMaster=no'
ANSIBLE_FACT_CACHING=none ansible-playbook --inventory vpc-unics-cloud/inventory.ini inf/ansible/vpn_openvpn.yml --extra-vars vpc=unics-cloud --flush-cache --ssh-common-args='-o ControlMaster=no'
```
I have OpenVPN up and running on one of the host but not even installed on the other. This shell script always gives the same result, both running or both dne, depending on which inventory used first.
### Expected Results
It should connect to the correct machine and give the true result. For the machine that has OpenVPN running, it should say OK, and for the machine without OpenVPN installed, it should report the error detail but not failing (since I set it to ignore errors). Since I have configured OpenVPN only on one machine, the result shouldn't be the same.
### Actual Results
```console
They are the same; either both are shown as running or both are shown as doesn't exist, which is not the truth. Due to the concern for private info in the -vvvv output, I'd rather not post it here.
```
### Code of Conduct
- [x] I agree to follow the Ansible Code of Conduct | open | 2024-10-20T10:31:17Z | 2024-10-22T15:10:19Z | https://github.com/ansible/ansible/issues/84148 | [
"bug",
"affects_2.16"
] | yuxiaolejs | 3 |
mouredev/Hello-Python | fastapi | 451 | ็ฝ่ต่ขซ้ปไธ็ปๆๆฌพ๏ผๅนณๅฐๆๆฌพไธ็ดๆ็คบๅคฑ่ดฅๆไนๅ็ฝ่ตๅนณๅฐๅบๆฌพ้้็ปดๆค้ฃๆงๅฎกๆ ธไธ็ปๆๆฌพๆไนๅ๏ผ | 
่ฏไฟกๅธฎๅบ้ปๅจ่ฏข+ๅพฎ๏ผxiaolu460570 ้ฃๆบ๏ผ@lc15688
ๅ่ฎฐ๏ผๅช่ฆๆจ่ตข้ฑไบ๏ผ้ๅฐไปปไฝไธ็ปไฝ ๆ็ฐ็ๅๅฃ๏ผๅบๆฌ่กจๆๆจ่ขซ้ปไบใ
ๅฆๆไฝ ๅบ็ฐไปฅไธ่ฟไบๆ
ๅต๏ผ่ฏดๆไฝ ๅทฒ็ป่ขซ้ปไบ๏ผโ โ
ใ1ใ้ๅถไฝ ่ดฆๅท็้จๅๅ่ฝ!ๅบๆฌพๅๅ
ฅๆฌพ็ซฏๅฃๅ
ณ้ญ๏ผไปฅๅ่ฎฉไฝ ๅ
ๅผ่งฃๅผๅๆฌพ้้็ญ็ญ!
ใ2ใๅฎขๆๆพไธไบๅๅฃ่ฏดไปไน็ณป็ป็ปดๆค๏ผ้ฃๆงๅฎกๆ ธ็ญ็ญๅๅฃ๏ผๅฐฑๆฏไธ่ฎฉๅๆฌพ!
ใ็ฝ่ต่ขซ้ปๆไนๅใใ็ฝ่ต่ตขไบๅนณๅฐไธ็ปๅบๆฌพใใ็ณป็ปๆดๆฐใใๅๆฌพๅคฑ่ดฅใใๆณจๅๅผๅธธใใ็ฝ็ปๆณขๅจใใๆไบคๅคฑ่ดฅใ
ใๅๆณจไธบๅๅฝใใๅๆณจๆชๆดๆฐใใๅบๆฌพ้้็ปดๆคใ ใๆๅๅๆตๆฐดใ ใๅ
ๅผๅ็ญ็้้ขใ
ๅ
ณไบ็ฝไธ็ฝ่ตๅจฑไนๅนณๅฐ่ตข้ฑไบๅ็งๅๅฃไธ็ปๅบๆฌพๆๆฐ่งฃๅณๆนๆณ
ๅ่ฎฐ๏ผๅช่ฆไฝ ่ตข้ฑไบ๏ผ้ๅฐไปปไฝไธ็ปไฝ ๆ็ฐ็ๅๅฃ๏ผๅบๆฌ่กจๆไฝ ๅทฒ็ป่ขซ้ปไบใ
็ฌฌไธ๏ผๆๆฌพ่ขซๆ็ป๏ผๅ็งๅๅฃ็็ฑ๏ผๅฐฑๆฏไธ่ฎฉๅบๆฌพ๏ผ่ฎฉๆๅๆๆตๆฐด็ญ็ญ๏ผ
็ฌฌไบ๏ผ้ๅถไฝ ่ดฆๅท็้จๅๅ่ฝ๏ผๅบๆฌพๅๅ
ฅๆฌพ็ซฏๅฃๅ
ณ้ญ๏ผไปฅๅ่ฎฉไฝ ๅ
ๅผ่งฃๅผๅๆฌพ้้็ญ็ญ๏ผ
็ฌฌไธ๏ผๅฎขๆๆพไธไบๅๅฃ่ฏดไปไน็ณป็ป็ปดๆค๏ผ้ฃๆงๅฎกๆ ธ็ญ็ญๅๅฃ๏ผๅฐฑๆฏไธ่ฎฉๅๆฌพ๏ผ
็ฌฌๅ๏ผ็กฎ่ฎค่ขซ้ปไบ๏ผๅบ่ฏฅๆไนๅ๏ผ ๏ผๆพไธไธๅข้ๆไฝ ๆไนๅๆฝๅๆๅคฑ๏ผๅบๆฌพไธๆๅไธๆถๅไปปไฝๅๆ่ดน็จ๏ผ
็ฌฌไบ๏ผไฟๆๅท้๏ผไธ่ฆๅๅฎขๆไบๅต๏ผ้ฒๆญขๅท่ขซๅป็ปใ
็ฌฌๅ
ญ๏ผ็จณไฝๅฎขๆๆ
็ปช๏ผๅช่ฆๅนณๅฐ่งๅพไฝ ่ฟๅจๆญฃๅธธๆธธๆใ
็ฌฌไธ๏ผๅฟฝๆ ๅฎขๆ๏ผไธ็ปๆ็่กจ่พพ่ชๅทฑ็็ปๆตๅฎๅ๏ผไธ้ๅฝ็่ฃ
ๅปใ
็ฌฌๅ
ซ๏ผๅช่ฆๅฏไปฅ็ป้๏ผๅฏไปฅ่ฝฌๆข้ขๅบฆ๏ผๅฉไธ็ไบค็ปๆไปฌ๏ผๆไปฌๅฐไผๅธฎไฝ ๆๆๅคฑ้ๅฐๆไฝใ
ๅข้็ป้ชๆทฑ๏ผ8ๅนด่ๅข้๏ผๅข้ๆๆง็ไธไบๆๆฐๅบๆฌพๆๆฏๅฏไปฅๅธฎๅฐไฝ ใ
ๅช่ฆ่ดฆๅท่ฟ่ฝๆญฃๅธธ็ปๅฝๆไปฌๅข้ๆ80%็ๆๆกๅธฎไฝ ๅบๆฌพๆๅใ๏ผๆณจ๏ผๆไปฌๅข้ๆฏๅ
ๅบๆฌพๅๆถ่ดน๏ผ่ฎฉไฝ ๅ
ไบค่ดน็่ฏทๅฟๅๆฌกไธๅฝๅ้ช๏ผ่ฏไฟกๅไฝ๏ผ๏ผ
็ฝ่ต่ฆ็ฉๅฐฑ็ฉๅฎ้
็ๅฎไฝๅนณๅฐ่ต้ๅฎๅ
จไฟ้-ๅช่ฆๆไบบ่ฏด่ฝ็พๅ็พ่ตข็ๅๅ็งๅฝฉ้่ฏฑๆ็ญ็ญโฆ..้ฝๆฏๆ็ชๅฐ้้ธกๅฐ่ต้่ดฆๅท่ขซ้ป่ขซๅป็ปๅชๆฏ่ฟๆฉ็ไบ#่ฟ็ฆป่ตๅ | closed | 2025-03-03T13:33:11Z | 2025-03-04T08:41:40Z | https://github.com/mouredev/Hello-Python/issues/451 | [] | 376838 | 0 |
vaexio/vaex | data-science | 1,650 | [BUG-REPORT] - ArrowInvalid: offset overflow while concatenating arrays | Thank you for reaching out and helping us improve Vaex!
Before you submit a new Issue, please read through the [documentation](https://docs.vaex.io/en/latest/). Also, make sure you search through the Open and Closed Issues - your problem may already be discussed or addressed.
**Description**
When doing operations on large dataframes with long string columns (~500 characters), slicing the dataframe results in the error
```
ArrowInvalid: offset overflow while concatenating arrays
```
This doesn't happen with small datasets, and also doesn't happen with short strings. It's explicitly a problem with many large strings.
Example
```
import vaex
from vaex.dataframe import DataFrame
from random import random
import numpy as np
x = str(random())*25
def create_test_df(
num_samples: int = 10000000, num_classes: int = 20
):
id_column = np.arange(num_samples)
val1 = np.random.randint(0, 20, size=num_samples)
val2 = np.random.randint(0, 20, size=num_samples)
text_data = [x for _ in range(num_samples)]
score = np.random.uniform(0, 1.0, size=num_samples)
matrix = {
'id': id_column,
'val1': val1,
'val2': val2,
'score': score,
'text': text_data
}
return vaex.from_arrays(**matrix)
d2 = create_test_df(num_samples=10000000)
d2.sort(by='score')[0:500].to_records()
```
In the trace, I see this
```
~/.pyenv/versions/3.9.6/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/vaex/column.py in __getitem__(self, slice)
283 take_indices[mask] = 0
284 if isinstance(ar_unfiltered, supported_arrow_array_types):
--> 285 ar = ar_unfiltered.take(vaex.array_types.to_arrow(take_indices))
286 else:
287 ar = ar_unfiltered[take_indices]
```
which lead me to some investigation and found [this](https://github.com/huggingface/datasets/issues/615) and [this](https://github.com/huggingface/datasets/pull/645/files) - I think you need to switch to using `.slice` instead of `.take`
Doย you have any ideas for a workaround I can use for now?
**Software information**
- Vaex version (`import vaex; vaex.__version__)`:
```
{'vaex': '4.5.0',
'vaex-core': '4.5.1',
'vaex-viz': '0.5.0',
'vaex-hdf5': '0.10.0',
'vaex-server': '0.6.1',
'vaex-astro': '0.9.0',
'vaex-jupyter': '0.6.0',
'vaex-ml': '0.14.0'}
```
- Vaex was installed via: pip / conda-forge / from source - pip
- OS: Macos big sur
**Additional information**
Please state any supplementary information or provide additional context for the problem (e.g. screenshots, data, etc..).
| closed | 2021-10-15T20:38:00Z | 2023-02-28T22:53:54Z | https://github.com/vaexio/vaex/issues/1650 | [
"bug"
] | Ben-Epstein | 11 |
huggingface/pytorch-image-models | pytorch | 1,984 | [FEATURE] Add SigLIP weights | Hello,
Google recently released weights of the [SigLIP paper](https://arxiv.org/abs/2303.15343) (see [here](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/SigLIP_demo.ipynb)) with amazing zero shot performance (in particular a shape optimised ViT-L).
Do you have any plan to integrate them in timm ?
Thanks,
Simon | closed | 2023-10-08T06:44:41Z | 2023-10-18T23:41:23Z | https://github.com/huggingface/pytorch-image-models/issues/1984 | [
"enhancement"
] | SimJeg | 2 |
vaexio/vaex | data-science | 1,367 | traitlets.traitlets.TraitError: The 'min' trait of an Axis instance expected a float, not the datetime64 numpy.datetime64 | **Description**
I'm trying to plot the following data:
x axis: dates
y axis: co2 data (floats)
Using the following code:
```
import numpy as np
import datetime
import vaex
df = vaex.from_csv('data.csv')
def convert_to_datetime(d):
dt = datetime.datetime.strptime(str(d),"%Y-%m-%d %H:%M:%S%z")
dt = dt.replace(tzinfo=None)
return np.datetime64(dt)
df['time2'] = df.time.apply(convert_to_datetime)
df.plot_widget(df.time2 , df.co2, show=True)
```
When run, I get the following error:
```
traitlets.traitlets.TraitError: The 'min' trait of an Axis instance expected a float, not the datetime64 numpy.datetime64('2018-11-27T15:07:48.001792').
```
I'm rather new to vaex, so any pointers would be most appreciated.
**Software information**
- {'vaex': '4.1.0', 'vaex-core': '4.1.0', 'vaex-viz': '0.5.0', 'vaex-hdf5': '0.7.0', 'vaex-server': '0.4.0', 'vaex-astro': '0.8.0', 'vaex-jupyter': '0.6.0', 'vaex-ml': '0.11.1'}
- Vaex was installed via: pip
- OS: Linux
| open | 2021-05-23T11:52:42Z | 2021-05-24T16:33:56Z | https://github.com/vaexio/vaex/issues/1367 | [] | chrisruk | 2 |
d2l-ai/d2l-en | deep-learning | 2,421 | Discussion Forum Not Showing up on Classic Branch | As the below image shows, none of the lessons on the classic website have functioning discussion forums (eg. http://classic.d2l.ai/chapter_recurrent-modern/beam-search.html). :
I've checked it on Firefox and Edge already, I don't think this is browser related.
| closed | 2022-12-28T16:41:41Z | 2023-01-06T11:27:15Z | https://github.com/d2l-ai/d2l-en/issues/2421 | [] | Vortexx2 | 2 |
zappa/Zappa | flask | 930 | [Migrated] Zappa support for permission boundaries? | Originally from: https://github.com/Miserlou/Zappa/issues/2196 by [christophersbarrett](https://github.com/christophersbarrett)
Does Zappa support adding a permission boundary onto the lambda execution role? | closed | 2021-02-20T13:24:40Z | 2024-04-13T19:36:48Z | https://github.com/zappa/Zappa/issues/930 | [
"no-activity",
"auto-closed"
] | jneves | 2 |
junyanz/pytorch-CycleGAN-and-pix2pix | computer-vision | 707 | I reduced the image size . but result is poor | Hello, I appreciate to your great research and implementations.
I would like to thank you for doing such a great job.
Anyway, I have a Question.
When I learned with 3x256x256 images , it was fine
but I learn with 1x50x50 image, it is poor
I want to learn with 1(channel) x 50 x 50 image from this model.
So I modify some options
images = A 4000 : B 4000
input_nc = 1
output_nc = 1
load size = 50
crop size = 50 (or preprocessing = none)
batch size = 64 ( when I learn with 3x256x256 image , it was 3)
epoch = 200
(Discriminator loss is very poor. this is got 0.00xx when 10~20 epoch.)
I want know that what I modified options is influence to poor generate
and if it is influenced my learn, I want to know how tune the options
Thank you.
| closed | 2019-07-17T08:17:48Z | 2019-07-19T03:52:11Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/707 | [] | Realdr4g0n | 2 |
pandas-dev/pandas | pandas | 60,802 | DOC: Specify what "non-null" means in DataFrame.info() | ### Pandas version checks
- [x] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html
### Documentation problem
Non-null is not specific
### Suggested fix for documentation
Link to documentation or specify exactly what non-null means. In particular, for float64s NaN are considered "null". And does it also represent NULLs in the Nullable integer types? https://pandas.pydata.org/docs/user_guide/integer_na.html
Pandas is not consistent with its terminology of NA, NULL, and NaN.
NaN is a floating point value that is not in the IEEE standard as a missing value.
R uses NA consistently and SQL uses NULL consistently in 3VL. | open | 2025-01-27T22:18:02Z | 2025-03-14T10:19:32Z | https://github.com/pandas-dev/pandas/issues/60802 | [
"Docs",
"Missing-data"
] | jxu | 4 |
MilesCranmer/PySR | scikit-learn | 36 | Symbolic deep learning | Trying to recreate the examples from this [paper](https://arxiv.org/abs/2006.11287)
PySR is always predicting scalars as a low complexity solution, which doesn't make much sense, can you please elaborate on that?
And what is wrong why I'm unable to get the right expression?
```
Cycles per second: 3.050e+03
Progress: 19 / 20 total iterations (95.000%)
Hall of Fame:
-----------------------------------------
Complexity Loss Score Equation
1 1.278e-01 -9.446e-02 -0.08741549
2 1.165e-01 9.256e-02 square(-0.18644808)
3 2.592e-02 1.503e+00 (x0 * -0.2923665)
5 1.682e-02 2.163e-01 ((-0.10430038 * x0) * x2)
8 1.576e-02 2.176e-02 (1.6735333 * sin((-0.067048885 * x0) * x2))
```
The code used to generate this is:
```
import numpy as np
from pysr import pysr, best
# Dataset
X = np.array(messages_over_time[-1][['dx', 'dy', 'r', 'm1', 'm2']]) # Taken from this notebook https://github.com/MilesCranmer/symbolic_deep_learning/blob/master/GN_Demo_Colab.ipynb
y = np.array(messages_over_time[-1]['e64'])
# Learn equations
equations = pysr(X, y, niterations=5,
binary_operators=["plus", "mult" , 'sub', 'pow', 'div'],
unary_operators=[
"cos", "exp", "sin", 'neg', 'square', 'cube', 'exp',
"inv(x) = 1/x"], batching=True, batchSize=1000)
print(best(equations))
``` | open | 2021-03-04T19:49:15Z | 2024-11-21T09:00:09Z | https://github.com/MilesCranmer/PySR/issues/36 | [
"question"
] | abdalazizrashid | 14 |
nl8590687/ASRT_SpeechRecognition | tensorflow | 222 | ่ฏญ้ณ็ๆฎต้ฟๅบฆ | ๆต่ฏ็ๆถๅ่พๅ
ฅ็่ฏญ้ณ็ๆฎต้ฟๅบฆๆถๅบๅฎ็ๅ๏ผๆไฝฟ็จtest.pyๆต่ฏไธไธชๅๅ ๅ้็่ฏญ้ณๆไปถๆพ็คบๆ ผๅผๅคงๅฐไธๅน้
ใ
`Traceback (most recent call last):
File "test.py", line 34, in <module>
r = ms.RecognizeSpeech_FromFile('/home/user/XiJing/XiJing (1)/้ณ้ข1.wav')
File "/home/user/ASRT_v0.6.1/SpeechModel251.py", line 382, in RecognizeSpeech_FromFile
r = self.RecognizeSpeech(wavsignal, fs)
File "/home/user/ASRT_v0.6.1/SpeechModel251.py", line 362, in RecognizeSpeech
r1 = self.Predict(data_input, input_length)
File "/home/user/ASRT_v0.6.1/SpeechModel251.py", line 304, in Predict
base_pred = self.base_model.predict(x = x_in)
File "/home/user/anaconda3/envs/ml/lib/python3.6/site-packages/keras/engine/training.py", line 1441, in predict
x, _, _ = self._standardize_user_data(x)
File "/home/user/anaconda3/envs/ml/lib/python3.6/site-packages/keras/engine/training.py", line 579, in _standardize_user_data
exception_prefix='input')
File "/home/user/anaconda3/envs/ml/lib/python3.6/site-packages/keras/engine/training_utils.py", line 145, in standardize_input_data
str(data_shape))
ValueError: Error when checking input: expected the_input to have shape (1600, 200, 1) but got array with shape (164184, 200, 1)` | open | 2020-11-20T03:40:25Z | 2021-03-28T11:17:19Z | https://github.com/nl8590687/ASRT_SpeechRecognition/issues/222 | [] | tingxin1 | 3 |
drivendataorg/cookiecutter-data-science | data-science | 156 | integration with mlflows and snakemake | I think that logging via mlfows and make via snakemake can enhance this project | closed | 2018-12-16T18:54:00Z | 2019-04-15T11:30:52Z | https://github.com/drivendataorg/cookiecutter-data-science/issues/156 | [] | chanansh | 1 |
Miserlou/Zappa | flask | 1,652 | ResourceNotFoundException: An error occurred (ResourceNotFoundException) when calling the DescribeLogStreams | When attempting to tail logs on dev the tailing is succesfull, however tailing environment on another account fails with:
```
Traceback (most recent call last):
File "/opt/kidday/env/lib/python3.6/site-packages/zappa/cli.py", line 2693, in handle
sys.exit(cli.handle())
File "/opt/kidday/env/lib/python3.6/site-packages/zappa/cli.py", line 504, in handle
self.dispatch_command(self.command, stage)
File "/opt/kidday/env/lib/python3.6/site-packages/zappa/cli.py", line 595, in dispatch_command
force_colorize=self.vargs['force_color'] or None,
File "/opt/kidday/env/lib/python3.6/site-packages/zappa/cli.py", line 1064, in tail
filter_pattern=filter_pattern,
File "/opt/kidday/env/lib/python3.6/site-packages/zappa/core.py", line 2745, in fetch_logs
orderBy='LastEventTime'
File "/opt/kidday/env/lib/python3.6/site-packages/botocore/client.py", line 320, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/opt/kidday/env/lib/python3.6/site-packages/botocore/client.py", line 623, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.errorfactory.ResourceNotFoundException: An error occurred (ResourceNotFoundException) when calling the DescribeLogStreams operation: The specified log group does not exist.
```
Checking the deployment with status (zappa status prod) yields:
`No Lambda src-prod detected in eu-west-1 - have you deployed yet?`
Although the deployment has been succesful and the Lambda name can be found on AWS console itself.
## Possible Fix
Send in jneves! :D
| open | 2018-10-12T13:14:23Z | 2018-10-12T13:14:23Z | https://github.com/Miserlou/Zappa/issues/1652 | [] | 4lph4-Ph4un | 0 |
CorentinJ/Real-Time-Voice-Cloning | pytorch | 538 | New pretrained synthesizer model (tensorflow) | Trained on LibriSpeech, using the current synthesizer (tensorflow). This performs similarly to the current model, with fewer random gaps appearing in the middle of synthesized utterances. It handles short input texts better too.
### Download link: https://www.dropbox.com/s/3kyjgew55c4yxtf/librispeech_270k_tf.zip?dl=0
Unzip the file and move the `logs-pretrained` folder to `synthesizer/saved_models`.
I am not going to provide scripts to reproduce the training. For anyone interested, you will need to curate LibriSpeech to have more consistent prosody. This is what I did when running synthesizer_preprocess_audio.py:
1. In synthesizer/hparams.py, set `silence_min_duration_split=0.05`
2. Right before [this line](https://github.com/CorentinJ/Real-Time-Voice-Cloning/blob/8f71d678d2457dffc4d07b52e75be11433313e15/synthesizer/preprocess.py#L182), run `encoder.preprocess_wav()` on each wav, this will use voice activation detection to trim silences (see #501). Compare the lengths of the "before" and "after" wavs. If they don't match then it means a silence is detected and it is discarded. I keep the "before" wav if the lengths match.
3. Post-process `datasets_root/SV2TTS/synthesizer/train.txt` to include utterances between 225 and 600 mel frames (2.8 to 7.5 sec). This leaves 48 hours of training data.
4. Train from scratch for about 270k steps. I used a batch size of 12 because of limited GPU memory. | closed | 2020-09-30T07:59:31Z | 2021-12-04T06:01:56Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/538 | [] | ghost | 3 |
kynan/nbstripout | jupyter | 89 | Possible to design a "smudge" git filter that reverses the stripping process? | I work on multiple servers for my research, and use git to sync my project repository between these servers servers. Sometimes this means I want to work on the same jupyter notebook on different servers. The problem with this is, if I make some changes on server A and want just the *code* changes on server B, after pushing and pulling the server B output is stripped.
Would it be possible to design a "smudge" filter that *reverses* the stripping process -- i.e., when a merge between a stripped repository file and un-stripped work directory file is performed, the code changes are made but the output is preserved?
I'm guessing the answer is: this is super non-trivial. But just a thought. An example use case: you want to append a couple cells onto a large notebook whilst on server B, then transport those changes to server A without deleting the output of the (completely untouched) rest of the notebook. | closed | 2018-11-01T07:14:37Z | 2021-04-25T17:45:53Z | https://github.com/kynan/nbstripout/issues/89 | [
"type:enhancement",
"resolution:obsolete"
] | lukelbd | 3 |
jschneier/django-storages | django | 640 | GoogleCloud Storage not respecting ACL setting when using default_storage class Django | When using default_storage class to write a file to google bucket, it is created as projectPrivate even if the acl setting is publicRead. | closed | 2018-12-20T10:27:17Z | 2019-09-10T07:35:15Z | https://github.com/jschneier/django-storages/issues/640 | [
"bug",
"google"
] | mj8894 | 3 |
zihangdai/xlnet | tensorflow | 142 | How long does it take to pretrain xlnet? | I have 8 TESLA K80 GPU with 11GB RAM each.
I am running `sudo python3 train_gpu.py --record_info_dir=fix3/tfrecords --train_batch_size=32 --seq_len=512 --reuse_len=256 --mem_len=384 --perm_size=256 --n_layer=6 --d_model=768 --d_embed=768 --n_head=6 --d_head=64 --d_inner=3072 --untie_r=True --model_dir=my_model --uncased=False --num_predict=85 `.
How long does this command take to pretrain xlnet? | closed | 2019-07-09T06:54:58Z | 2019-07-10T07:36:25Z | https://github.com/zihangdai/xlnet/issues/142 | [] | Bagdu | 0 |
plotly/dash-bio | dash | 737 | Oncoprint error: Cannot read properties of undefined (reading 'displayName') | Hi Plotly / Dash,
I'm facing the error message `Cannot read properties of undefined (reading 'displayName')` for some alteration types in the oncoprint plot.
I'm quite convinced my code has worked before, but I can't tell you which version it worked/broke.
I've tried to create a minimal example below:
```python
from dash import dash, html
from dash_bio import OncoPrint
app = dash.Dash(__name__)
app.layout = html.Div(
OncoPrint(
id="oncoprint-plot",
data=[{
'alteration': None,
# When changing below type to 'INFRAME', 'MISSENSE' or 'TRUNC' the oncoprint plot works, but
# any of 'AMP', 'GAIN', 'HETLOSS' or 'HMODEL' breaks the oncoprint plot with the error
'type': 'HMODEL',
'gene': 'TEST',
'sample': 'TEST-sample'
}],
showlegend=False,
showoverview=False,
),
)
if __name__ == '__main__':
app.run_server(debug=True)
```
I'm using:
Python 3.9.5
Dash version 2.9.3
Plotly version 5.9.0
| closed | 2023-04-30T09:43:22Z | 2023-06-18T14:10:01Z | https://github.com/plotly/dash-bio/issues/737 | [] | Donnyvdm | 1 |
predict-idlab/plotly-resampler | data-visualization | 169 | Add Mypy | Not sure if this is wanted or not but I was playing around with this and would either need to add a lot of `type: ignores` to the existing code or drop support for python 3.7 in the version where mypy is included as a few of the stubs needed to correctly type out some code do not support 3.7
Happy to hack on this if wanted | open | 2023-02-03T00:42:28Z | 2023-02-06T02:28:45Z | https://github.com/predict-idlab/plotly-resampler/issues/169 | [
"documentation",
"enhancement"
] | jayceslesar | 2 |
Lightning-AI/pytorch-lightning | data-science | 20,670 | Outdated Versioning Policy | ### ๐ Documentation
The [Compatibility Matrix](https://lightning.ai/docs/pytorch/stable/versioning.html#compatibility-matrix) on the Versioning Policy documentation page does not mention the 2.5 release series.
The v2.5 release notes [say](https://github.com/Lightning-AI/pytorch-lightning/releases/tag/2.5.0):
> Lightning 2.5 comes with improvements on several fronts, with **zero** API changes
The v2.5.1 release notes [say](https://github.com/Lightning-AI/pytorch-lightning/releases/tag/2.5.1):
> bump: testing with latest torch 2.6 (https://github.com/Lightning-AI/pytorch-lightning/pull/20509)
Does this mean that the 2.5 series is compatible with PyTorch 2.6.x, or does the "early visibility" mentioned in the PR refer to upcoming support? Is PyTorch 2.5.x supported?
cc @lantiga @borda | open | 2025-03-24T14:49:04Z | 2025-03-24T14:51:12Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20670 | [
"docs",
"needs triage"
] | iwr-redmond | 0 |
nonebot/nonebot2 | fastapi | 3,177 | Plugin: ๅๆ ้ฟๅผฅ้ไฝ | ### PyPI ้กน็ฎๅ
nonebot-plugin-amitabha
### ๆไปถ import ๅ
ๅ
nonebot_plugin_amitabha
### ๆ ็ญพ
[{"label":"ๅฟตไฝ","color":"#fae1a9"},{"label":"้ฟๅผฅ้ไฝ","color":"#fab6a9"}]
### ๆไปถ้
็ฝฎ้กน
```dotenv
send_interval=5
```
### ๆไปถๆต่ฏ
- [ ] ๅฆ้้ๆฐ่ฟ่กๆไปถๆต่ฏ๏ผ่ฏทๅพ้ๅทฆไพงๅพ้ๆก | closed | 2024-12-09T15:12:35Z | 2024-12-17T15:05:33Z | https://github.com/nonebot/nonebot2/issues/3177 | [
"Plugin",
"Publish"
] | Kaguya233qwq | 8 |
tqdm/tqdm | jupyter | 856 | Support skins | - [x] I have marked all applicable categories:
+ [x] new feature request
- [x] I have visited the [source website], and in particular
read the [known issues]
- [x] I have searched through the [issue tracker] for duplicates
https://github.com/verigak/progress/blob/master/progress/bar.py has skins. I guess we should add them to tqdm too. | closed | 2019-11-30T08:01:03Z | 2019-11-30T14:01:24Z | https://github.com/tqdm/tqdm/issues/856 | [
"duplicate ๐",
"question/docs โฝ",
"need-feedback ๐ข"
] | KOLANICH | 2 |
zappa/Zappa | flask | 458 | [Migrated] IAM Role creation fails when generated role name is longer than 64 characters | Originally from: https://github.com/Miserlou/Zappa/issues/1223 by [philvarner](https://github.com/philvarner)
## Context
I created a deploy target:
```
"sandbox_enqueuer": {
"project_name": "dev_enqueuer_surveygizmo",
```
which gave me this output when deploying:
```
bash-3.2$ zappa deploy sandbox_enqueuer
Calling deploy for stage sandbox_enqueuer..
Creating dev-enqueuer-surveygizmo-sandbox-enqueuer-ZappaLambdaExecutionRole IAM Role..
Error: Failed to manage IAM roles!
You may lack the necessary AWS permissions to automatically manage a Zappa execution role.
To fix this, see here: https://github.com/Miserlou/Zappa#using-custom-aws-iam-roles-and-policies
```
## Expected Behavior
Either the role would be created or the error would say that I needed to change my target or project_name to be shorter.
## Actual Behavior
Error message with not enough detail to resolve the issue.
## Possible Fix
Prior to attempting to create in IAM, an error would be presented that said I needed to change my target or project_name to be shorter.
## Steps to Reproduce
1. Created a deploy target:
```
"sandbox_enqueuer": {
"project_name": "dev_enqueuer_surveygizmo",
```
1. zappa deploy sandbox_enqueuer
## Your Environment
n/a | closed | 2021-02-20T08:35:08Z | 2024-04-13T16:18:18Z | https://github.com/zappa/Zappa/issues/458 | [
"no-activity",
"auto-closed"
] | jneves | 2 |
flairNLP/flair | nlp | 3,458 | [Question]: Resume training | ### Question
I'm trying to resume training according to :
[This code](https://github.com/flairNLP/flair/blob/8bcc3d9dac0b0e318e0bd0290af5a36f4d414fab/resources/docs/TUTORIAL_TRAINING_MORE.md?plain=1#L73)
where it says :
# 7. continue training at later point. Load previously trained model checkpoint, then resume
trained_model = SequenceTagger.load(path + '/checkpoint.pt')
# resume training best model, but this time until epoch 25
trainer.resume(trained_model,
base_path=path + '-resume',
max_epochs=25,
)
but resume is not defined in :
[class ModelTrainer(Pluggable)]( https://github.com/flairNLP/flair/blob/8bcc3d9dac0b0e318e0bd0290af5a36f4d414fab/flair/trainers/trainer.py#L41)
I'm sure it's a common task using your awesome library yet I cannot get it working.
Any information would be very appreciated. | open | 2024-05-17T07:28:23Z | 2024-06-24T02:20:42Z | https://github.com/flairNLP/flair/issues/3458 | [
"question"
] | alfredwallace7 | 5 |
gradio-app/gradio | deep-learning | 10,722 | Antivirus Flagging frpc_darwin_arm64_v0.3 as Infostealer Breaks Gradio Share Functionality | Hello,
Our corporate security solution (e.g., SentinelOne Cloud) is flagging the file located at:
```
/opt/homebrew/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/frpc_darwin_arm64_v0.3
```
as an "Infostealer." This file is a critical component used by Gradio's share functionality to create a tunnel for exposing local interfaces. Because the file is being blocked, the share functionality, which enables remote access to our local interface, is not operating correctly.
Could you please advise if there is an official fix for this issue, or if there is an option to make this component optional?
Thank you. | closed | 2025-03-04T01:23:58Z | 2025-03-19T19:56:41Z | https://github.com/gradio-app/gradio/issues/10722 | [
"bug",
"pending clarification"
] | Twodragon0 | 5 |
pbugnion/gmaps | jupyter | 74 | Full options for heatmap | It would be good to give access to the following options:
- dissipating
- opacity
- gradient
See, eg. issue #73 .
| closed | 2016-07-24T15:14:01Z | 2016-07-30T09:50:24Z | https://github.com/pbugnion/gmaps/issues/74 | [
"enhancement"
] | pbugnion | 1 |
wandb/wandb | data-science | 9,270 | [Bug]: wandb.login() hangs indefinitely under certain conditions | ### Describe the bug
Problem:
It seems that wandb.login() sometimes hangs forever. I've observed it happen under these conditions (not comprehensive):
* Multiple independent processes are simultaneously trying to use wandb.login() with the same key & project.
* Or when I'm training hundreds of models sequentially, it is likely to hang on one of them & obstruct the whole process.
Expected Behavior:
Handle login requests successfully, and if that is not possible due to limitations of the plan (idk if this is true) then simply raise an error.
My Setup:
* wandb version = 0.19.3
* OS = Linux
* Plan: Free
P.S. [wandb.login(timeout) arg](https://docs.wandb.ai/ref/python/login/) says: "timeout | (int, optional) Number of seconds to wait for user input."
I don't know what this means, but I'd like to know if this timeout also applies to waiting for the server to respond, so that a workaround could be `wandb.login(key=key, timeout=X)`?
| open | 2025-01-15T21:31:19Z | 2025-02-10T20:05:52Z | https://github.com/wandb/wandb/issues/9270 | [
"ty:bug",
"a:sdk",
"c:sdk:login"
] | profPlum | 15 |
jupyterlab/jupyter-ai | jupyter | 492 | Missing/incorrect formatting in the Jupyternaut response | <!-- Welcome! Thank you for contributing. These HTML comments will not render in the issue.
Before creating a new issue:
* Search for relevant issues
* Follow the issue reporting guidelines:
https://jupyterlab.readthedocs.io/en/latest/getting_started/issue.html
-->
## Description
I am using the jupyter ai extension with a custom model provider as per steps in https://jupyter-ai.readthedocs.io/en/latest/users/index.html#custom-model-providers
I have not modified the output format in the custom model provider, the provider looks like this:
```
class DESCOLLMProvider(BaseProvider, Llama2Chat):
id = "desco_llama_provider"
name = "desco llama provider"
model_id_key = "model_id"
models = [
"codellama-7B-Instruct",
]
def __init__(self, **kwargs):
llm = LlamaCpp(
model_path="/path/to/gguf/mode/file",
temperature=0.2,
max_tokens=640,
top_p=0.0,
verbose=True, # Verbose is required to pass to the callback manager
n_ctx=1024,
top_k=1,
n_gpu_layers=100,
streaming=False
)
super().__init__(model_id="desco_llama_provider", llm=llm)
```
## Reproduce
<!--Describe step-by-step instructions to reproduce the behavior-->
1. Select the registered custom model
2. Provide it with different inputs
3. After few Q&A iterations, the formatting of the response and subsequent response is garbled.
Actual response:
Once I clear the bot history, change model and revert the model back to the custom model, the formatting looks correct (which should be the ideal expected output). Now:
<!--Describe how you diagnosed the issue. See the guidelines at
https://jupyterlab.readthedocs.io/en/latest/getting_started/issue.html -->
## Expected behavior
Output format to look like:
## Context
<!--Complete the following for context, and add any other relevant context-->
- Operating System and version: Fedora Linux 8
- Browser and version: Chrome build 119.0.6045.160
- JupyterLab version: 4.0.9
-
<!--The more content you provide, the more we can help!-->
<details><summary>Generated prompt for the correct rendering</summary>
<pre>
Prompt after formatting:
System: You are Jupyternaut, a conversational assistant living in JupyterLab to help users.
You are not a language model, but rather an application built on a foundation model from desco llama provider called desco_llama_provider.
You are talkative and you provide lots of specific details from the foundation model's context.
You may use Markdown to format your response.
Code blocks must be formatted in Markdown.
Math should be rendered with inline TeX markup, surrounded by $.
If you do not know the answer to a question, answer truthfully by responding that you do not know.
The following is a friendly conversation between you and a human.
Human: given a pandas dataframe with columns id, name, salary, write code to find the name with Nth highest salary
AI: To find the name with the Nth highest salary in a pandas dataframe, you can use the `nlargest` method.
Here is an example:
import pandas as pd
# create sample data
data = {'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'], 'salary': [5000, 60000, 40000]}}
# find the name with the Nth highest salary
N = 2 # find the name with the 2nd highest salary
result = data.nlargest(N, 'salary'), on='id')
print(result))
Human: given a pandas dataframe with columns id, name, salary, write code to find the name with Nth highest salary
AI: Here is some sample code that demonstrates how to find the name with the Nth highest salary in a pandas DataFrame:
import pandas as pd
# create sample data
data = {'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'], 'salary': [5000, 60000, 400000]}}
# find the name with the Nth highest salary
N = 2 # find the name with the 2nd highest salary
result = data.nlargest(N, 'salary'), on='id')
print(result))
Human: write code to make a http POST call to example101.com
AI: Here is an example of how you can make a HTTP POST call using the `requests` library in Python:
```
import requests
url = "https://example101.com"
payload = {
"key": "value",
}
headers = {
'Content-Type': 'application/json',
}
response = requests.post(url, data=payload, headers=headers))
print(response.text))
```
</pre>
</details>
<details><summary>Generated prompt for the incorrect rendering</summary>
<pre>
> Entering new ConversationChain chain...
Prompt after formatting:
System: You are Jupyternaut, a conversational assistant living in JupyterLab to help users.
You are not a language model, but rather an application built on a foundation model from desco llama provider called desco_llama_provider.
You are talkative and you provide lots of specific details from the foundation model's context.
You may use Markdown to format your response.
Code blocks must be formatted in Markdown.
Math should be rendered with inline TeX markup, surrounded by $.
If you do not know the answer to a question, answer truthfully by responding that you do not know.
The following is a friendly conversation between you and a human.
Human: convert numpy array to a dataframe
AI: You can convert a NumPy array to a pandas DataFrame using the `to_df()` method of the NumPy array.
Here is an example:
import numpy as np
# create a NumPy array
arr = np.array([1,2],[3,4])
# convert the NumPy array to a pandas DataFrame
df = arr.to_df()
print(df))
Human: write code to make a http POST call to example101.com
AI: Here is an example of how you can make a HTTP POST call to example101.com using Python:
import requests
url = "https://example101.com"
payload = {
"key": "value",
}
response = requests.post(url, data=payload))
print(response.text))
</pre>
</details>
As we can see, "```" is missing around the code snippet in the response rendered in the second prompt.
Is this expected? | open | 2023-11-28T12:44:06Z | 2024-01-26T01:13:39Z | https://github.com/jupyterlab/jupyter-ai/issues/492 | [
"bug"
] | sundaraa-deshaw | 3 |
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