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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
numba/numba | numpy | 9,424 | Support math.nextafter in nopython mode | <!--
Thanks for opening an issue! To help the Numba team handle your information
efficiently, please first ensure that there is no other issue present that
already describes the issue you have
(search at https://github.com/numba/numba/issues?&q=is%3Aissue).
-->
## Feature request
<!--
Please include details of the feature you would like to see, why you would
like to see it/the use case.
-->
`math.nextafter` was added in Python 3.9. However, numba doesn't seem to support it in nopython mode. Can support please be added? | closed | 2024-02-07T18:02:20Z | 2024-04-26T16:32:23Z | https://github.com/numba/numba/issues/9424 | [
"feature_request",
"good first issue"
] | groutr | 8 |
widgetti/solara | fastapi | 1,013 | Potential issue: WebSocket already closed error | Solara attempts to send data over a closed WebSocket connection and then tries to close an already closed connection
This might be related to https://github.com/widgetti/solara/issues/1012
### Steps to Reproduce
While it's challenging to reproduce these issues consistently, I've observed them most frequently when:
Using the application for an extended period (several hours)
When the browser/client experiences network instability
When browser tabs are closed abruptly
Possibly when using multiple tabs or rapidly reloading the application
### Additional Context
The application appears to function correctly despite these errors, but the logs are filled with these exceptions. These appear to be race conditions in the WebSocket and page connection handling where:
Solara attempts to disconnect a page that's already disconnected
Solara attempts to send data through or close WebSocket connections that are already closed
I'm not sure if these are known issues or if there are recommended ways to handle them. Any insights or suggestions would be greatly appreciated!
### Stack trace
<details>
<summary> click to see stacktrace </summary>
```
ERROR: Error sending message: , closing websocket
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 331, in asgi_send
await self.send(data) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 620, in send
await self.ensure_open()
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 930, in ensure_open
raise self.connection_closed_exc()
websockets.exceptions.ConnectionClosedOK: received 1005 (no status received [internal]); then sent 1005 (no status received [internal])
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 85, in send
await self._send(message)
File "/app/.venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 39, in sender
await send(message)
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 344, in asgi_send
raise ClientDisconnected from exc
uvicorn.protocols.utils.ClientDisconnected
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/solara/server/kernel.py", line 217, in send_websockets
ws.send(binary_msg)
File "/app/.venv/lib/python3.12/site-packages/solara/server/websocket.py", line 26, in send
self.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 180, in send_text
self.portal.call(self._send_text_exc, data)
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 290, in call
return cast(T_Retval, self.start_task_soon(func, *args).result())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 221, in _call_func
retval = await retval_or_awaitable
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 146, in _send_text_exc
await self.ws.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 165, in send_text
await self.send({"type": "websocket.send", "text": data})
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 88, in send
raise WebSocketDisconnect(code=1006)
starlette.websockets.WebSocketDisconnect
2025-03-02 22:19:30 - ERROR - Error sending message: , closing websocket
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 331, in asgi_send
await self.send(data) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 620, in send
await self.ensure_open()
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 930, in ensure_open
raise self.connection_closed_exc()
websockets.exceptions.ConnectionClosedOK: received 1005 (no status received [internal]); then sent 1005 (no status received [internal])
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 85, in send
await self._send(message)
File "/app/.venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 39, in sender
await send(message)
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 344, in asgi_send
raise ClientDisconnected from exc
uvicorn.protocols.utils.ClientDisconnected
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/solara/server/kernel.py", line 217, in send_websockets
ws.send(binary_msg)
File "/app/.venv/lib/python3.12/site-packages/solara/server/websocket.py", line 26, in send
self.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 180, in send_text
self.portal.call(self._send_text_exc, data)
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 290, in call
return cast(T_Retval, self.start_task_soon(func, *args).result())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 221, in _call_func
retval = await retval_or_awaitable
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 146, in _send_text_exc
await self.ws.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 165, in send_text
await self.send({"type": "websocket.send", "text": data})
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 88, in send
raise WebSocketDisconnect(code=1006)
starlette.websockets.WebSocketDisconnect
ERROR: Error closing websocket: Cannot call "send" once a close message has been sent.
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 331, in asgi_send
await self.send(data) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 620, in send
await self.ensure_open()
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 930, in ensure_open
raise self.connection_closed_exc()
websockets.exceptions.ConnectionClosedOK: received 1005 (no status received [internal]); then sent 1005 (no status received [internal])
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 85, in send
await self._send(message)
File "/app/.venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 39, in sender
await send(message)
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 344, in asgi_send
raise ClientDisconnected from exc
uvicorn.protocols.utils.ClientDisconnected
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/solara/server/kernel.py", line 217, in send_websockets
ws.send(binary_msg)
File "/app/.venv/lib/python3.12/site-packages/solara/server/websocket.py", line 26, in send
self.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 180, in send_text
self.portal.call(self._send_text_exc, data)
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 290, in call
return cast(T_Retval, self.start_task_soon(func, *args).result())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 221, in _call_func
retval = await retval_or_awaitable
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 146, in _send_text_exc
await self.ws.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 165, in send_text
await self.send({"type": "websocket.send", "text": data})
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 88, in send
raise WebSocketDisconnect(code=1006)
starlette.websockets.WebSocketDisconnect
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/solara/server/kernel.py", line 229, in send_websockets
ws.close()
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 157, in close
self.portal.call(self.ws.close)
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 290, in call
return cast(T_Retval, self.start_task_soon(func, *args).result())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 221, in _call_func
retval = await retval_or_awaitable
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 180, in close
await self.send({"type": "websocket.close", "code": code, "reason": reason or ""})
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 97, in send
raise RuntimeError('Cannot call "send" once a close message has been sent.')
RuntimeError: Cannot call "send" once a close message has been sent.
2025-03-02 22:19:30 - ERROR - Error closing websocket: Cannot call "send" once a close message has been sent.
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 331, in asgi_send
await self.send(data) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 620, in send
await self.ensure_open()
File "/app/.venv/lib/python3.12/site-packages/websockets/legacy/protocol.py", line 930, in ensure_open
raise self.connection_closed_exc()
websockets.exceptions.ConnectionClosedOK: received 1005 (no status received [internal]); then sent 1005 (no status received [internal])
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 85, in send
await self._send(message)
File "/app/.venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 39, in sender
await send(message)
File "/app/.venv/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 344, in asgi_send
raise ClientDisconnected from exc
uvicorn.protocols.utils.ClientDisconnected
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/solara/server/kernel.py", line 217, in send_websockets
ws.send(binary_msg)
File "/app/.venv/lib/python3.12/site-packages/solara/server/websocket.py", line 26, in send
self.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 180, in send_text
self.portal.call(self._send_text_exc, data)
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 290, in call
return cast(T_Retval, self.start_task_soon(func, *args).result())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 221, in _call_func
retval = await retval_or_awaitable
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 146, in _send_text_exc
await self.ws.send_text(data)
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 165, in send_text
await self.send({"type": "websocket.send", "text": data})
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 88, in send
raise WebSocketDisconnect(code=1006)
starlette.websockets.WebSocketDisconnect
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/app/.venv/lib/python3.12/site-packages/solara/server/kernel.py", line 229, in send_websockets
ws.close()
File "/app/.venv/lib/python3.12/site-packages/solara/server/starlette.py", line 157, in close
self.portal.call(self.ws.close)
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 290, in call
return cast(T_Retval, self.start_task_soon(func, *args).result())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/app/.venv/lib/python3.12/site-packages/anyio/from_thread.py", line 221, in _call_func
retval = await retval_or_awaitable
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 180, in close
await self.send({"type": "websocket.close", "code": code, "reason": reason or ""})
File "/app/.venv/lib/python3.12/site-packages/starlette/websockets.py", line 97, in send
raise RuntimeError('Cannot call "send" once a close message has been sent.')
RuntimeError: Cannot call "send" once a close message has been sent.
```
</details> | open | 2025-03-03T08:45:27Z | 2025-03-03T08:45:27Z | https://github.com/widgetti/solara/issues/1013 | [] | JovanVeljanoski | 0 |
amidaware/tacticalrmm | django | 962 | How to put TRMM in NPM (Nginx Proxy Manager)? | **Server Info (please complete the following information):**
- OS: Debian 10
- Browser: Chrome
- RMM Version: v0.11.3
**Installation Method:**
- [x] Standard
- [ ] Docker
**Describe**
As part of a homelab, I did a basic installation of Tactical RMM. So, I wanted to put this one in NPM (Nginx Proxy Manager) and I had a problem: when I wanted to access my machines from TRMM with remote desktop, it didn’t work (black screen).
Being a novice in the field, I don’t have too many details of why I had this blackscreen but couldn’t find any solution other than this. So, I was kindly asked to post the solution I found here.
**To Reproduce**
1. Have communication port for agents NAT TCP (default: 4222) on your firewall/router.
2. In NPM, declare the hosts with the parameters:
api.{domain}
https | TRMM server IP | 433
ON: Cache Assets | Block Common Exploits | Websockets Support
mesh.{domain}
https | TRMM server IP | 443
ON: Cache Assets | Block Common Exploits | Websockets Support
rmm.{domain}
https | TRMM server IP | 443
ON: Cache Assets | Block Common Exploits | Websockets Support
proxy.{domain}
http | NPM server IP | 81 (NPM web UI port)
Then connect in SSH to your TRMM server to modify the nginx config of Mesh:
`cd ..`
`nano meshcentral/meshcentral-data/config.json`
3. Modify in this file the "TlsOffload" field to put the local IP address of your NPM and the port that goes with it, then also modify the "CertUrl" field to put the public domain name of your NPM.
{
"settings": {
"Cert": "${meshdomain}",
"MongoDb": "mongodb://127.0.0.1:27017",
"MongoDbName": "meshcentral",
"WANonly": true,
"Minify": 1,
"Port": 4430,
"AliasPort": 443,
"RedirPort": 800,
"AllowLoginToken": true,
"AllowFraming": true,
"_AgentPing": 60,
"AgentPong": 300,
"AllowHighQualityDesktop": true,
"**TlsOffload**": "{NPM LAN IP}:81",
"agentCoreDump": false,
"Compression": true,
"WsCompression": true,
"AgentWsCompression": true,
"MaxInvalidLogin": { "time": 5, "count": 5, "coolofftime": 30 }
},
"domains": {
"": {
"Title": "Tactical RMM",
"Title2": "Tactical RMM",
"NewAccounts": false,
"**CertUrl**": "https://proxy.{domain}:443/",
"GeoLocation": true,
"CookieIpCheck": false,
"mstsc": true
}
}
}
So all you have to do is restart mesh:
`systemctl restart meshcentral.service`
Check that your Mesh service has restarted:
`systemctl status meshcentral.service`
And so your agents should work.
However, if not: a "RECOVER CONNECTION" has personally helped me get agents working that no longer wanted to come back up.
**Additional context**
It must be taken into account that I am still a student, which means that here, potentially, in terms of security, it is not the best thing to do but I remain open to advice/criticism.
Thanks you @sadnub for putting me on the track. | closed | 2022-01-31T14:31:45Z | 2022-03-16T20:53:38Z | https://github.com/amidaware/tacticalrmm/issues/962 | [
"documentation"
] | Reaapers | 1 |
dpgaspar/Flask-AppBuilder | rest-api | 1,444 | formatters_columns dictionary has no effect on ListLinkWidget/ListThumbnail/ListItem/Listblock widgets | closed | 2020-07-24T09:46:17Z | 2020-12-12T20:55:23Z | https://github.com/dpgaspar/Flask-AppBuilder/issues/1444 | [
"stale"
] | ThomasP0815 | 2 | |
mlfoundations/open_clip | computer-vision | 945 | Fine Tune for emotion | Is it possible to finetune for emotion detection? How should we label the data for that? | closed | 2024-09-27T10:22:28Z | 2024-10-03T04:10:54Z | https://github.com/mlfoundations/open_clip/issues/945 | [] | SutirthaChakraborty | 0 |
cleanlab/cleanlab | data-science | 854 | Add method to compute Out-of-bag Data Shapely value | Add this efficient method for Data Shapely and Data Valuation w arbitrary ML models:
[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://arxiv.org/abs/2304.07718)
Make sure the code runs quickly, but is numerically reproducible/stable too | open | 2023-09-30T04:42:39Z | 2023-11-23T18:43:46Z | https://github.com/cleanlab/cleanlab/issues/854 | [
"enhancement",
"good first issue",
"help-wanted"
] | jwmueller | 2 |
Yorko/mlcourse.ai | seaborn | 572 | typo in topic3_decision_trees_kNN article's image | Hello.
Typo in image [topic3_hse_instruction.png](https://github.com/Yorko/mlcourse.ai/blob/master/img/topic3_hse_instruction.png)
Arvix -> arXiv | closed | 2019-02-17T18:39:46Z | 2019-02-17T19:41:21Z | https://github.com/Yorko/mlcourse.ai/issues/572 | [] | klensy | 1 |
jina-ai/serve | deep-learning | 5,605 | [docs] request more examples | **Describe your proposal/problem**
I see that [the previous documentation](https://github.com/jina-ai/docs/blob/master/chapters/my_first_jina_app.md) and [examples](https://github.com/jina-ai/examples) have been archived, now [the examples](https://docs.jina.ai/get-started/create-app/) in the documentation are relatively simple, I hope to provide more examples and a more detailed process, such as text2img, text2video
| closed | 2023-01-17T09:56:44Z | 2023-05-02T00:17:35Z | https://github.com/jina-ai/serve/issues/5605 | [
"Stale"
] | songhn233 | 1 |
explosion/spaCy | deep-learning | 13,606 | Cannot complete a Docker image build with spaCy 3.7.6 | <!-- NOTE: For questions or install related issues, please open a Discussion instead. -->
## 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. -->
We use spaCy in our project, and as part of our regular work process, we build Docker images. As of the new spaCy version 3.7.6, we are no longer able to complete our Docker image builds, as the process hangs up here (from our workflow logs):
```
#31 96.78 Getting requirements to build wheel: started
#31 249.2 Getting requirements to build wheel: still running...
```
I found this recent `cibuildwheel` PR in the repo here, and I'm wondering if it might be related: https://github.com/explosion/spaCy/pull/13603
It looks like a handful of Checks for this PR failed: https://github.com/explosion/spaCy/pull/li13603/checks
This includes the automated tests for Ubuntu latest with Python 3.12.4, which is our environment as well. In fact, these sepcific tests seemed have failed (or were manually canceled?) at the same point we experience this issue:
https://github.com/explosion/spaCy/actions/runs/10180926505/job/28159915584?pr=13603
Perhaps more of an infrastructure issue than a code bug per se, but I'm hoping someone could advise us on it? Thanks!
## Your Environment
<!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
* Operating System: Ubuntu, latest (issue reported evident in our GitHub Actions Docker image build process)
* Python Version Used: 3.12.4
* spaCy Version Used: 3.7.6
* Environment Information:
| closed | 2024-08-23T13:29:21Z | 2024-11-01T00:03:23Z | https://github.com/explosion/spaCy/issues/13606 | [] | erikspears | 10 |
python-gino/gino | sqlalchemy | 675 | Test is failing on SQLAlchemy 1.3.17 | * GINO version: 1.0, 1.1.x
* Python version: *
* asyncpg version: *
* aiocontextvars version: *
* PostgreSQL version: *
### Description
https://github.com/python-gino/gino/runs/695012657
maybe a bug? | closed | 2020-05-21T03:55:10Z | 2020-05-21T05:44:53Z | https://github.com/python-gino/gino/issues/675 | [
"bug"
] | fantix | 1 |
adamerose/PandasGUI | pandas | 68 | Indexing error? | Hey! Huge fan of your package. Tried it out the other day and it was great.
I am getting this error when I try to do show(raw_data). Any ideas?
`---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-31-7f1299f1231e> in <module>
1 #explore with pandasgui
----> 2 show(raw_data)
~\Programs\anaconda3\lib\site-packages\pandasgui\gui.py in show(settings, *args, **kwargs)
262 kwargs = {**kwargs, **dataframes}
263
--> 264 pandas_gui = PandasGui(settings=settings, **kwargs)
265 return pandas_gui
266
~\Programs\anaconda3\lib\site-packages\pandasgui\gui.py in __init__(self, settings, **kwargs)
56 # Adds DataFrames listed in kwargs to data store.
57 for df_name, df in kwargs.items():
---> 58 self.store.add_dataframe(df, df_name)
59
60 # Default to first item
~\Programs\anaconda3\lib\site-packages\pandasgui\store.py in add_dataframe(self, pgdf, name)
243 if pgdf.dataframe_explorer is None:
244 from pandasgui.widgets.dataframe_explorer import DataFrameExplorer
--> 245 pgdf.dataframe_explorer = DataFrameExplorer(pgdf)
246 dfe = pgdf.dataframe_explorer
247 self.gui.stacked_widget.addWidget(dfe)
~\Programs\anaconda3\lib\site-packages\pandasgui\widgets\dataframe_explorer.py in __init__(self, pgdf)
38
39 # Statistics tab
---> 40 self.statistics_tab = self.make_statistics_tab(pgdf)
41 self.statistics_dock = self.add_view(self.statistics_tab, "Statistics")
42
~\Programs\anaconda3\lib\site-packages\pandasgui\widgets\dataframe_explorer.py in make_statistics_tab(self, pgdf)
81 def make_statistics_tab(self, pgdf: PandasGuiDataFrame):
82
---> 83 stats_df = pd.DataFrame(
84 {
85 "Type": pgdf.dataframe.dtypes.replace("object", "string").astype(str),
~\Programs\anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
433 )
434 elif isinstance(data, dict):
--> 435 mgr = init_dict(data, index, columns, dtype=dtype)
436 elif isinstance(data, ma.MaskedArray):
437 import numpy.ma.mrecords as mrecords
~\Programs\anaconda3\lib\site-packages\pandas\core\internals\construction.py in init_dict(data, index, columns, dtype)
252 arr if not is_datetime64tz_dtype(arr) else arr.copy() for arr in arrays
253 ]
--> 254 return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
255
256
~\Programs\anaconda3\lib\site-packages\pandas\core\internals\construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype)
67
68 # don't force copy because getting jammed in an ndarray anyway
---> 69 arrays = _homogenize(arrays, index, dtype)
70
71 # from BlockManager perspective
~\Programs\anaconda3\lib\site-packages\pandas\core\internals\construction.py in _homogenize(data, index, dtype)
309 # Forces alignment. No need to copy data since we
310 # are putting it into an ndarray later
--> 311 val = val.reindex(index, copy=False)
312 else:
313 if isinstance(val, dict):
~\Programs\anaconda3\lib\site-packages\pandas\core\series.py in reindex(self, index, **kwargs)
4028 @Appender(generic.NDFrame.reindex.__doc__)
4029 def reindex(self, index=None, **kwargs):
-> 4030 return super().reindex(index=index, **kwargs)
4031
4032 def drop(
~\Programs\anaconda3\lib\site-packages\pandas\core\generic.py in reindex(self, *args, **kwargs)
4541
4542 # perform the reindex on the axes
-> 4543 return self._reindex_axes(
4544 axes, level, limit, tolerance, method, fill_value, copy
4545 ).__finalize__(self)
~\Programs\anaconda3\lib\site-packages\pandas\core\generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4561
4562 axis = self._get_axis_number(a)
-> 4563 obj = obj._reindex_with_indexers(
4564 {axis: [new_index, indexer]},
4565 fill_value=fill_value,
~\Programs\anaconda3\lib\site-packages\pandas\core\generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
4605
4606 # TODO: speed up on homogeneous DataFrame objects
-> 4607 new_data = new_data.reindex_indexer(
4608 index,
4609 indexer,
~\Programs\anaconda3\lib\site-packages\pandas\core\internals\managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy)
1249 # some axes don't allow reindexing with dups
1250 if not allow_dups:
-> 1251 self.axes[axis]._can_reindex(indexer)
1252
1253 if axis >= self.ndim:
~\Programs\anaconda3\lib\site-packages\pandas\core\indexes\base.py in _can_reindex(self, indexer)
3097 # trying to reindex on an axis with duplicates
3098 if not self.is_unique and len(indexer):
-> 3099 raise ValueError("cannot reindex from a duplicate axis")
3100
3101 def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
ValueError: cannot reindex from a duplicate axis` | closed | 2020-11-25T10:14:49Z | 2020-11-25T10:19:35Z | https://github.com/adamerose/PandasGUI/issues/68 | [] | lucashadin | 1 |
modin-project/modin | data-science | 6,553 | BUG: read_csv with `iterator=True` throws AttributeError | ### Modin version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest released version of Modin.
- [ ] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)
### Reproducible Example
```python
import modin.pandas as pd
df_iter = pd.read_csv('path/to/file.csv', iterator=True)
```
### Issue Description
`AttributeError: 'TextFileReader' object has no attribute 'columns'`
### Expected Behavior
returns TextFileReader.
### Error Logs
<details>
```python-traceback
...
venv/lib/python3.10/site-packages/modin/core/io/text/text_file_dispatcher.py", line 1062, in _read
column_names = pd_df_metadata.columns
AttributeError: 'TextFileReader' object has no attribute 'columns'
```
</details>
### Installed Versions
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : b5545c686751f4eed6913f1785f9c68f41f4e51d
python : 3.10.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.16.3-microsoft-standard-WSL2
Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
Modin dependencies
------------------
modin : 0.23.1
ray : 2.6.3
dask : None
distributed : None
hdk : None
pandas dependencies
-------------------
pandas : 2.0.3
numpy : 1.25.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 59.6.0
pip : 23.2.1
Cython : None
pytest : 7.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2023.9.0
gcsfs : None
matplotlib : 3.7.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.2
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
>>>
</details>
| closed | 2023-09-12T14:51:00Z | 2023-09-13T18:12:25Z | https://github.com/modin-project/modin/issues/6553 | [
"bug 🦗",
"P1"
] | SiRumCz | 1 |
deezer/spleeter | tensorflow | 550 | Warning! ***HDF5 library version mismatched error*** | I installed the softwear but when i try to run it gives me this waring "Warning! ***HDF5 library version mismatched error***". I checked and found that i needed to update the h5py to 1.10.6, but when i did that the h5py install gave me this error.
`PackagesNotFoundError: The following packages are not available from current channels:
- h5py=1.10.6` | closed | 2021-01-06T22:27:46Z | 2021-01-08T12:10:49Z | https://github.com/deezer/spleeter/issues/550 | [
"bug",
"invalid"
] | bfp4 | 1 |
cs230-stanford/cs230-code-examples | computer-vision | 16 | Problem in Build the dataset of size 64x64 | Hello,
After I do the first step (Build the dataset of size 64x64), there is file 64x64_SIGNS file created, but there is no image in the train_signs. How to solve it? | closed | 2019-02-14T10:13:53Z | 2019-02-15T01:31:46Z | https://github.com/cs230-stanford/cs230-code-examples/issues/16 | [] | ChunJyeBehBeh | 0 |
proplot-dev/proplot | matplotlib | 104 | Add TeX Gyre fonts as the default serif, sans-serif, and monospace fonts | Currently, ProPlot makes some more widely-used (IMHO nicer) fonts the default rather than the DejaVu fonts. With the DejaVu fonts pushed to the back, the `rcParams` font family priorities are:
```yaml
# ProPlot changes
font.serif: New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, Computer Modern Roman, DejaVu Serif, Bitstream Vera Serif, serif
font.sans-serif: Helvetica, Arial, Lucida Grande, Verdana, Geneva, Lucid, Avant Garde, TeX Gyre Heros, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, sans-serif
font.monospace: Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, Computer Modern Typewriter, DejaVu Sans Mono, Bitstream Vera Sans Mono, monospace
# Unchanged from matplotlib
font.cursive: Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive
font.fantasy: Comic Sans MS, Chicago, Charcoal, ImpactWestern, Humor Sans, xkcd, fantasy
```
Unfortunately, this means the user experience will be inconsistent across systems, and I can't always rule out that a thin font will be picked up by matplotlib and used in place of the "regular" version, or that matplotlib won't incorrectly read some other font weight (#94).
While I've added a Helvetica lookalike called [TeX Gyre Heros](https://www.fontsquirrel.com/fonts/tex-gyre-heros), I had to put it near the back because I didn't like that `'serif'` would be the only font category guaranteed to be the same on all systems.
However, TeX Gyre is a [whole font *series*](http://www.gust.org.pl/projects/e-foundry/tex-gy). It includes look-alikes for the Century fonts, Times new Roman, and the monospace Nimbus font.
So... I think it would be very straightforward to add the whole TeX Gyre series to ProPlot and put them at the front of the `font.serif`, `font.sans-serif`, and `font.monospace` lists! I will also convert the files from OTF to TTF before packaging, so PS files can be saved and PDFs can be saved without the backend embedding the entire font specification in the document (see matplotlib/matplotlib#1668). | closed | 2020-01-09T05:54:04Z | 2020-01-13T20:42:02Z | https://github.com/proplot-dev/proplot/issues/104 | [
"enhancement"
] | lukelbd | 1 |
gradio-app/gradio | python | 10,731 | The reply text rendered by the streaming chatbot page is incomplete | ### Describe the bug
The reply text rendered by the streaming chatbot page is incomplete and missing from the content of response_message in yield response_message, state
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
yield response_message, state
```
### Screenshot
_No response_
### Logs
```shell
```
### System Info
```shell
Name: gradio
Version: 5.20.0
```
### Severity
Blocking usage of gradio | closed | 2025-03-05T07:11:51Z | 2025-03-07T14:27:22Z | https://github.com/gradio-app/gradio/issues/10731 | [
"bug",
"needs repro"
] | wuxianyess | 8 |
gradio-app/gradio | machine-learning | 10,863 | Support for a favicon | - [ X] I have searched to see if a similar issue already exists.
**Is your feature request related to a problem? Please describe.**
not a problem, just an improvement.
**Describe the solution you'd like**
eg ```demo.launch(favicon="/path/to/favicon.ico')```
**Additional context**
Add any other context or screenshots about the feature request here.
| closed | 2025-03-22T16:42:06Z | 2025-03-23T21:31:12Z | https://github.com/gradio-app/gradio/issues/10863 | [] | rbpasker | 1 |
ultralytics/ultralytics | computer-vision | 18,958 | ablation experiments | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
Hello author, I would like to ask you a question about ablation experiments. When designing an ablation experiment, if module A and module B are added to the model, do we need to ensure that the original model, original model + A, and original model + B have the same experimental parameters? If the parameters must be consistent, wouldn't the innovation work be a simple superposition?
### Additional
_No response_ | open | 2025-01-31T10:36:08Z | 2025-02-04T00:03:15Z | https://github.com/ultralytics/ultralytics/issues/18958 | [
"question"
] | Ellohiye | 11 |
shashankvemuri/Finance | pandas | 13 | On trading view | Hi Shashank,
I can not see which files/packages need to install before using fiance project?
Can you pelase let me know that step by step ?
| closed | 2021-07-23T18:32:29Z | 2023-04-29T18:16:30Z | https://github.com/shashankvemuri/Finance/issues/13 | [] | Amolkulkarni2 | 1 |
PaddlePaddle/models | computer-vision | 4,738 | BMN模型batch_size调小之后loss为nan | 在我把BMN的batch_size由16调成8时，训练loss会出现nan,继续调为2之后，启动训练会报错。下面为使用batch_size为8时的结果。使用的训练集是官方的示例
| open | 2020-07-03T08:20:02Z | 2020-07-06T10:36:00Z | https://github.com/PaddlePaddle/models/issues/4738 | [] | liu824 | 5 |
ultralytics/ultralytics | python | 18,817 | yolo11 RKNN autobackend bug | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### Ultralytics YOLO Component
Integrations
### Bug
i am using rock 5b+ with python3.11 env, and i want to deploy yolov11 model on RK3588 follow this [doc](https://docs.ultralytics.com/integrations/rockchip-rknn/#installation_1)
on my X86 PC, i used ultralytics to export rknn model without any problem
```bash
$ yolo export model=yolo11n.pt format=rknn name=rk3588
Ultralytics 8.3.65 🚀 Python-3.8.2 torch-2.4.0+cu121 CPU (Intel Core(TM) i9-14900KF)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs
PyTorch: starting from 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (5.4 MB)
RKNN: starting export with rknn-toolkit2...
ONNX: starting export with onnx 1.17.0 opset 19...
ONNX: slimming with onnxslim 0.1.47...
ONNX: export success ✅ 0.5s, saved as 'yolo11n.onnx' (10.2 MB)
I rknn-toolkit2 version: 2.3.0
I Loading : 100%|█████████████████████████████████████████████| 196/196 [00:00<00:00, 110897.56it/s]
I OpFusing 0: 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 1916.51it/s]
I OpFusing 1 : 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 704.39it/s]
I OpFusing 0 : 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 290.85it/s]
I OpFusing 1 : 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 264.11it/s]
I OpFusing 0 : 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 251.18it/s]
I OpFusing 1 : 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 242.71it/s]
I OpFusing 2 : 100%|█████████████████████████████████████████████| 100/100 [00:00<00:00, 163.19it/s]
I rknn building ...
I rknn building done.
```
after i copy the yolo11n-rk3588.rknn to the rock5b+, install the ultralytics==8.3.65, try to inference with
```bash
(.venv) rock@rock-5b-plus:~/yolov11$ yolo predict model='./yolo11n-rk3588.rknn' source='https://ultralytics.com/images/bus.jpg'
Creating new Ultralytics Settings v0.0.6 file ✅
View Ultralytics Settings with 'yolo settings' or at '/home/rock/.config/Ultralytics/settings.json'
Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.
WARNING ⚠️ Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'.
Ultralytics 8.3.65 🚀 Python-3.11.2 torch-2.5.1 CPU (-)
Traceback (most recent call last):
File "/home/rock/yolov11/.venv/bin/yolo", line 8, in <module>
sys.exit(entrypoint())
^^^^^^^^^^^^
File "/home/rock/yolov11/.venv/lib/python3.11/site-packages/ultralytics/cfg/__init__.py", line 991, in entrypoint
getattr(model, mode)(**overrides) # default args from model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/rock/yolov11/.venv/lib/python3.11/site-packages/ultralytics/engine/model.py", line 551, in predict
self.predictor.setup_model(model=self.model, verbose=is_cli)
File "/home/rock/yolov11/.venv/lib/python3.11/site-packages/ultralytics/engine/predictor.py", line 310, in setup_model
self.model = AutoBackend(
^^^^^^^^^^^^
File "/home/rock/yolov11/.venv/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/rock/yolov11/.venv/lib/python3.11/site-packages/ultralytics/nn/autobackend.py", line 492, in __init__
raise TypeError(
TypeError: model='yolo11n-rk3588.rknn' is not a supported model format. Ultralytics supports: ('PyTorch', 'TorchScript', 'ONNX', 'OpenVINO', 'TensorRT', 'CoreML', 'TensorFlow SavedModel', 'TensorFlow GraphDef', 'TensorFlow Lite', 'TensorFlow Edge TPU', 'TensorFlow.js', 'PaddlePaddle', 'MNN', 'NCNN', 'IMX', 'RKNN')
See https://docs.ultralytics.com/modes/predict for help.
```
this look like it lod the rknn model success but inference failed due with format problem.
### Environment
PC:
```bash
OS: ubuntu22.04
python3: 3.8.2
ultralytics: 8.3.65
```
Rock5b+ RK3588:
```bash
OS: Linux rock-5b-plus 6.1.84-1-rk2410
python: 3.11.2
ultralytics: 8.3.65
```
### Minimal Reproducible Example
PC:
```bash
yolo export model=yolo11n.pt format=rknn name=rk3588
```
Rock5b+:
```bash
yolo predict model='./yolo11n-rk3588.rknn' source='https://ultralytics.com/images/bus.jpg'
```
### Additional
_No response_
### Are you willing to submit a PR?
- [x] Yes I'd like to help by submitting a PR! | closed | 2025-01-22T08:41:26Z | 2025-01-22T09:32:44Z | https://github.com/ultralytics/ultralytics/issues/18817 | [
"embedded",
"exports"
] | ZIFENG278 | 4 |
twopirllc/pandas-ta | pandas | 783 | Discripency Between Yahoo Finance and Panda's TA in Accumulation/Distribution Indicator | **Which version are you running? The lastest version is on Github. Pip is for major releases.**
```python
import pandas_ta as ta
print(ta.version)
```
0.3.14b0
**Do you have _TA Lib_ also installed in your environment?**
```sh
$ pip list
```
pandas-ta 0.3.14b0
**Have you tried the _development_ version? Did it resolve the issue?**
```sh
$ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development
```
No
**Describe the bug**
A clear and concise description of what the bug is.
I am trying to calculate Accumulation/Distribution Indicator using Pandas TA
I have compared the output with Yahoo Finance and it seems that the numbers are mismatching
Below is a sample for MSFT Day Interval from both Panda's TA and Yahoo Finance
<img width="1021" alt="image" src="https://github.com/twopirllc/pandas-ta/assets/71660135/7cf59b41-9956-42fa-88e4-98fc9b4aacea">
While the graphs are relevant the numbers in Yahoo Finance are in terms of Billions While from Panda's TA are in terms of Millions
**To Reproduce**
Provide sample code.
```python
aggs = []
for a in polygon_client.list_aggs(ticker="MSFT", multiplier=1, timespan="day", from_="2023-12-04", to="2024-04-05", limit=50000):
aggs.append(a)
stock=pd.DataFrame(aggs)
stock.ta.ad(high=stock['high'], low=stock['low'], close=stock['close'],volume=stock['volume'], open_=stock['Open'])
```
**Expected behavior**
A clear and concise description of what you expected to happen.
Below is pandas ta output
```sh
0 1.937360e+05
1 1.893756e+07
2 2.617762e+06
3 1.486887e+07
4 3.113148e+07
...
80 1.862813e+08
81 1.915287e+08
82 1.943746e+08
83 1.819667e+08
84 1.819712e+08
Name: ADo, Length: 85, dtype: float64
```
last item is 181 Million while yahoo Finance shows 1.62 Billion
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Additional context**
Add any other context about the problem here.
Thanks for using Pandas TA!
| closed | 2024-04-05T10:28:20Z | 2024-04-15T19:55:46Z | https://github.com/twopirllc/pandas-ta/issues/783 | [
"wontfix",
"info"
] | ibrahimmina | 1 |
K3D-tools/K3D-jupyter | jupyter | 240 | Assigning textures when getting vtk_polydata from glb/ obj+mtl | Hi I am trying to get a workflow to display ifc files in k3d with the correct textures in place. I have converted all my geometry to glb and obj+mtl to try to do this, while I am getting the geometry to display correctly, I am missing the textures.
I had opened these two issues on stack overflow, regarding this workflow;
https://stackoverflow.com/questions/63130386/rendering-a-gltf-glb-file-in-jupyter-notebook-using-vtk-and-k3d
https://stackoverflow.com/questions/63132207/vtkobjreader-object-has-no-attribute-setfilenamemtl-mtl-for-object-obj-fil
I have managed to assign an arbitrary texture, from a bitmap file. However I am struggling to find a workflow that allows textures to be mapped directly from an imported file. Currently k3d.vtk_poly_data() sets texture to None.
How can one map textures from a glb or a obj + mtl when piping polydata using k3d.vtk_poly_data() ?
Relevant line in code;
https://github.com/K3D-tools/K3D-jupyter/blob/ffb096fc06587511aa7a281e3c6a3550d48682dc/k3d/factory.py#L937
Thanks for any information you can provide on this issue. | closed | 2020-08-12T09:13:59Z | 2020-11-16T11:11:26Z | https://github.com/K3D-tools/K3D-jupyter/issues/240 | [] | BarusXXX | 1 |
falconry/falcon | api | 1,671 | [pylint E1101] [E] Module 'falcon' has no 'HTTP_200' member (no-member) | pylint is not recognizing falcon status codes. e.g HTTP_201
```python
import falcon
class ObjectRequestClass:
def on_get(self, req, resp):
resp.status = falcon.HTTP_201
``` | closed | 2020-02-15T16:46:15Z | 2020-02-16T14:13:15Z | https://github.com/falconry/falcon/issues/1671 | [
"duplicate"
] | zubairakram | 2 |
scanapi/scanapi | rest-api | 339 | Improve tests readability with pytest-it | ## Improve tests readability with pytest-it
https://pypi.org/project/pytest-it/ | closed | 2020-12-10T11:41:16Z | 2021-04-20T13:10:47Z | https://github.com/scanapi/scanapi/issues/339 | [
"Refactor"
] | camilamaia | 1 |
junyanz/pytorch-CycleGAN-and-pix2pix | pytorch | 1,522 | Visdom style.css error | **Hey Junyanz, thanks for your great work. I have the following issue:**
python -m visdom.server
Checking for scripts.
It's Alive!
ERROR:root:initializing
INFO:root:Application Started
INFO:root:Working directory: C:\Users\olive\.visdom
You can navigate to http://localhost:8097
INFO:tornado.access:304 GET / (::1) 30.08ms
INFO:tornado.access:304 GET /user/style.css?v=311c1a25a99ac897d22df72ea024df452eee92fdc1453817050a243b62106e375c791d4f9b831ec371f6f342f84fd41d05bcc29d7835ec2bb78a150ba90282d1 (::1) 1.00ms
INFO:tornado.access:304 GET /static/fonts/glyphicons-halflings-regular.woff2 (::1) 284.73ms
INFO:tornado.access:304 GET / (::1) 72.16ms
INFO:tornado.access:304 GET /user/style.css?v=311c1a25a99ac897d22df72ea024df452eee92fdc1453817050a243b62106e375c791d4f9b831ec371f6f342f84fd41d05bcc29d7835ec2bb78a150ba90282d1 (::1) 1.02ms
INFO:tornado.access:101 GET /socket (::1) 0.00ms
INFO:root:Opened new socket from ip: ::1
INFO:tornado.access:200 POST /env/main (::1) 1.00ms
INFO:tornado.access:200 POST /env/main (::1) 0.00ms
INFO:tornado.access:304 GET /favicon.png (::1) 14.04ms
---------------------------------------------
**I have gone to the file directory where visdom is saved in the environment and manually created a user\style.css file. I am not sure what code to include in the style.css. I couldn't fix the problem (I have also deleted and reinstalled visdom. It doesn't give me the user\style.css files.** | open | 2022-12-24T21:21:26Z | 2023-07-26T03:11:22Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1522 | [] | MayuO2 | 1 |
deepfakes/faceswap | machine-learning | 870 | ValueError: not enough values to unpack (expected 2, got 0) | **Describe the bug**
When trying to run a train with Original, I get "not enough values to unpack" error/crash
This is on the Windows GUI build
Crash report:
```09/14/2019 21:16:09 MainProcess training_0 multithreading start DEBUG Starting thread 2 of 2: '_run_1'
09/14/2019 21:16:09 MainProcess _run_1 training_data minibatch DEBUG Loading minibatch generator: (image_count: 644, side: 'b', is_display: False, do_shuffle: True)
09/14/2019 21:16:09 MainProcess training_0 multithreading start DEBUG Started all threads '_run': 2
09/14/2019 21:16:09 MainProcess training_0 _base set_tensorboard DEBUG Enabling TensorBoard Logging
09/14/2019 21:16:09 MainProcess training_0 _base set_tensorboard DEBUG Setting up TensorBoard Logging. Side: a
09/14/2019 21:16:09 MainProcess training_0 _base name DEBUG model name: 'original'
09/14/2019 21:16:09 MainProcess training_0 _base tensorboard_kwargs DEBUG Tensorflow version: [1, 14, 0]
09/14/2019 21:16:09 MainProcess training_0 _base tensorboard_kwargs DEBUG {'histogram_freq': 0, 'batch_size': 64, 'write_graph': True, 'write_grads': True, 'update_freq': 'batch', 'profile_batch': 0}
09/14/2019 21:16:11 MainProcess training_0 _base set_tensorboard DEBUG Setting up TensorBoard Logging. Side: b
09/14/2019 21:16:11 MainProcess training_0 _base name DEBUG model name: 'original'
09/14/2019 21:16:11 MainProcess training_0 _base tensorboard_kwargs DEBUG Tensorflow version: [1, 14, 0]
09/14/2019 21:16:11 MainProcess training_0 _base tensorboard_kwargs DEBUG {'histogram_freq': 0, 'batch_size': 64, 'write_graph': True, 'write_grads': True, 'update_freq': 'batch', 'profile_batch': 0}
09/14/2019 21:16:12 MainProcess training_0 _base set_tensorboard INFO Enabled TensorBoard Logging
09/14/2019 21:16:12 MainProcess training_0 _base use_mask DEBUG False
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initializing Samples: model: '<plugins.train.model.original.Model object at 0x000002B964163C88>', use_mask: False, coverage_ratio: 0.6875)
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initialized Samples
09/14/2019 21:16:12 MainProcess training_0 _base use_mask DEBUG False
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initializing Timelapse: model: <plugins.train.model.original.Model object at 0x000002B964163C88>, use_mask: False, coverage_ratio: 0.6875, preview_images: 14, batchers: '{'a': <plugins.train.trainer._base.Batcher object at 0x000002B921FA2AC8>, 'b': <plugins.train.trainer._base.Batcher object at 0x000002B9222574A8>}')
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initializing Samples: model: '<plugins.train.model.original.Model object at 0x000002B964163C88>', use_mask: False, coverage_ratio: 0.6875)
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initialized Samples
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initialized Timelapse
09/14/2019 21:16:12 MainProcess training_0 _base __init__ DEBUG Initialized Trainer
09/14/2019 21:16:12 MainProcess training_0 train load_trainer DEBUG Loaded Trainer
09/14/2019 21:16:12 MainProcess training_0 train run_training_cycle DEBUG Running Training Cycle
09/14/2019 21:16:13 MainProcess training_0 _base generate_preview DEBUG Generating preview
09/14/2019 21:16:13 MainProcess training_0 _base set_preview_feed DEBUG Setting preview feed: (side: 'a')
09/14/2019 21:16:13 MainProcess training_0 _base load_generator DEBUG Loading generator: a
09/14/2019 21:16:13 MainProcess training_0 _base load_generator DEBUG input_size: 64, output_shapes: [(64, 64, 3)]
09/14/2019 21:16:13 MainProcess training_0 training_data __init__ DEBUG Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], training_opts: {'alignments': {'a': 'C:\\Users\\Administrator\\Documents\\fs\\fs\\craigex\\craig_alignments.json', 'b': 'C:\\Users\\Administrator\\Documents\\fs\\fs\\robex\\rob_alignments.json'}, 'preview_scaling': 0.5, 'warp_to_landmarks': False, 'augment_color': True, 'no_flip': False, 'pingpong': False, 'snapshot_interval': 25000, 'training_size': 256, 'no_logs': False, 'mask_type': None, 'coverage_ratio': 0.6875}, landmarks: False, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur': False, 'icnr_init': False, 'conv_aware_init': False, 'subpixel_upscaling': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
09/14/2019 21:16:13 MainProcess training_0 training_data set_mask_class DEBUG Mask class: None
09/14/2019 21:16:13 MainProcess training_0 training_data __init__ DEBUG Initializing ImageManipulation: (input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.6875, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur': False, 'icnr_init': False, 'conv_aware_init': False, 'subpixel_upscaling': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
09/14/2019 21:16:13 MainProcess training_0 training_data __init__ DEBUG Output sizes: [64]
09/14/2019 21:16:13 MainProcess training_0 training_data __init__ DEBUG Initialized ImageManipulation
09/14/2019 21:16:13 MainProcess training_0 training_data __init__ DEBUG Initialized TrainingDataGenerator
09/14/2019 21:16:13 MainProcess training_0 training_data minibatch_ab DEBUG Queue batches: (image_count: 586, batchsize: 14, side: 'a', do_shuffle: True, is_preview, True, is_timelapse: False)
09/14/2019 21:16:13 MainProcess training_0 multithreading __init__ DEBUG Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
09/14/2019 21:16:13 MainProcess training_0 multithreading __init__ DEBUG Initialized BackgroundGenerator: '_run'
09/14/2019 21:16:13 MainProcess training_0 multithreading start DEBUG Starting thread(s): '_run'
09/14/2019 21:16:13 MainProcess training_0 multithreading start DEBUG Starting thread 1 of 2: '_run_0'
09/14/2019 21:16:13 MainProcess _run_0 training_data minibatch DEBUG Loading minibatch generator: (image_count: 586, side: 'a', is_display: True, do_shuffle: True)
09/14/2019 21:16:13 MainProcess training_0 multithreading start DEBUG Starting thread 2 of 2: '_run_1'
09/14/2019 21:16:13 MainProcess _run_1 training_data minibatch DEBUG Loading minibatch generator: (image_count: 586, side: 'a', is_display: True, do_shuffle: True)
09/14/2019 21:16:13 MainProcess training_0 multithreading start DEBUG Started all threads '_run': 2
09/14/2019 21:16:13 MainProcess training_0 _base set_preview_feed DEBUG Set preview feed. Batchsize: 14
09/14/2019 21:16:14 MainProcess training_0 _base largest_face_index DEBUG 0
09/14/2019 21:16:24 MainProcess training_0 _base compile_sample DEBUG Compiling samples: (side: 'a', samples: 14)
09/14/2019 21:16:24 MainProcess training_0 _base get_sample DEBUG Getting timelapse samples: 'a'
09/14/2019 21:16:24 MainProcess training_0 _base setup DEBUG Setting up timelapse
09/14/2019 21:16:24 MainProcess training_0 _base setup DEBUG Timelapse output set to 'C:\Users\Administrator\Documents\fs\fs\tl'
09/14/2019 21:16:24 MainProcess training_0 utils get_image_paths DEBUG Scanned Folder contains 0 files
09/14/2019 21:16:24 MainProcess training_0 utils get_image_paths DEBUG Returning 0 images
09/14/2019 21:16:24 MainProcess training_0 utils get_image_paths DEBUG Scanned Folder contains 0 files
09/14/2019 21:16:24 MainProcess training_0 utils get_image_paths DEBUG Returning 0 images
09/14/2019 21:16:24 MainProcess training_0 _base set_timelapse_feed DEBUG Setting timelapse feed: (side: 'a', input_images: '[]', batchsize: 0)
09/14/2019 21:16:24 MainProcess training_0 _base load_generator DEBUG Loading generator: a
09/14/2019 21:16:24 MainProcess training_0 _base load_generator DEBUG input_size: 64, output_shapes: [(64, 64, 3)]
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], training_opts: {'alignments': {'a': 'C:\\Users\\Administrator\\Documents\\fs\\fs\\craigex\\craig_alignments.json', 'b': 'C:\\Users\\Administrator\\Documents\\fs\\fs\\robex\\rob_alignments.json'}, 'preview_scaling': 0.5, 'warp_to_landmarks': False, 'augment_color': True, 'no_flip': False, 'pingpong': False, 'snapshot_interval': 25000, 'training_size': 256, 'no_logs': False, 'mask_type': None, 'coverage_ratio': 0.6875}, landmarks: False, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur': False, 'icnr_init': False, 'conv_aware_init': False, 'subpixel_upscaling': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
09/14/2019 21:16:24 MainProcess training_0 training_data set_mask_class DEBUG Mask class: None
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initializing ImageManipulation: (input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.6875, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur': False, 'icnr_init': False, 'conv_aware_init': False, 'subpixel_upscaling': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Output sizes: [64]
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initialized ImageManipulation
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initialized TrainingDataGenerator
09/14/2019 21:16:24 MainProcess training_0 training_data minibatch_ab DEBUG Queue batches: (image_count: 0, batchsize: 0, side: 'a', do_shuffle: False, is_preview, False, is_timelapse: True)
09/14/2019 21:16:24 MainProcess training_0 multithreading __init__ DEBUG Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
09/14/2019 21:16:24 MainProcess training_0 multithreading __init__ DEBUG Initialized BackgroundGenerator: '_run'
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Starting thread(s): '_run'
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Starting thread 1 of 2: '_run_0'
09/14/2019 21:16:24 MainProcess _run_0 training_data minibatch DEBUG Loading minibatch generator: (image_count: 0, side: 'a', is_display: True, do_shuffle: False)
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Starting thread 2 of 2: '_run_1'
09/14/2019 21:16:24 MainProcess _run_1 training_data minibatch DEBUG Loading minibatch generator: (image_count: 0, side: 'a', is_display: True, do_shuffle: False)
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Started all threads '_run': 2
09/14/2019 21:16:24 MainProcess training_0 _base set_timelapse_feed DEBUG Set timelapse feed
09/14/2019 21:16:24 MainProcess training_0 _base set_timelapse_feed DEBUG Setting timelapse feed: (side: 'b', input_images: '[]', batchsize: 0)
09/14/2019 21:16:24 MainProcess training_0 _base load_generator DEBUG Loading generator: b
09/14/2019 21:16:24 MainProcess training_0 _base load_generator DEBUG input_size: 64, output_shapes: [(64, 64, 3)]
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initializing TrainingDataGenerator: (model_input_size: 64, model_output_shapes: [(64, 64, 3)], training_opts: {'alignments': {'a': 'C:\\Users\\Administrator\\Documents\\fs\\fs\\craigex\\craig_alignments.json', 'b': 'C:\\Users\\Administrator\\Documents\\fs\\fs\\robex\\rob_alignments.json'}, 'preview_scaling': 0.5, 'warp_to_landmarks': False, 'augment_color': True, 'no_flip': False, 'pingpong': False, 'snapshot_interval': 25000, 'training_size': 256, 'no_logs': False, 'mask_type': None, 'coverage_ratio': 0.6875}, landmarks: False, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur': False, 'icnr_init': False, 'conv_aware_init': False, 'subpixel_upscaling': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
09/14/2019 21:16:24 MainProcess training_0 training_data set_mask_class DEBUG Mask class: None
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initializing ImageManipulation: (input_size: 64, output_shapes: [(64, 64, 3)], coverage_ratio: 0.6875, config: {'coverage': 68.75, 'mask_type': None, 'mask_blur': False, 'icnr_init': False, 'conv_aware_init': False, 'subpixel_upscaling': False, 'reflect_padding': False, 'penalized_mask_loss': True, 'loss_function': 'mae', 'learning_rate': 5e-05, 'preview_images': 14, 'zoom_amount': 5, 'rotation_range': 10, 'shift_range': 5, 'flip_chance': 50, 'color_lightness': 30, 'color_ab': 8, 'color_clahe_chance': 50, 'color_clahe_max_size': 4})
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Output sizes: [64]
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initialized ImageManipulation
09/14/2019 21:16:24 MainProcess training_0 training_data __init__ DEBUG Initialized TrainingDataGenerator
09/14/2019 21:16:24 MainProcess training_0 training_data minibatch_ab DEBUG Queue batches: (image_count: 0, batchsize: 0, side: 'b', do_shuffle: False, is_preview, False, is_timelapse: True)
09/14/2019 21:16:24 MainProcess training_0 multithreading __init__ DEBUG Initializing BackgroundGenerator: (target: '_run', thread_count: 2)
09/14/2019 21:16:24 MainProcess training_0 multithreading __init__ DEBUG Initialized BackgroundGenerator: '_run'
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Starting thread(s): '_run'
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Starting thread 1 of 2: '_run_0'
09/14/2019 21:16:24 MainProcess _run_0 training_data minibatch DEBUG Loading minibatch generator: (image_count: 0, side: 'b', is_display: True, do_shuffle: False)
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Starting thread 2 of 2: '_run_1'
09/14/2019 21:16:24 MainProcess _run_1 training_data minibatch DEBUG Loading minibatch generator: (image_count: 0, side: 'b', is_display: True, do_shuffle: False)
09/14/2019 21:16:24 MainProcess training_0 multithreading start DEBUG Started all threads '_run': 2
09/14/2019 21:16:24 MainProcess training_0 _base set_timelapse_feed DEBUG Set timelapse feed
09/14/2019 21:16:24 MainProcess training_0 _base setup DEBUG Set up timelapse
09/14/2019 21:16:24 MainProcess training_0 multithreading run DEBUG Error in thread (training_0): not enough values to unpack (expected 2, got 0)
09/14/2019 21:16:25 MainProcess MainThread train monitor DEBUG Thread error detected
09/14/2019 21:16:25 MainProcess MainThread train monitor DEBUG Closed Monitor
09/14/2019 21:16:25 MainProcess MainThread train end_thread DEBUG Ending Training thread
09/14/2019 21:16:25 MainProcess MainThread train end_thread CRITICAL Error caught! Exiting...
09/14/2019 21:16:25 MainProcess MainThread multithreading join DEBUG Joining Threads: 'training'
09/14/2019 21:16:25 MainProcess MainThread multithreading join DEBUG Joining Thread: 'training_0'
09/14/2019 21:16:25 MainProcess MainThread multithreading join ERROR Caught exception in thread: 'training_0'
Traceback (most recent call last):
File "C:\Users\Administrator\faceswap\lib\cli.py", line 128, in execute_script
process.process()
File "C:\Users\Administrator\faceswap\scripts\train.py", line 98, in process
self.end_thread(thread, err)
File "C:\Users\Administrator\faceswap\scripts\train.py", line 124, in end_thread
thread.join()
File "C:\Users\Administrator\faceswap\lib\multithreading.py", line 216, in join
raise thread.err[1].with_traceback(thread.err[2])
File "C:\Users\Administrator\faceswap\lib\multithreading.py", line 147, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\Administrator\faceswap\scripts\train.py", line 149, in training
raise err
File "C:\Users\Administrator\faceswap\scripts\train.py", line 139, in training
self.run_training_cycle(model, trainer)
File "C:\Users\Administrator\faceswap\scripts\train.py", line 221, in run_training_cycle
trainer.train_one_step(viewer, timelapse)
File "C:\Users\Administrator\faceswap\plugins\train\trainer\_base.py", line 211, in train_one_step
raise err
File "C:\Users\Administrator\faceswap\plugins\train\trainer\_base.py", line 185, in train_one_step
self.timelapse.get_sample(side, timelapse_kwargs)
File "C:\Users\Administrator\faceswap\plugins\train\trainer\_base.py", line 614, in get_sample
self.samples.images[side] = self.batchers[side].compile_timelapse_sample()
File "C:\Users\Administrator\faceswap\plugins\train\trainer\_base.py", line 350, in compile_timelapse_sample
samples, feed = batch[:2]
ValueError: not enough values to unpack (expected 2, got 0)
============ System Information ============
encoding: cp1252
git_branch: master
git_commits: f8e0190 update opencv-python
gpu_cuda: 8.0
gpu_cudnn: 6.0.21
gpu_devices: GPU_0: Tesla V100-SXM2-16GB
gpu_devices_active: GPU_0
gpu_driver: 425.25
gpu_vram: GPU_0: 16258MB
os_machine: AMD64
os_platform: Windows-10-10.0.14393-SP0
os_release: 10
py_command: C:\Users\Administrator\faceswap\faceswap.py train -A C:/Users/Administrator/Documents/fs/fs/craigex -ala C:/Users/Administrator/Documents/fs/fs/craigex/craig_alignments.json -B C:/Users/Administrator/Documents/fs/fs/robex -alb C:/Users/Administrator/Documents/fs/fs/robex/rob_alignments.json -m C:/Users/Administrator/Documents/fs/fs/model -t original -bs 96 -it 1000000 -g 1 -s 100 -ss 25000 -tia C:/Users/Administrator/Documents/fs/fs/tl-a -tib C:/Users/Administrator/Documents/fs/fs/tl-b -to C:/Users/Administrator/Documents/fs/fs/tl -ps 50 -L INFO -gui
py_conda_version: conda 4.7.11
py_implementation: CPython
py_version: 3.6.9
py_virtual_env: True
sys_cores: 8
sys_processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
sys_ram: Total: 62463MB, Available: 54533MB, Used: 7929MB, Free: 54533MB
=============== Pip Packages ===============
absl-py==0.7.1
astor==0.8.0
certifi==2019.6.16
cloudpickle==1.2.2
cycler==0.10.0
cytoolz==0.10.0
dask==2.3.0
decorator==4.4.0
fastcluster==1.1.25
ffmpy==0.2.2
gast==0.2.2
grpcio==1.16.1
h5py==2.9.0
imageio==2.5.0
imageio-ffmpeg==0.3.0
joblib==0.13.2
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
Markdown==3.1.1
matplotlib==2.2.2
mkl-fft==1.0.14
mkl-random==1.0.2
mkl-service==2.3.0
networkx==2.3
numpy==1.16.2
nvidia-ml-py3==7.352.1
olefile==0.46
opencv-python==4.1.1.26
pathlib==1.0.1
Pillow==6.1.0
protobuf==3.8.0
psutil==5.6.3
pyparsing==2.4.2
pyreadline==2.1
python-dateutil==2.8.0
pytz==2019.2
PyWavelets==1.0.3
pywin32==223
PyYAML==5.1.2
scikit-image==0.15.0
scikit-learn==0.21.2
scipy==1.3.1
six==1.12.0
tensorboard==1.14.0
tensorflow==1.14.0
tensorflow-estimator==1.14.0
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==6.0.3
tqdm==4.32.1
Werkzeug==0.15.5
wincertstore==0.2
wrapt==1.11.2
============== Conda Packages ==============
# packages in environment at C:\ProgramData\Miniconda3\envs\faceswap:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu
absl-py 0.7.1 py36_0
astor 0.8.0 py36_0
blas 1.0 mkl
ca-certificates 2019.5.15 1
certifi 2019.6.16 py36_1
cloudpickle 1.2.2 py_0
cudatoolkit 10.0.130 0
cudnn 7.6.0 cuda10.0_0
cycler 0.10.0 py36h009560c_0
cytoolz 0.10.0 py36he774522_0
dask-core 2.3.0 py_0
decorator 4.4.0 py36_1
fastcluster 1.1.25 py36h830ac7b_1000 conda-forge
ffmpeg 4.2 h6538335_0 conda-forge
ffmpy 0.2.2 pypi_0 pypi
freetype 2.9.1 ha9979f8_1
gast 0.2.2 py36_0
grpcio 1.16.1 py36h351948d_1
h5py 2.9.0 py36h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
imageio 2.5.0 py36_0
imageio-ffmpeg 0.3.0 py_0 conda-forge
intel-openmp 2019.4 245
joblib 0.13.2 py36_0
jpeg 9b hb83a4c4_2
keras 2.2.4 0
keras-applications 1.0.8 py_0
keras-base 2.2.4 py36_0
keras-preprocessing 1.1.0 py_1
kiwisolver 1.1.0 py36ha925a31_0
libmklml 2019.0.5 0
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.8.0 h7bd577a_0
libtiff 4.0.10 hb898794_2
markdown 3.1.1 py36_0
matplotlib 2.2.2 py36had4c4a9_2
mkl 2019.4 245
mkl-service 2.3.0 py36hb782905_0
mkl_fft 1.0.14 py36h14836fe_0
mkl_random 1.0.2 py36h343c172_0
networkx 2.3 py_0
numpy 1.16.2 py36h19fb1c0_0
numpy-base 1.16.2 py36hc3f5095_0
nvidia-ml-py3 7.352.1 pypi_0 pypi
olefile 0.46 py36_0
opencv-python 4.1.1.26 pypi_0 pypi
openssl 1.1.1d he774522_0
pathlib 1.0.1 py36_1
pillow 6.1.0 py36hdc69c19_0
pip 19.2.2 py36_0
protobuf 3.8.0 py36h33f27b4_0
psutil 5.6.3 py36he774522_0
pyparsing 2.4.2 py_0
pyqt 5.9.2 py36h6538335_2
pyreadline 2.1 py36_1
python 3.6.9 h5500b2f_0
python-dateutil 2.8.0 py36_0
pytz 2019.2 py_0
pywavelets 1.0.3 py36h8c2d366_1
pywin32 223 py36hfa6e2cd_1
pyyaml 5.1.2 py36he774522_0
qt 5.9.7 vc14h73c81de_0
scikit-image 0.15.0 py36ha925a31_0
scikit-learn 0.21.2 py36h6288b17_0
scipy 1.3.1 py36h29ff71c_0
setuptools 41.0.1 py36_0
sip 4.19.8 py36h6538335_0
six 1.12.0 py36_0
sqlite 3.29.0 he774522_0
tensorboard 1.14.0 py36he3c9ec2_0
tensorflow 1.14.0 gpu_py36h305fd99_0
tensorflow-base 1.14.0 gpu_py36h55fc52a_0
tensorflow-estimator 1.14.0 py_0
tensorflow-gpu 1.14.0 h0d30ee6_0
termcolor 1.1.0 py36_1
tk 8.6.8 hfa6e2cd_0
toolz 0.10.0 py_0
toposort 1.5 py_3 conda-forge
tornado 6.0.3 py36he774522_0
tqdm 4.32.1 py_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_0
werkzeug 0.15.5 py_0
wheel 0.33.4 py36_0
wincertstore 0.2 py36h7fe50ca_0
wrapt 1.11.2 py36he774522_0
xz 5.2.4 h2fa13f4_4
yaml 0.1.7 hc54c509_2
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_0
================= Configs ==================
--------- .faceswap ---------
backend: nvidia
--------- convert.ini ---------
[color.color_transfer]
clip: True
preserve_paper: True
[color.manual_balance]
colorspace: HSV
balance_1: 0.0
balance_2: 0.0
balance_3: 0.0
contrast: 0.0
brightness: 0.0
[color.match_hist]
threshold: 99.0
[mask.box_blend]
type: gaussian
distance: 11.0
radius: 5.0
passes: 1
[mask.mask_blend]
type: normalized
radius: 3.0
passes: 4
erosion: 0.0
[scaling.sharpen]
method: unsharp_mask
amount: 150
radius: 0.3
threshold: 5.0
[writer.ffmpeg]
container: mp4
codec: libx264
crf: 23
preset: medium
tune: none
profile: auto
level: auto
[writer.gif]
fps: 25
loop: 0
palettesize: 256
subrectangles: False
[writer.opencv]
format: png
draw_transparent: False
jpg_quality: 75
png_compress_level: 3
[writer.pillow]
format: png
draw_transparent: False
optimize: False
gif_interlace: True
jpg_quality: 75
png_compress_level: 3
tif_compression: tiff_deflate
--------- extract.ini ---------
[detect.cv2_dnn]
confidence: 50
[detect.mtcnn]
minsize: 20
threshold_1: 0.6
threshold_2: 0.7
threshold_3: 0.7
scalefactor: 0.709
[detect.s3fd_amd]
confidence: 50
batch-size: 8
[detect.s3fd]
confidence: 50
--------- gui.ini ---------
[global]
fullscreen: False
tab: extract
options_panel_width: 30
console_panel_height: 20
font: default
font_size: 9
--------- train.ini ---------
[global]
coverage: 68.75
mask_type: none
mask_blur: False
icnr_init: False
conv_aware_init: False
subpixel_upscaling: False
reflect_padding: False
penalized_mask_loss: True
loss_function: mae
learning_rate: 5e-05
[model.dfl_h128]
lowmem: False
[model.dfl_sae]
input_size: 128
clipnorm: True
architecture: df
autoencoder_dims: 0
encoder_dims: 42
decoder_dims: 21
multiscale_decoder: False
[model.original]
lowmem: False
[model.realface]
input_size: 64
output_size: 128
dense_nodes: 1536
complexity_encoder: 128
complexity_decoder: 512
[model.unbalanced]
input_size: 128
lowmem: False
clipnorm: True
nodes: 1024
complexity_encoder: 128
complexity_decoder_a: 384
complexity_decoder_b: 512
[model.villain]
lowmem: False
[trainer.original]
preview_images: 14
zoom_amount: 5
rotation_range: 10
shift_range: 5
flip_chance: 50
color_lightness: 30
color_ab: 8
color_clahe_chance: 50
color_clahe_max_size: 4``` | closed | 2019-09-14T21:21:26Z | 2019-09-15T22:50:03Z | https://github.com/deepfakes/faceswap/issues/870 | [] | rknightion | 1 |
LAION-AI/Open-Assistant | python | 3,203 | Allow injecting system messages in chat | Feature Request: On the Open Assistant website, when on the chat with the assistant, allow for a single "Assistant" message to be injected before the user input.
Rationale:
The model gets additional training via the thumbs up and thumbs down buttons and we use this to score the responses. However, there are cases that even after 10 retries and fiddling with parameters, the model is incapable of producing a correct output. Therefore all answers are downvoted. I don't think this is very useful without a correct answer to compare against.
However, it is possible to "inject" hints that would make it produce a correct output. As an example, here's the user input I gave:
```
HINT: flat_map
NOTE: Reply in a concise manner, write tiny snippets. Make your answer AS SHORT AS POSSIBLE.
In rust suppose we have an iterator (.into_iter()) over a Vec<String> we want to collect into a new vec. I want something similar to a map() function, but that instead of yielding a single element, it could yield many. This is because I might encounter an item that is too big and I might want to split it into smaller pieces, creating more elements in place. An iterator should be able to do this, but I don't know if there's a function to do this.
```
This creates the correct answer after editing. However, voting thumbs up for this does include the prompt engineering and the training might be not as effective as it could be.
What I am suggesting is to have an extra option to inject these hints before or after my user message, possibly marked as "assistant" for the tool. These hints can be associated with the current message.
This would allow on the data you collect to differentiate what are hints and what is the actual user request.
I really don't care how this is implemented in the UI. It could be even a button that injects a marker in the textbox such as "<----- PLACE YOUR ASSISTANT HINTS BELOW ---->", and the tool can parse it afterwards. Or a textbox that pops up. All that matters is that this option is visible in the UI in such a way that everyone uses it in the same way. | open | 2023-05-20T18:10:23Z | 2023-05-28T12:31:46Z | https://github.com/LAION-AI/Open-Assistant/issues/3203 | [
"feature"
] | deavid | 2 |
microsoft/qlib | deep-learning | 976 | how online_svr run backtest | i want use run backtest on rolling_online_management script 。
how can i do ? | closed | 2022-03-13T09:46:00Z | 2022-06-21T18:01:59Z | https://github.com/microsoft/qlib/issues/976 | [
"question",
"stale"
] | louis-xuy | 3 |
pbugnion/gmaps | jupyter | 87 | The ability to load arbitrary data sets | It would be great to have api support that allows you add any data set you want instead of just the few added.
| closed | 2016-10-03T17:18:56Z | 2016-10-04T16:11:23Z | https://github.com/pbugnion/gmaps/issues/87 | [] | gregv21v | 2 |
bmoscon/cryptofeed | asyncio | 118 | Coinbene publishes repeated trades | Coinbene uses REST for market data. To get trades, it's API allows you to request the last N trades, up to 2,000. (See https://github.com/Coinbene/API-Documents/wiki/1.1.2-Get-Trades-%5BMarket%5D ). Thus, a way to get trades is to repeatedly query the last trades and publish only non-repeated trades. The current code does this, but there's a bug in the logic, in this function:
https://github.com/bmoscon/cryptofeed/blob/5016b0cdba5ae69d7f86e403f9ada59f6446a79a/cryptofeed/coinbene/coinbene.py#L28
Let's say you get trades with ids: a,b,c. You'll publish these and store a,b,c in the last_trade_update dict. If a,b,c repeats again next poll, you won't publish, but the last_trade_update will get cleared out since the update dict is never updated. If a,b,c comes up again, it will be republished.
You may have to check on timestamps instead, or keep a list of all tradeIDs. Several APIs in other exchanges allow you to get all trades after a given timestamp, so perhaps timestamp checking is the way to go? | closed | 2019-07-15T01:54:59Z | 2019-08-06T12:51:03Z | https://github.com/bmoscon/cryptofeed/issues/118 | [
"bug",
"good first issue"
] | glgnohk | 2 |
biolab/orange3 | pandas | 6,057 | Implementing Numba in Python and Apple Accelerate (matrix processor support) in Numpy? | <!--
Thanks for taking the time to submit a feature request!
For the best chance at our team considering your request, please answer the following questions to the best of your ability.
-->
**What's your use case?**
<!-- In other words, what's your pain point? -->
By using Numba and the Apple Accelerate (matrix) compiler directives for Numpy the performance might increase for larger data sets and longer operations.
<!-- Is your request related to a problem, or perhaps a frustration? -->
Just some thoughts...
<!-- Tell us the story that led you to write this request. -->
Want to use Orange3 to be a good analysis tool for very large datasets and complex calculations.
**What's your proposed solution?**
<!-- Be specific, clear, and concise. -->
**Are there any alternative solutions?**
| closed | 2022-07-11T13:24:43Z | 2023-01-10T10:59:35Z | https://github.com/biolab/orange3/issues/6057 | [] | stenerikbjorling | 1 |
hbldh/bleak | asyncio | 1,724 | Difficulty connecting to BLE devices on specific adapter | Problem: difficulty connecting (timeout or not found) to BLE devices using internal bluetooth adapter.
When using the Mediatek MT7922 802.11ax with bluetooth 5.2 support I have trouble connecting to 3 different BLE devices. I'm running:
- Ubuntu 24.04, 6.11.0-17-generic
- Bluez `5.72-0ubuntu5`
- bleak version `0.22.3`
- python: `3.12.3`
The devices are:
- P11 Smartwatch (running an nrf something)
- Colmi R02 smart ring (running BlueX RF03)
- Colmi R10 smart ring (running RTL8762)
I'm not trying to do anything particular complicated, just connect so that I can read/write some GATT characteristics.
Here's a minimal example I've used to see if the issue was the device being asleep or something else
```python
import asyncio
import bleak
from bleak import BleakClient
async def main():
tries = 0
success = 0
while True:
tries += 1
client = BleakClient("70:CB:0D:D0:34:1C")
try:
async with client:
success += 1
print("Connection success")
except TimeoutError:
print("Timeout")
except bleak.exc.BleakDeviceNotFoundError:
print("not found")
print(f"{success} / {tries}")
asyncio.run(main())
```
This results in a mix of timeout and not found exceptions.
If I run the same script using a generic UGREEN USB bluetooth 4 adapter, I can successfully connect to all 3 devices consistently.
Here's the output of `hciconfig` , where hci1 is the USB adapter, and hci0 is the internal mediatek one.
```
hci1: Type: Primary Bus: USB
BD Address: 00:1A:7D:DA:71:11 ACL MTU: 310:10 SCO MTU: 64:8
UP RUNNING
RX bytes:24125 acl:110 sco:0 events:847 errors:0
TX bytes:7005 acl:95 sco:0 commands:159 errors:0
hci0: Type: Primary Bus: USB
BD Address: 78:46:5C:01:23:70 ACL MTU: 1021:6 SCO MTU: 240:8
UP RUNNING PSCAN
RX bytes:19991554 acl:400 sco:0 events:393929 errors:0
TX bytes:556678 acl:306 sco:0 commands:3385 errors:0
```
I thought perhaps the issue was with BlueZ or the linux driver but if I use `gattcat` from [bluer-tools](https://crates.io/crates/bluer-tools) (built on the [official bluez rust bindings](https://github.com/bluez/bluer)) I am able to connect and read attributes from all 3 devices using the internal Mediatek adapter. I think that helps isolate the issue to Bleak, since both are using Bluez/D-Bus and the same hardware.
One thing I do notice with `gattcat` is that it takes significantly longer to connect using the mediatek device compared to the usb one, so perhaps it's an issue with things timing out?
I've attached wireshark captures of the above script using both adapters, one successful, one not. One difference I notice is that the usb adapter receives LE Advertising Reports, where the internal mediatek one receives LE Extended Reports. I'm pretty new to bluetooth internals, so maybe that's just to be expected with Bluetooth 5+ adapters.
- https://cdn.tahnok.ca/u/working.pcapng
- https://cdn.tahnok.ca/u/broken.pcapng
Some ideas as an newcomer:
- difference in how bluez handles bt 5+ adapters, or other change in hci
- difference in the actual BLE protocol in 5?
- adapter is just slower, timeouts need to be longer?
- MTU something something?
I initially noticed this in my work on https://github.com/tahnok/colmi_r02_client where several people reported issues connecting.
Seems similar to https://github.com/hbldh/bleak/issues/1505 | open | 2025-02-22T22:28:32Z | 2025-02-22T22:40:46Z | https://github.com/hbldh/bleak/issues/1724 | [] | tahnok | 0 |
google-research/bert | nlp | 1,274 | use tensorflow2.0 | if I use tensorflow>=2.0 ,the model that have trained wether still useful? | open | 2021-11-03T01:08:53Z | 2022-06-08T13:07:21Z | https://github.com/google-research/bert/issues/1274 | [] | AutumnLeavesCHE | 3 |
ranaroussi/yfinance | pandas | 1,291 | Scraper error "TypeError: string indices must be integers" - Yahoo decrypt fail | ## Updates
### 2023 January 13
By the time of posting the issue (2023 January 12), the issue only occured sometimes. The library is now (2023 January 13) completely broken and I am unable to retrieve any stock informatio
### 2023 January 14
Fix has been merged to the branch `dev`
## Info about your system:
yfinance version: 0.2.3
Operating system: macOS Monteray 12.0.1
### Snippet that can recreate the error
```
stock = yf.Ticker("^GSPC")
info = stock.info
```
## Error
Message:`TypeError: string indices must be integers`
It seems to be a problem where the scraper is not scraping the correct information, leading to a crash.
### Traceback:
```
Traceback (most recent call last):
File "/home/2022/szhang139/.local/lib/python3.10/site-packages/apscheduler/executors/base_py3.py", line 30, in run_coroutine_job
retval = await job.func(*job.args, **job.kwargs)
File "/home/2022/szhang139/repos/STONK/src/main.py", line 61, in notify
market = get_major_index(f'Market Close - {daytime.today_date()}')
File "/home/2022/szhang139/repos/STONK/src/market_info.py", line 63, in get_major_index
sp500 = get_stock('^GSPC')
File "/home/2022/szhang139/repos/STONK/src/market_info.py", line 41, in get_stock
stock_info = get_stock_info(stock_name)
File "/home/2022/szhang139/repos/STONK/src/market_info.py", line 8, in get_stock_info
info = stock.info
File "/home/2022/szhang139/.local/lib/python3.10/site-packages/yfinance/ticker.py", line 138, in info
return self.get_info()
File "/home/2022/szhang139/.local/lib/python3.10/site-packages/yfinance/base.py", line 894, in get_info
data = self._quote.info
File "/home/2022/szhang139/.local/lib/python3.10/site-packages/yfinance/scrapers/quote.py", line 27, in info
self._scrape(self.proxy)
File "/home/2022/szhang139/.local/lib/python3.10/site-packages/yfinance/scrapers/quote.py", line 58, in _scrape
quote_summary_store = json_data['QuoteSummaryStore']
```
### Frequency
The error occurs in no apparent pattern. Every time it occurs, it seem to persist for some range of time before it recovers back to normal.
n.
| closed | 2023-01-12T22:06:11Z | 2025-01-28T09:20:06Z | https://github.com/ranaroussi/yfinance/issues/1291 | [] | SamZhang02 | 39 |
scikit-image/scikit-image | computer-vision | 7,105 | invalid colour conversion raises error rather than warning | This seems like a bug in colour conversion.
```python
lab2rgb([0, 0, 28.0])
```
raises `TypeError: 'numpy.float64' object does not support item assignment`
while:
```python
lab2rgb([0, 0, 27.0])
```
works fine.
This difference is because the former results in a negative z-value in _lab2xyz conversion. The intended behaviour seems to be to assign this to 0 and return the n_invalid count back to the caller for warnings to be raised.
However, the attempt to reassign is performed by indexing on the z value (a numpy.float64 object) which raises the type error in the last line here.
https://github.com/scikit-image/scikit-image/blob/2336565966acdcf2bfd6e6a7322c6f1f477fca8e/skimage/color/colorconv.py#L1173C1-L1182C23
A simple fix is for this last line to read z=0 rather than z[invalid] = 0
Should I submit a pull request for this? | closed | 2023-08-21T15:20:39Z | 2023-09-16T14:16:36Z | https://github.com/scikit-image/scikit-image/issues/7105 | [
":beginner: Good first issue",
":bug: Bug"
] | marcusmchale | 2 |
TencentARC/GFPGAN | deep-learning | 74 | How to remove sharpening? | Hi. Thanks for your excellent research work.
I have a question about how to remove sharpening in v2 model? | open | 2021-10-08T00:41:18Z | 2021-10-08T00:41:18Z | https://github.com/TencentARC/GFPGAN/issues/74 | [] | xuewengeophysics | 0 |
opengeos/leafmap | jupyter | 737 | Styles are not working with streamlit | ### Environment Information
- leafmap version: lates
- Python version: 3.11
- Operating System: Linux
### Description
The Color Styles are not working with streamlit UI. I have followed below two approaches
1- Using ```m.add_gdf(gdf, layer_name = "ABD", style_callback= lambda feature: style_function_custom(feature, "f1"))```
2. Converting GDF to GeoJSON and Adding Explicit properties of color for each feature into feature collection
Looks like, m.to_streamlit is not handling the Polygone colors.
```
| closed | 2024-05-10T10:58:20Z | 2024-06-16T23:18:04Z | https://github.com/opengeos/leafmap/issues/737 | [
"bug"
] | amjadraza | 3 |
tflearn/tflearn | data-science | 1,122 | Tensorboard Image Summaries with TFLearn? | Hello! I'm trying to see my feature maps in my neural network, or even just the kinds of outputs that my neural network is pumping out. I have no idea how to do this with tflearn though... What should I add to the following code so I can see some images in tensorboard?
`
import tflearn
import datautil
import tensorflow as tf
batch_size = 64
testX, testY = datautil.getBatchRange(batch_size)
network = tflearn.input_data([None, 280, 120, 3], name='input')
network = tflearn.conv_2d(network, 3, 5, activation='leaky_relu')
network = tflearn.conv_2d(network, 3, 20,strides=[1,5,5,1], activation='leaky_relu')
network = tflearn.fully_connected(network, 336, activation='leaky_relu')
network = tflearn.reshape(network, (-1, 12, 28), name="output")
image = tf.reshape(network[:1], [-1, 12, 28, 1])
tf.summary.image("output", image)
network = tflearn.regression(network, optimizer='adam', learning_rate=0.05, # <--impatient
loss='categorical_crossentropy', name='target')
model = tflearn.DNN(network, tensorboard_verbose=3,
checkpoint_path="./RangeModel/", best_checkpoint_path="./Bestmodel/best.model", max_checkpoints=5)
for x in range(500):
X, Y = datautil.getBatchRange(batch_size)
model.fit({'input': X}, {'target': Y}, n_epoch=32,
validation_set=({'input': testX}, {'target': testY}),
snapshot_step=100, show_metric=True)
model.save("RangeModel/"+str(x)+".model")
` | open | 2019-03-10T16:22:46Z | 2021-06-01T05:00:14Z | https://github.com/tflearn/tflearn/issues/1122 | [] | SinclairHudson | 2 |
netbox-community/netbox | django | 18,639 | Prefixes - Wrong depth | ### Deployment Type
Self-hosted
### NetBox Version
v4.2.3
### Python Version
3.10
### Steps to Reproduce
I creating prefixes via terraform
```
variable "networks" {
type = list(object({
cloud = string
prefix = string
labels = list(string)
status = optional(string)
}))
}
resource "netbox_prefix" "tens" {
prefix = "10.0.0.0/8"
status = "container"
tenant_id = var.tenant_id
vrf_id = var.vrf_id
tags = [upper(var.environment)]
}
resource "netbox_prefix" "network" {
for_each = {
for k, v in var.networks : k => v
}
prefix = each.value.prefix
status = each.value.status != null ? each.value.status : "active"
tenant_id = var.tenant_id
vrf_id = var.vrf_id
tags = each.value.cloud == null ? concat([upper(var.environment)], each.value.labels) : concat([upper(each.value.cloud), upper(var.environment)], each.value.labels)
}
```
<details>
<summary>config inside</summary>
```
"networks": [
{
"prefix": "10.128.0.0/10",
"labels": [
"INFRA"
],
"status": "container"
},
{
"cloud":"aws",
"prefix": "10.128.0.0/24",
"labels": [
"PUBLIC"
]
},
{
"cloud":"azr",
"prefix": "10.128.1.0/24",
"labels": [
"PUBLIC"
]
},
{
"cloud":"gcp",
"prefix": "10.128.2.0/24",
"labels": [
"PUBLIC"
]
}
]
```
</details>
### Expected Behavior
Prefixes created
Container 10/8 have depth = 0
Container 10.128.0.0/10 have depth = 1
Prefixes (10.128.0.0/24, 10.128.1.0/24, 10.128.2.0/24) have the same depth = 2
Children count have a corresponding value.
### Observed Behavior
"random" depth
one example
<img width="624" alt="Image" src="https://github.com/user-attachments/assets/227c1a8a-ad75-4a1e-bec3-9729b05de540" />
another one
<img width="614" alt="Image" src="https://github.com/user-attachments/assets/39c31303-9eb0-45c8-ad98-126dd51b6d57" />
<details>
<summary>as you can see below, same thing in output fro rest request (_depth attribute)</summary>
```
{
"id": 1196,
"url": "https://192.168.1.82:9443/api/ipam/prefixes/1196/",
"display_url": "https://192.168.1.82:9443/ipam/prefixes/1196/",
"display": "10.0.0.0/8",
"family": {
"value": 4,
"label": "IPv4"
},
"prefix": "10.0.0.0/8",
"vrf": {
"id": 6,
"url": "https://192.168.1.82:9443/api/ipam/vrfs/6/",
"display": "devnet-infra_v3.3",
"name": "devnet-infra_v3.3",
"rd": null,
"description": ""
},
"scope_type": null,
"scope_id": null,
"scope": null,
"tenant": {
"id": 2,
"url": "https://192.168.1.82:9443/api/tenancy/tenants/2/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"description": "This is the environment for network development"
},
"vlan": null,
"status": {
"value": "container",
"label": "Container"
},
"role": null,
"is_pool": false,
"mark_utilized": false,
"description": "",
"comments": "",
"tags": [
{
"id": 13,
"url": "https://192.168.1.82:9443/api/extras/tags/13/",
"display_url": "https://192.168.1.82:9443/extras/tags/13/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"color": "ff5722"
}
],
"custom_fields": {},
"created": "2025-02-12T17:07:02.035430Z",
"last_updated": "2025-02-12T17:07:02.035438Z",
"children": 4,
"_depth": 0
},
{
"id": 1198,
"url": "https://192.168.1.82:9443/api/ipam/prefixes/1198/",
"display_url": "https://192.168.1.82:9443/ipam/prefixes/1198/",
"display": "10.128.0.0/10",
"family": {
"value": 4,
"label": "IPv4"
},
"prefix": "10.128.0.0/10",
"vrf": {
"id": 6,
"url": "https://192.168.1.82:9443/api/ipam/vrfs/6/",
"display": "devnet-infra_v3.3",
"name": "devnet-infra_v3.3",
"rd": null,
"description": ""
},
"scope_type": null,
"scope_id": null,
"scope": null,
"tenant": {
"id": 2,
"url": "https://192.168.1.82:9443/api/tenancy/tenants/2/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"description": "This is the environment for network development"
},
"vlan": null,
"status": {
"value": "container",
"label": "Container"
},
"role": null,
"is_pool": false,
"mark_utilized": false,
"description": "",
"comments": "",
"tags": [
{
"id": 13,
"url": "https://192.168.1.82:9443/api/extras/tags/13/",
"display_url": "https://192.168.1.82:9443/extras/tags/13/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"color": "ff5722"
}
],
"custom_fields": {},
"created": "2025-02-12T17:07:02.078119Z",
"last_updated": "2025-02-12T17:07:02.078126Z",
"children": 3,
"_depth": 0
},
{
"id": 1204,
"url": "https://192.168.1.82:9443/api/ipam/prefixes/1204/",
"display_url": "https://192.168.1.82:9443/ipam/prefixes/1204/",
"display": "10.128.0.0/24",
"family": {
"value": 4,
"label": "IPv4"
},
"prefix": "10.128.0.0/24",
"vrf": {
"id": 6,
"url": "https://192.168.1.82:9443/api/ipam/vrfs/6/",
"display": "devnet-infra_v3.3",
"name": "devnet-infra_v3.3",
"rd": null,
"description": ""
},
"scope_type": null,
"scope_id": null,
"scope": null,
"tenant": {
"id": 2,
"url": "https://192.168.1.82:9443/api/tenancy/tenants/2/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"description": "This is the environment for network development"
},
"vlan": null,
"status": {
"value": "active",
"label": "Active"
},
"role": null,
"is_pool": false,
"mark_utilized": false,
"description": "",
"comments": "",
"tags": [
{
"id": 3,
"url": "https://192.168.1.82:9443/api/extras/tags/3/",
"display_url": "https://192.168.1.82:9443/extras/tags/3/",
"display": "AWS",
"name": "AWS",
"slug": "aws",
"color": "ffe4e1"
},
{
"id": 13,
"url": "https://192.168.1.82:9443/api/extras/tags/13/",
"display_url": "https://192.168.1.82:9443/extras/tags/13/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"color": "ff5722"
},
{
"id": 12,
"url": "https://192.168.1.82:9443/api/extras/tags/12/",
"display_url": "https://192.168.1.82:9443/extras/tags/12/",
"display": "PUBLIC",
"name": "PUBLIC",
"slug": "public",
"color": "607d8b"
}
],
"custom_fields": {},
"created": "2025-02-12T17:34:26.645054Z",
"last_updated": "2025-02-12T17:34:26.645059Z",
"children": 0,
"_depth": 2
},
{
"id": 1197,
"url": "https://192.168.1.82:9443/api/ipam/prefixes/1197/",
"display_url": "https://192.168.1.82:9443/ipam/prefixes/1197/",
"display": "10.128.1.0/24",
"family": {
"value": 4,
"label": "IPv4"
},
"prefix": "10.128.1.0/24",
"vrf": {
"id": 6,
"url": "https://192.168.1.82:9443/api/ipam/vrfs/6/",
"display": "devnet-infra_v3.3",
"name": "devnet-infra_v3.3",
"rd": null,
"description": ""
},
"scope_type": null,
"scope_id": null,
"scope": null,
"tenant": {
"id": 2,
"url": "https://192.168.1.82:9443/api/tenancy/tenants/2/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"description": "This is the environment for network development"
},
"vlan": null,
"status": {
"value": "active",
"label": "Active"
},
"role": null,
"is_pool": false,
"mark_utilized": false,
"description": "",
"comments": "",
"tags": [
{
"id": 2,
"url": "https://192.168.1.82:9443/api/extras/tags/2/",
"display_url": "https://192.168.1.82:9443/extras/tags/2/",
"display": "AZR",
"name": "AZR",
"slug": "azr",
"color": "ffe4e1"
},
{
"id": 13,
"url": "https://192.168.1.82:9443/api/extras/tags/13/",
"display_url": "https://192.168.1.82:9443/extras/tags/13/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"color": "ff5722"
},
{
"id": 12,
"url": "https://192.168.1.82:9443/api/extras/tags/12/",
"display_url": "https://192.168.1.82:9443/extras/tags/12/",
"display": "PUBLIC",
"name": "PUBLIC",
"slug": "public",
"color": "607d8b"
}
],
"custom_fields": {},
"created": "2025-02-12T17:07:02.075949Z",
"last_updated": "2025-02-12T17:07:02.075956Z",
"children": 0,
"_depth": 0
},
{
"id": 1199,
"url": "https://192.168.1.82:9443/api/ipam/prefixes/1199/",
"display_url": "https://192.168.1.82:9443/ipam/prefixes/1199/",
"display": "10.128.2.0/24",
"family": {
"value": 4,
"label": "IPv4"
},
"prefix": "10.128.2.0/24",
"vrf": {
"id": 6,
"url": "https://192.168.1.82:9443/api/ipam/vrfs/6/",
"display": "devnet-infra_v3.3",
"name": "devnet-infra_v3.3",
"rd": null,
"description": ""
},
"scope_type": null,
"scope_id": null,
"scope": null,
"tenant": {
"id": 2,
"url": "https://192.168.1.82:9443/api/tenancy/tenants/2/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"description": "This is the environment for network development"
},
"vlan": null,
"status": {
"value": "active",
"label": "Active"
},
"role": null,
"is_pool": false,
"mark_utilized": false,
"description": "",
"comments": "",
"tags": [
{
"id": 13,
"url": "https://192.168.1.82:9443/api/extras/tags/13/",
"display_url": "https://192.168.1.82:9443/extras/tags/13/",
"display": "DEVNET",
"name": "DEVNET",
"slug": "devnet",
"color": "ff5722"
},
{
"id": 1,
"url": "https://192.168.1.82:9443/api/extras/tags/1/",
"display_url": "https://192.168.1.82:9443/extras/tags/1/",
"display": "GCP",
"name": "GCP",
"slug": "gcp",
"color": "ffe4e1"
},
{
"id": 12,
"url": "https://192.168.1.82:9443/api/extras/tags/12/",
"display_url": "https://192.168.1.82:9443/extras/tags/12/",
"display": "PUBLIC",
"name": "PUBLIC",
"slug": "public",
"color": "607d8b"
}
],
"custom_fields": {},
"created": "2025-02-12T17:07:02.101193Z",
"last_updated": "2025-02-12T17:15:53.791048Z",
"children": 0,
"_depth": 1
}
```
</details> | closed | 2025-02-13T12:57:32Z | 2025-03-01T04:23:02Z | https://github.com/netbox-community/netbox/issues/18639 | [
"type: bug",
"status: revisions needed",
"pending closure"
] | rybakovanton-metta | 3 |
deepinsight/insightface | pytorch | 1,971 | partial fc petrained model inference: what to use for fp16 flag | I am trying to make an inference using the partialfc r100 and r50 models, and I have downloaded them from:
https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#5-pretrain-models
I see that the models are named `16_**` and `16*`
Is it safe to assume that these are fp16 models?
So, in line:
https://github.com/deepinsight/insightface/blob/17cdeab12a35efcebc2660453a8cbeae96e20950/recognition/arcface_torch/inference.py#L22
should I be setting it as:
```
net = get_model(name, fp16=True)
```
is this the correct way to set the model for inference on partialfc using the given pretrained model?
thank you for the awesome package.
| open | 2022-04-14T14:56:34Z | 2022-04-14T14:56:34Z | https://github.com/deepinsight/insightface/issues/1971 | [] | abaqusstudent | 0 |
wkentaro/labelme | deep-learning | 1,563 | Crash after creating a new polygon: 'NoneType' object has no attribute 'name' Unhandled Python exception | ### Provide environment information
**Environment**: Linux (local)
**Version**: 5.8.0
**Reproducible**: Yes
**Debugging info**:
```
which python; python --version; python -m pip list | grep labelme
/usr/bin/python
Python 3.12.8
labelme 5.8.0
labelme
2025-03-17 15:27:35.249 | INFO | labelme.config:get_config:66 - Loading config file from: /root/.labelmerc
QStandardPaths: XDG_RUNTIME_DIR not set, defaulting to '/tmp/runtime-root'
2025-03-17 15:28:35.867 | DEBUG | labelme.__main__:write:23 - Traceback (most recent call last):
2025-03-17 15:28:35.867 | DEBUG | labelme.__main__:write:23 - File "/usr/local/lib/python3.12/site-packages/labelme/widgets/canvas.py", line 435, in mousePressEvent
2025-03-17 15:28:35.868 | DEBUG | labelme.__main__:write:23 - self.finalise()
2025-03-17 15:28:35.868 | DEBUG | labelme.__main__:write:23 - File "/usr/local/lib/python3.12/site-packages/labelme/widgets/canvas.py", line 797, in finalise
2025-03-17 15:28:35.868 | DEBUG | labelme.__main__:write:23 - model_name=self._sam.name,
2025-03-17 15:28:35.868 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.869 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.869 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.869 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.869 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.870 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.871 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.871 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.871 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.871 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.872 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.872 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.872 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.872 | DEBUG | labelme.__main__:write:23 - ^
2025-03-17 15:28:35.873 | DEBUG | labelme.__main__:write:23 - AttributeError
2025-03-17 15:28:35.873 | DEBUG | labelme.__main__:write:23 - :
2025-03-17 15:28:35.873 | DEBUG | labelme.__main__:write:23 - 'NoneType' object has no attribute 'name'
Unhandled Python exception
[1] 8890 IOT instruction (core dumped) labelme
```
### What OS are you using?
Linux (Fedora)
### Describe the Bug
After creating the first and only polygon it crashes when the last node of the polygon is connected.
### Expected Behavior
To not crash.
### To Reproduce
As far as I can tell, it happens every time on Linux. | closed | 2025-03-17T19:35:13Z | 2025-03-24T03:37:22Z | https://github.com/wkentaro/labelme/issues/1563 | [
"issue::bug"
] | hack-r | 7 |
roboflow/supervision | deep-learning | 1,707 | Implement metrics comparison table & plotting | The metrics system allows the users to compute a metrics result - a class with values for a specific metrics run.
When comparing multiple models, a natural next step is to aggregate the results into a single table and/or plot them on a single chart.
Let's make this step easy!
I propose two new functions:
```python
def aggregate_metric_results(metrics_results: List[MetricResult], *, include_object_sizes=False) -> pd.DataFrame:
"""
Raises when different types of metrics results are passed in
"""
def plot_aggregate_metric_results(metrics_results: List[MetricResult], *, include_object_sizes=False) -> None:
...
class MetricResult(ABC):
@abstractmethod
def to_pandas():
raise NotImplementedError()
def plot():
raise NotImplementedError()
```
---
Several questions we need to address:
1. Would it be better for `plot_aggregate_metric_results` to take in a `pd.DataFrame`? This way, the user can apply their own sorting or preprocessing, but we're not guaranteed to have the fields we need.
2. Could the naming be improved?
---
Suggested by @David-rn, [discussion](https://github.com/roboflow/supervision/discussions/1706#discussioncomment-11445217)
| open | 2024-12-03T10:05:11Z | 2024-12-09T21:44:33Z | https://github.com/roboflow/supervision/issues/1707 | [
"enhancement"
] | LinasKo | 7 |
ClimbsRocks/auto_ml | scikit-learn | 206 | user validation: make sure there are at least two classes for classification problems | right now the error message is uninformative. | open | 2017-04-24T21:07:30Z | 2017-04-24T21:07:30Z | https://github.com/ClimbsRocks/auto_ml/issues/206 | [] | ClimbsRocks | 0 |
holoviz/panel | plotly | 7,281 | Tabulator: Separate aggregators for different columns blocked by type hint | <details>
<summary>Software Version Info</summary>
```plaintext
panel 1.4.5
```
</details>
#### Description of expected behavior and the observed behavior
- 'If separate aggregators for different columns are required the dictionary may be nested as {index_name: {column_name: aggregator}}'
- Aggregators parameter in class DataTabulator accepts only Dict(String, String)
#### Complete, minimal, self-contained example code that reproduces the issue
```python
pn.widgets.Tabulator(
value = value,
hierarchical = True,
aggregators = {index_name: {column_name: aggregator}}
```
#### Stack traceback and/or browser JavaScript console output
```python
ValueError: failed to validate DataTabulator(id=id, ...).aggregators: expected a dict of type Dict(String, String), got a dict with invalid values for keys: index_name
``` | closed | 2024-09-15T09:12:49Z | 2024-12-02T13:17:57Z | https://github.com/holoviz/panel/issues/7281 | [
"component: tabulator"
] | AxZolotl | 2 |
Anjok07/ultimatevocalremovergui | pytorch | 1,276 | Error issues while extracting instrumental | Last Error Received:
Process: VR Architecture
If this error persists, please contact the developers with the error details.
Raw Error Details:
ValueError: "zero-size array to reduction operation maximum which has no identity"
Traceback Error: "
File "UVR.py", line 6638, in process_start
File "separate.py", line 1070, in seperate
File "separate.py", line 382, in final_process
File "separate.py", line 446, in write_audio
File "separate.py", line 419, in save_with_message
File "separate.py", line 389, in save_audio_file
File "lib_v5\spec_utils.py", line 86, in normalize
File "numpy\core\_methods.py", line 40, in _amax
"
Error Time Stamp [2024-04-07 21:33:37]
Full Application Settings:
vr_model: 5_HP-Karaoke-UVR
aggression_setting: 5
window_size: 512
mdx_segment_size: 256
batch_size: Default
crop_size: 256
is_tta: False
is_output_image: False
is_post_process: False
is_high_end_process: False
post_process_threshold: 0.2
vr_voc_inst_secondary_model: No Model Selected
vr_other_secondary_model: No Model Selected
vr_bass_secondary_model: No Model Selected
vr_drums_secondary_model: No Model Selected
vr_is_secondary_model_activate: False
vr_voc_inst_secondary_model_scale: 0.9
vr_other_secondary_model_scale: 0.7
vr_bass_secondary_model_scale: 0.5
vr_drums_secondary_model_scale: 0.5
demucs_model: Choose Model
segment: Default
overlap: 0.25
overlap_mdx: Default
overlap_mdx23: 8
shifts: 2
chunks_demucs: Auto
margin_demucs: 44100
is_chunk_demucs: False
is_chunk_mdxnet: False
is_primary_stem_only_Demucs: False
is_secondary_stem_only_Demucs: False
is_split_mode: True
is_demucs_combine_stems: True
is_mdx23_combine_stems: True
demucs_voc_inst_secondary_model: No Model Selected
demucs_other_secondary_model: No Model Selected
demucs_bass_secondary_model: No Model Selected
demucs_drums_secondary_model: No Model Selected
demucs_is_secondary_model_activate: False
demucs_voc_inst_secondary_model_scale: 0.9
demucs_other_secondary_model_scale: 0.7
demucs_bass_secondary_model_scale: 0.5
demucs_drums_secondary_model_scale: 0.5
demucs_pre_proc_model: No Model Selected
is_demucs_pre_proc_model_activate: False
is_demucs_pre_proc_model_inst_mix: False
mdx_net_model: Choose Model
chunks: Auto
margin: 44100
compensate: Auto
denoise_option: None
is_match_frequency_pitch: True
phase_option: Automatic
phase_shifts: None
is_save_align: False
is_match_silence: True
is_spec_match: False
is_mdx_c_seg_def: False
is_invert_spec: False
is_deverb_vocals: False
deverb_vocal_opt: Main Vocals Only
voc_split_save_opt: Lead Only
is_mixer_mode: False
mdx_batch_size: Default
mdx_voc_inst_secondary_model: No Model Selected
mdx_other_secondary_model: No Model Selected
mdx_bass_secondary_model: No Model Selected
mdx_drums_secondary_model: No Model Selected
mdx_is_secondary_model_activate: False
mdx_voc_inst_secondary_model_scale: 0.9
mdx_other_secondary_model_scale: 0.7
mdx_bass_secondary_model_scale: 0.5
mdx_drums_secondary_model_scale: 0.5
is_save_all_outputs_ensemble: True
is_append_ensemble_name: False
chosen_audio_tool: Manual Ensemble
choose_algorithm: Min Spec
time_stretch_rate: 2.0
pitch_rate: 2.0
is_time_correction: True
is_gpu_conversion: False
is_primary_stem_only: True
is_secondary_stem_only: False
is_testing_audio: False
is_auto_update_model_params: True
is_add_model_name: False
is_accept_any_input: False
is_task_complete: False
is_normalization: False
is_use_opencl: False
is_wav_ensemble: False
is_create_model_folder: False
mp3_bit_set: 320k
semitone_shift: 0
save_format: WAV
wav_type_set: PCM_16
device_set: Default
help_hints_var: True
set_vocal_splitter: No Model Selected
is_set_vocal_splitter: False
is_save_inst_set_vocal_splitter: False
model_sample_mode: True
model_sample_mode_duration: 30
demucs_stems: All Stems
mdx_stems: All Stems | open | 2024-04-07T16:10:17Z | 2024-04-07T16:10:17Z | https://github.com/Anjok07/ultimatevocalremovergui/issues/1276 | [] | bachanstar | 0 |
jowilf/starlette-admin | sqlalchemy | 303 | Enhancement: `additional_js_links` in `BaseModelView` | **Is your feature request related to a problem? Please describe.**
If I want to include a js file in a view, (specifically a datatables plugin for the list view), currently it must be included with a field that is rendered on that page.
Additionally, if I want to include that file across all views in my application, it requires subclassing many types of fields.
**Describe the solution you'd like**
`BaseModelView` should have a property: `additional_js_links` (and `additional_css_links`), to allow js / css files to be included for that view.
That way, I can specify it for all model views:
```py
class MyBaseModelView(ModelView):
"""Base settings for all views"""
additional_js_links = ['/static/js/bootstrap_input.js']
```
**Describe alternatives you've considered**
It might be nice to designate a file for specific action types. In my case, it's only needed for the `LIST` view, but specifying / excluding the js+css might be unnecessarily complex, where as this is a simple solution.
**Additional context**
PR forthcoming! | closed | 2023-09-22T16:56:40Z | 2023-09-22T23:18:27Z | https://github.com/jowilf/starlette-admin/issues/303 | [
"enhancement"
] | mrharpo | 0 |
django-oscar/django-oscar | django | 4,169 | Question: Best/proper way to install and use | I'm trying to understand the best way to use and customize Oscar.
For example, I want to be able to show all partners who are selling a product and let the user choose which one (like a selectable multi-vendor marketplace like Amazon's "view buying options":
Should we be forking the repo and building from that? Or should we be installing with PIP into an empty project like the "Building your own shop" and extending. (I'm assuming almost anything can be customized/extended?)
| closed | 2023-09-15T19:28:58Z | 2023-10-11T19:59:02Z | https://github.com/django-oscar/django-oscar/issues/4169 | [] | ecumike | 2 |
amidaware/tacticalrmm | django | 1,208 | Database error after update | Hi!
I've updated from 0.13.4 to 0.14.2, and in the update script I'm getting a bunch of database permission errors:
psycopg2.errors.InsufficientPrivilege: permission denied for table django_migrations
I rebooted & tried again with -force, and i get something similar:
if i run psql i get an error that the role tatical does not exist:
meshcentral still works 100%
any ideas? | closed | 2022-07-13T21:44:44Z | 2022-07-14T00:52:08Z | https://github.com/amidaware/tacticalrmm/issues/1208 | [] | lifeoflejf | 0 |
jumpserver/jumpserver | django | 15,053 | [Question] 命令过滤这个功能模块中，接受动作的作用是什么？ | ### Product Version
v4.2
### Product Edition
- [x] Community Edition
- [ ] Enterprise Edition
- [ ] Enterprise Trial Edition
### Installation Method
- [ ] Online Installation (One-click command installation)
- [ ] Offline Package Installation
- [ ] All-in-One
- [ ] 1Panel
- [x] Kubernetes
- [ ] Source Code
### Environment Information
k8s，阿里云
### 🤔 Question Description
命令过滤功能里面，我配置了一个白名单命令组，然后配置在命令过滤规则中，并选择了接受动作。
我的预期是，我只能执行这个白名单组里面的命令。
但实践中发现，白名单命令组之外的命令也可以执行。那请问，这个接受动作的作用是什么？感觉配置不配置都没有什么用处。
### Expected Behavior
预期行为：可执行命令限制在配置了接受动作的命令组中。
实际行为：非白名单命令组中的命令也可以执行，不明白接受动作的意义是什么？
### Additional Information
_No response_ | open | 2025-03-18T02:36:42Z | 2025-03-21T10:48:58Z | https://github.com/jumpserver/jumpserver/issues/15053 | [
"⏳ Pending feedback",
"🤔 Question"
] | yxxchange | 1 |
Lightning-AI/pytorch-lightning | machine-learning | 20,347 | Can't resume automatically a job, ckpt_path="hpc" throws ValueError from the start | ## Summary
When attempting to resume a job from where it left off before reaching wall-time on a SLURM cluster using PyTorch Lightning, the ckpt_path="hpc" option causes an error if no HPC checkpoint exists yet. This prevents the initial training run from starting.
## Expected Behavior
- The job should be able to resume from an HPC checkpoint if one exists when using in combination:
- `SLURMEnvironment(auto_requeue=True, requeue_signal=signal.SIGUSR1)`
- `trainer.fit(model, datamodule=dm, ckpt_path="hpc")`
- `#SBATCH --signal=SIGUSR1@30`
- If no HPC checkpoint exists (e.g., on the first run), the job should start training from scratch without throwing an error. Currently it throws one:
```
.fit(ckpt_path="hpc")` is set but no HPC checkpoint was found.' Please pass an exact checkpoint path to `.fit(ckpt_path=...)
```
## Current Behavior
### Using `ckpt_path=None` allows the job to start but doesn't resume from the HPC checkpoint when one is created.
If I use `trainer.fit(model, datamodule=dm, ckpt_path=None)`, the SIGUSR1 is correctly catched and the checkpoint `hpc_ckpt_1.ckpt` correctly created. However the checkpoint is not used which is expected because we left `ckpt_path=None`.
```
requeing job 245038...
Requeued SLURM job: 245038
srun: Job step aborted: Waiting up to 62 seconds for job step to finish.
slurmstepd: error: *** JOB 245038 ON jzxh061 CANCELLED AT 2024-10-17T15:45:56 DUE TO JOB REQUEUE ***
slurmstepd: error: *** STEP 245038.0 ON jzxh061 CANCELLED AT 2024-10-17T15:45:56 DUE TO JOB REQUEUE ***
```
### Using `ckpt_path="hpc"` throws an error if no HPC checkpoint is found, preventing the initial training run.
The logic of looking for and loading the hpc checkpoint from what I understood should be handled by setting `ckpt_path="hpc"`
However, as can be seen in https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py#L193C1-L199C46
if an hpc ckpt is not found it throws an error and stops:
```
.fit(ckpt_path="hpc")` is set but no HPC checkpoint was found.' Please pass an exact checkpoint path to `.fit(ckpt_path=...)
```
The issue is that for the very first training of course there would be no hpc checkpoint because we haven't started any training yet
### Relevant issues
#16639
### What version are you seeing the problem on?
v2.4
### How to reproduce the bug
dummy_model.py
```python
import os
import torch
import lightning as L
from torch.utils.data import Dataset
from lightning.pytorch.callbacks import ModelCheckpoint
import argparse
from torchdata.stateful_dataloader import StatefulDataLoader
from torch.distributed.checkpoint.stateful import Stateful
from torch.utils.data.distributed import DistributedSampler
import pickle
import signal
from lightning.pytorch.plugins.environments import SLURMEnvironment
import time
class DummyDataset(Dataset):
def __init__(self, size=100000):
self.size = size
self.data = torch.randn(size, 10)
self.labels = torch.randint(0, 2, (size,))
self.current_index = 0
def __len__(self):
return self.size
def __getitem__(self, idx):
print(f"Accessing index: {idx}, {self.data[idx]}")
return self.data[idx], self.labels[idx]
class DPAwareDataLoader(StatefulDataLoader, Stateful):
def __init__(self, dataset: Dataset, batch_size: int, sampler=None, **kwargs):
super().__init__(dataset, batch_size=batch_size, sampler=sampler, **kwargs)
self._rank_id = f"dp_rank_{sampler.rank if sampler else 0}"
print(self._rank_id, " initialized")
def state_dict(self):
print(f"self._rank_id: ", f"{super().state_dict()}")
return {self._rank_id: super().state_dict()}
def load_state_dict(self, state_dict):
if not state_dict:
return
if self._rank_id not in state_dict:
print(f"DataLoader state is empty for dp rank {self._dp_rank}, expected key {self._rank_id}")
return
print(f"DataLoader state loading for dp rank {self._dp_rank}")
super().load_state_dict(state_dict[self._rank_id])
class DummyDataModule(L.LightningDataModule, Stateful):
def __init__(self, batch_size=32):
super().__init__()
self.batch_size = batch_size
self.train_dataset = None
self.dataloader = None
def setup(self, stage=None):
self.train_dataset = DummyDataset()
# DistributedSampler automatically retrieves world_size and rank
# from the current distributed group.
#
# ref: https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler
#
# In PyTorch Lightning:
# - The distributed environment is initialized by the Trainer.
# - This sets up the process group with the correct world_size and rank.
# - DistributedSampler then uses these values automatically.
#
# By not specifying num_replicas and rank, we allow DistributedSampler
# to adapt to the current distributed setup, making our code more flexible.
# This works seamlessly with PyTorch Lightning's managed distributed training.
#
# Note: This automatic retrieval only works correctly if the distributed
# environment has been initialized, which Lightning ensures before calling setup().
self.sampler = DistributedSampler(
self.train_dataset,
shuffle=False # Ensure deterministic data order across processes for testing purposes
)
def train_dataloader(self):
if self.dataloader is None:
self.dataloader = DPAwareDataLoader(
self.train_dataset,
batch_size=self.batch_size,
sampler=self.sampler,
num_workers=2
)
return self.dataloader
def state_dict(self):
return {
"dataloader_state": self.dataloader.state_dict() if self.dataloader else None,
}
def load_state_dict(self, state_dict):
if self.dataloader and state_dict["dataloader_state"]:
self.dataloader.load_state_dict(state_dict["dataloader_state"])
class DummyModel(L.LightningModule):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(10, 2)
self.loss = torch.nn.CrossEntropyLoss()
self.example_count = 0
self.custom_global_step = 0
def training_step(self, batch, batch_idx):
time.sleep(10)
x, y = batch
y_hat = self.layer(x)
loss = self.loss(y_hat, y)
self.log('train_loss', loss)
self.example_count += len(x)
self.custom_global_step = self.global_step
if self.example_count % 100 == 0:
print(f"GPU {self.global_rank}: Processed {self.example_count} examples, Global Step: {self.custom_global_step}")
return loss
def on_save_checkpoint(self, checkpoint):
checkpoint['example_count'] = self.example_count
checkpoint['custom_global_step'] = self.custom_global_step
def on_load_checkpoint(self, checkpoint):
self.example_count = checkpoint['example_count']
self.custom_global_step = checkpoint['custom_global_step']
def on_train_start(self):
print(f"GPU {self.global_rank}: Starting/Resuming training. Example count: {self.example_count}, Global Step: {self.custom_global_step}")
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.02)
def main():
parser = argparse.ArgumentParser(description="Train a dummy model with a unique run name.")
parser.add_argument("run_name", type=str, help="Unique name for this training run")
args = parser.parse_args()
print("="*30, args.run_name, "="*30)
log_dir = os.path.join("logs", args.run_name)
os.makedirs(log_dir, exist_ok=True)
model = DummyModel()
dm = DummyDataModule(batch_size=4)
checkpoint_callback = ModelCheckpoint(
dirpath=os.path.join(log_dir, 'checkpoints'),
filename='model-{epoch:02d}-{train_loss:.2f}',
save_top_k=1,
verbose=True,
monitor='train_loss',
mode='min',
every_n_epochs=1,
save_last=True
)
trainer = L.Trainer(
max_epochs=100,
devices=4,
accelerator='gpu',
strategy='ddp',
callbacks=[checkpoint_callback],
plugins=[SLURMEnvironment(auto_requeue=True, requeue_signal=signal.SIGUSR1)],
default_root_dir=log_dir,
use_distributed_sampler=False,
)
trainer.fit(model, datamodule=dm, ckpt_path="last")
if __name__ == '__main__':
main()
```
dummy_slurm.sh
```bash
#!/bin/bash
#SBATCH --job-name=auto_requeue_test
#SBATCH -C h100
#SBATCH -A ycy@h100
#SBATCH --nodes=1
#SBATCH --qos=qos_gpu_h100-dev
#SBATCH --ntasks-per-node=4
#SBATCH --gpus-per-node=4
#SBATCH --time=00:3:00
#SBATCH --signal=SIGUSR1@30 # Send signal 30 seconds before time limit
# Load any necessary modules or activate your environment here
# For example:
module purge
module load arch/h100
module load pytorch-gpu/py3/2.4.0
export PYTHONUSERBASE=$WORK/python_envs/worldmodel
echo "Starting job at $(date)"
# Generate a unique run name using the current date and time
RUN_NAME="run_${SLURM_JOB_ID}"
# Run the Python script with the unique run name
srun python dummy_model.py "$RUN_NAME"
echo "Job ended or requeued at $(date)"
```
### Environment
<details>
<summary>Current environment</summary>
```
- PyTorch Lightning Version: 2.4.0
- PyTorch Version: 2.4.0
- Python version: 3.11.9
- How you installed Lightning(`conda`, `pip`, source): pip
```
</details>
| open | 2024-10-18T09:25:47Z | 2024-10-25T00:19:29Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20347 | [
"bug",
"needs triage",
"ver: 2.4.x"
] | F-Barto | 1 |
marshmallow-code/flask-smorest | rest-api | 336 | description for route | Hi
how can I have `description` at route level ?
something like
```yaml
...
paths:
/api/adduser:
post:
description: |
test 1 \
test 2 :
* check 1
1. check 1\
1.1 check2
* *check 1*
* **check 1**
responses:
'201':
description: User created
...
```
I could not find any input argument to add to the generated json file
```yml
description: |
test 1 \
test 2 :
* check 1
1. check 1\
1.1 check2
* *check 1*
* **check 1**
```
| closed | 2022-03-16T15:56:23Z | 2022-03-18T10:42:34Z | https://github.com/marshmallow-code/flask-smorest/issues/336 | [
"question"
] | arabnejad | 2 |
marshmallow-code/marshmallow-sqlalchemy | sqlalchemy | 223 | Error `TypeError: unhashable type: 'list'` | If to try to deserialize model which should have relationship one to many and put the array in this field then there will be an error `TypeError: unhashable type: 'list'` instead of ValidationError
Example
class M(Base):
....
store_id = Column(ForeignKey('stores.id'))
store = relationship("Store", uselist=False)
....
M().load({"store":[1,2,3]})
| closed | 2019-07-03T10:43:57Z | 2025-01-17T19:24:42Z | https://github.com/marshmallow-code/marshmallow-sqlalchemy/issues/223 | [] | heckad | 3 |
autokey/autokey | automation | 444 | Unable to have AutoKey insert an em dash (—) | ## Classification: Bug
## Reproducibility: Always
## Version
AutoKey version: 0.95.10
Used GUI (Gtk, Qt, or both): Gtk
Linux Distribution: Mint
## Summary
Unable to have AutoKey insert an em dash (—)
## Steps to Reproduce (if applicable)
I create a simple phrase consisting entirely of the em dash character (—). I assign it to a hotkey (I tried several), e.g., <ctrl>+<shift>+- or <ctrl>+<f9>
## Expected Results
Pressing the hotkey would insert/type an em dash.
## Actual Results
Nothing happens. In some programs it blinks for a moment, but the character never appears.
## Notes
Substituting other text for the em dash, such as random characters, works just fine — the hotkey sends those characters as expected. It's only the em dash I've been unable to get to work.
| closed | 2020-09-23T15:42:32Z | 2022-06-23T07:55:18Z | https://github.com/autokey/autokey/issues/444 | [
"duplicate",
"wontfix"
] | xebico | 3 |
521xueweihan/HelloGitHub | python | 1,968 | 自荐项目 | Fantastic-admin - 一款开箱即用的 Vue 中后台管理系统框架 | ## 项目推荐
- 项目地址：https://github.com/hooray/fantastic-admin
- 类别：JS
- 项目后续更新计划：持续产出业务相关静态页面和 Vue3 版本视频教程
- 项目描述：
- 丰富的布局模式，覆盖市面上各种中后台布局，兼容PC、平板和移动端
- 提供主题配置文件，轻松实现个性化定制
- 精心设计的动效，让每一处的动画都干净利落
- 根据路由配置自动生成导航栏
- 支持全方位权限验证
- 集成多级路由缓存方案
- 推荐理由：不管是前端还是后端开发者，都可以快速上手
- 截图：
 | closed | 2021-11-15T00:56:34Z | 2021-11-25T01:47:33Z | https://github.com/521xueweihan/HelloGitHub/issues/1968 | [
"JavaScript 项目"
] | hooray | 1 |
sourcery-ai/python-best-practices-cookiecutter | pytest | 18 | Update Version of Black | ## Description
This **cookiecutter** template will generate a repository that uses `black v20.8b1`.
According to this [issue](https://github.com/psf/black/issues/2964), this version of `black` is not compatible with newer versions of `click` and will throw an error upon execution of `black`.
## Recommendation
Updating the default `black` version to be `22.3.0` for this template.
| open | 2022-04-28T13:00:22Z | 2022-12-31T19:11:50Z | https://github.com/sourcery-ai/python-best-practices-cookiecutter/issues/18 | [] | rawrke | 1 |
aminalaee/sqladmin | fastapi | 854 | Integrate Pydantic models with sqladmin for simplified column and form management | ### Checklist
- [X] There are no similar issues or pull requests for this yet.
### Is your feature related to a problem? Please describe.
Currently, sqladmin requires explicit lists of columns (e.g. `column_list`, `column_details_list`, `form_columns`) to be specified in each `ModelView` configuration. I want to be able to pass Pydantic models instead of a list of Sqlalchmey model columns. currently, I'm doing this using my utility functions which create a list of Model columns based on Pydantic models. but it would be nice if it were a library feature.
### Describe the solution you would like.
1. Allow `ModelView` to accept Pydantic models directly:
Modify `ModelView` to accept Pydantic models as an alternative input to the existing `column_list`, `column_details_list`, and other columns and form attributes.
```python
class Product(Base):
__tablename__ = "user"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str]
price: Mapped[float]
created_at: Mapped[datetime]
class ProductCreate(BaseModel):
name: str
price: str
class ProductRead(BaseModel):
id: int
name: str
price: float
created_at: datetime
class ProductList(BaseModel):
id: int
name: str
price: float
class ProductAdmin(ModelView, model=Product):
columns_list = ProductList
columns_create = ProductCreate
columns_read = ProductRead
```
2. auto-populate column attributes based on Pydantic models:
when Pydantic models are provided, sqladmin should dynamically inspect the Pydantic model's fields to automatically populate the columns for `column_list`, `column_details_list`, and other columns and form attributes behind the scenes.
SQL would need a mapping utility to align Pydantic model fields with their corresponding Sqlalchmey model columns. something like this:
```python
def get_cols_and_rels(
model: type["Base"],
schema: type["BaseSchema"],
exclude: List[str] | None = None
):
fields = schema.model_fields.copy()
if exclude is not None:
for key in exclude:
fields.pop(key)
cols_and_rels = {
k: v
for k, v in model.__dict__.items()
if isinstance(v, InstrumentedAttribute)
}
return [v for k, v in cols_and_rels.items() if k in fields]
```
this feature would streamline the `ModelView `configuration, reduce redundancy, and improve model consistency.
### Describe alternatives you considered
_No response_
### Additional context
_No response_ | closed | 2024-11-05T10:49:09Z | 2024-12-02T11:35:32Z | https://github.com/aminalaee/sqladmin/issues/854 | [] | nimaxin | 0 |
junyanz/pytorch-CycleGAN-and-pix2pix | pytorch | 1,303 | AssertionError: ./datasets/maps\trainA is not a valid directory | i have error too, after i run this"python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan", the cmd show me "Traceback (most recent call last):
File "train.py", line 29, in
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
File "C:\Users\USER\Downloads\pytorch-CycleGAN-and-pix2pix-master\pytorch-CycleGAN-and-pix2pix-master\data_init_.py", line 57, in create_dataset
data_loader = CustomDatasetDataLoader(opt)
File "C:\Users\USER\Downloads\pytorch-CycleGAN-and-pix2pix-master\pytorch-CycleGAN-and-pix2pix-master\data_init_.py", line 73, in init
self.dataset = dataset_class(opt)
File "C:\Users\USER\Downloads\pytorch-CycleGAN-and-pix2pix-master\pytorch-CycleGAN-and-pix2pix-master\data\unaligned_dataset.py", line 29, in init
self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
File "C:\Users\USER\Downloads\pytorch-CycleGAN-and-pix2pix-master\pytorch-CycleGAN-and-pix2pix-master\data\image_folder.py", line 25, in make_dataset
assert os.path.isdir(dir), '%s is not a valid directory' % dir
AssertionError: ./datasets/maps\trainA is not a valid directory"
Can someone help me "AssertionError: ./datasets/maps\trainA is not a valid directory" means what? | closed | 2021-07-26T13:36:47Z | 2023-06-28T02:54:00Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1303 | [] | ChenShunHsu | 3 |
wkentaro/labelme | deep-learning | 1,233 | [Windows 10] labelme gui not opening | ### Provide environment information
python : 3.10.9
```
conda create --name=labelme python=3
conda activate labelme
pip install labelme
```
### What OS are you using?
Windows 10
### Describe the Bug
Please see in the taskbar, the white app icon is the the labelme. It just apears like this and no log message in the terminal and no gui.
### Expected Behavior
_No response_
### To Reproduce
_No response_ | closed | 2023-02-05T15:37:17Z | 2024-05-17T06:20:01Z | https://github.com/wkentaro/labelme/issues/1233 | [
"issue::bug"
] | innat | 1 |
keras-team/keras | pytorch | 20,722 | Is it possible to use tf.data with tf operations while utilizing jax or torch as the backend? | Apart from tensorflow as backend, what are the proper approach to use basic operatons (i.e. tf.concat) inside the tf.data API pipelines? The following code works with tensorflow backend, but not with torch or jax.
```python
import os
os.environ["KERAS_BACKEND"] = "jax" # tensorflow, torch, jax
import keras
from keras import layers
import tensorflow as tf
aug_model = keras.Sequential([
keras.Input(shape=(224, 224, 3)),
layers.RandomFlip("horizontal_and_vertical")
])
def augment_data_tf(x, y):
combined = tf.concat([x, y], axis=-1)
z = aug_model(combined)
x = z[..., :3]
y = z[..., 3:]
return x, y
a = np.ones((4, 224, 224, 3)).astype(np.float32)
b = np.ones((4, 224, 224, 2)).astype(np.float32)
dataset = tf.data.Dataset.from_tensor_slices((a, b))
dataset = dataset.batch(3, drop_remainder=True)
dataset = dataset.map(
augment_data_tf, num_parallel_calls=tf.data.AUTOTUNE
)
```
```bash
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
[<ipython-input-7-2d25b0c0bbad>](https://localhost:8080/#) in <cell line: 3>()
1 dataset = tf.data.Dataset.from_tensor_slices((a, b))
2 dataset = dataset.batch(3, drop_remainder=True)
----> 3 dataset = dataset.map(
4 augment_data_tf, num_parallel_calls=tf.data.AUTOTUNE
5 )
25 frames
[/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py](https://localhost:8080/#) in _convert_to_array_if_dtype_fails(x)
4102 dtypes.dtype(x)
4103 except TypeError:
-> 4104 return np.asarray(x)
4105 else:
4106 return x
NotImplementedError: in user code:
File "<ipython-input-5-ca4b074b58a5>", line 6, in augment_data_tf *
z = aug_model(combined)
File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler **
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.10/dist-packages/optree/ops.py", line 752, in tree_map
return treespec.unflatten(map(func, *flat_args))
File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 4252, in asarray
return array(a, dtype=dtype, copy=bool(copy), order=order, device=device)
File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 4058, in array
leaves = [_convert_to_array_if_dtype_fails(leaf) for leaf in leaves]
File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 4058, in <listcomp>
leaves = [_convert_to_array_if_dtype_fails(leaf) for leaf in leaves]
File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 4104, in _convert_to_array_if_dtype_fails
return np.asarray(x)
NotImplementedError: Cannot convert a symbolic tf.Tensor (concat:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.
``` | closed | 2025-01-03T19:44:45Z | 2025-01-04T22:44:43Z | https://github.com/keras-team/keras/issues/20722 | [] | innat | 1 |
eriklindernoren/ML-From-Scratch | machine-learning | 66 | mnist = fetch_mldata('MNIST original') | ```
URLError: <urlopen error [Errno 110] Connection timed out>
```
mldata.org appears to be down.
Please see:
https://github.com/scikit-learn/scikit-learn/issues/8588 | open | 2019-11-27T17:45:38Z | 2020-11-14T04:39:45Z | https://github.com/eriklindernoren/ML-From-Scratch/issues/66 | [] | dbl001 | 1 |
gradio-app/gradio | machine-learning | 10,437 | Create function to call current Session variable | * [x] **I have searched to see if a similar issue already exists.**
**Is your feature request related to a problem? Please describe.**
In php I just need to call the following code to get current session ID, and it persistence across all pages.
```php
<?php
session_start();
echo session_id();
```
but there is no similar function in Gradio, I found the closest way yo get session id/hash is using `gradio.Request.session_hash` but the problem it only work with event listener and single tab.
**Describe the solution you'd like**
Expose `gradio.Request.session_hash` as global variable and/or Create function to get Session variable like in PHP
so instead of
```
import gradio as gr
def get_session(text, request: gr.Request):
return request.session_hash
io = gr.Interface(get_session, "textbox", "textbox").launch()
```
I will use like
```python
import gradio as gr
from gradio.Request import session_id
def get_session(tex):
# call it anywhere without event listener
return session_id()
io = gr.Interface(get_session, "textbox", "textbox").launch()
```
**Additional context**
| closed | 2025-01-26T06:55:32Z | 2025-01-26T07:19:36Z | https://github.com/gradio-app/gradio/issues/10437 | [] | ewwink | 1 |
ray-project/ray | tensorflow | 50,656 | [Core] Plugable storage backend besides Redis | ### Description
Redis as metadata storage backend has it only limitation, e.g. it can only guarantee eventual consistency instead of strong consistency.
It would be nice to be able to extend storage backend for the following reasons, as far as I can see.
1. uses prefer availability or consistency than performance
2. better cache engine than Redis
### Use case
_No response_ | open | 2025-02-17T03:15:30Z | 2025-03-22T00:55:10Z | https://github.com/ray-project/ray/issues/50656 | [
"enhancement",
"P2",
"core"
] | zhengy001 | 1 |
iperov/DeepFaceLab | machine-learning | 527 | settings for highend workstations | Hi :)
i found 2 settings (converter and extraction) which doubles the speed on my system.
could u do a list which settings i can change to use the fuul power?
one i found was the used cores in the converter file and the extraction file.
Converting increased from 2.5 it/s to over 5 it/s.
I know, u have to set the settings for common systems. but it scale not as it should :/
My sys: 32core and 64gb ram, 2x rtx 2080ti.
Greets and thanks for any inputs :) | closed | 2019-12-23T13:44:28Z | 2020-03-10T11:44:23Z | https://github.com/iperov/DeepFaceLab/issues/527 | [] | blanuk | 1 |
plotly/dash-table | plotly | 661 | [BUG] Editable table with dropdown presentation breaks with virtualization | Hi there,
similar to [https://community.plot.ly/t/dash-data-table-virtualization-completely-broken-with-an-editable-table/28565](url) (also here #583 and solved here #584 ), but now with dropdown in editable cell. An example:
```python
app = dash.Dash(__name__, external_stylesheets=['https://codepen.io/chriddyp/pen/bWLwgP.css'])
app.layout = html.Div([
dash_table.DataTable(
id='table',
columns=(
[{'id': 'col1', 'name': 'col1', 'editable': False}] +
[{'id': 'col2', 'name': 'col2', 'type': 'numeric', 'presentation':'dropdown'}]
),
data=[
{'col1': i, 'col2': i}
for i in range(100)
],
style_table={
'height': 240,
'width': 290,
'overflowY': 'auto'
},
style_cell_conditional=[
{'if': {'column_id': 'col1'},
'width': '55%'},
{'if': {'column_id': 'col2'},
'width': '45%'}
],
dropdown={'col2':{'options':[{'label':str(i),'value':i} for i in range(100)]}},
editable=True,
page_action='none',
virtualization=True
)
]
)
app.run_server(debug=True)
```
Editing the first few rows works fine. Scrolling down and editing some other row throws the following error:
```
Invalid argument `data` passed into DataTable with ID "table".
Expected an array.
Was supplied type `object`.
```
I have dash v1.4.1, dash-core-components v1.3.1, dash-renderer 1.1.2 and dash-table v4.4.1 installed and Chrome 78.0.3904.108 (Official Build) (64-bit).
Solvable bug?
Thanks for the great work!
| open | 2019-12-11T22:40:12Z | 2021-08-02T19:57:32Z | https://github.com/plotly/dash-table/issues/661 | [] | mirkomiorelli | 2 |
iterative/dvc | data-science | 10,363 | datasets: delta lake and huggingface | Following up on https://github.com/iterative/dvc/issues/10313 and related new features specifying `datasets` as dependencies, we can add more types of supported datasets:
- [delta lake](https://iterativeai.slack.com/archives/CB41NAL8H/p1709267375350649)
- hugging face
This could allow for setting these types of datasets as dependencies tracked by dvc using their own native versioning without downloading or caching anything.
Delta Lake example:
```python
from dvc.api import get_dataset
ds_info = get_dataset("mytable")
df = spark.read.format("delta").option("timestampAsOf", ds_info["timestamp"]).table(ds_info["name"])
```
Hugging Face example:
```python
from dvc.api import get_dataset
ds_info = get_dataset("mydataset")
dataset = load_dataset(ds_info["name"], rev=ds_info["rev"])
``` | open | 2024-03-19T19:51:59Z | 2024-03-21T14:22:42Z | https://github.com/iterative/dvc/issues/10363 | [
"feature request",
"p2-medium",
"A: data-management"
] | dberenbaum | 1 |
jupyterlab/jupyter-ai | jupyter | 1,010 | add VLLM support? | ### Problem/Solution
It has been great to see Ollama added as a first-class option in https://github.com/jupyterlab/jupyter-ai/pull/646, this has made it easy to access a huge variety of models and has been working very well us.
I increasingly see groups and university providers using [VLLM](https://github.com/vllm-project/vllm) for this as well. I'm out of my depth but I understand VLLM is considered better suited when a group is serving a local model to multiple users (e.g. from a local GPU cluster, rather than everyone running an independent Ollama). It gets passing mention in some threads here as well. I think supporting more providers is all to the good and would love to see support for this as a backend similar to the existing Ollama support, though maybe I'm not understanding the details and that is unnecessary? (i.e. it looks like it might be possible to simply use the OpenAI configuration with alternative endpoint to access a VLLM server?)
| closed | 2024-09-22T04:38:50Z | 2025-03-02T01:37:22Z | https://github.com/jupyterlab/jupyter-ai/issues/1010 | [
"enhancement"
] | cboettig | 17 |
firerpa/lamda | automation | 101 | 抓包的话没有lamda这个模块 | E:\Git\CodeHome\lamda\tools>python -u startmitm.py 172.16.1.15
Traceback (most recent call last):
File "E:\Git\CodeHome\lamda\tools\startmitm.py", line 34, in <module>
from lamda import __version__
ModuleNotFoundError: No module named 'lamda'
E:\Git\CodeHome\lamda\tools> | open | 2025-01-12T13:42:47Z | 2025-02-18T08:46:56Z | https://github.com/firerpa/lamda/issues/101 | [] | Linrupin | 3 |
horovod/horovod | tensorflow | 4,131 | imsham reham lekid videos | **Environment:**
1. Framework: (TensorFlow, Keras, PyTorch, MXNet)
2. Framework version:
3. Horovod version:
4. MPI version:
5. CUDA version:
6. NCCL version:
7. Python version:
8. Spark / PySpark version:
9. Ray version:
10. OS and version:
11. GCC version:
12. CMake version:
**Checklist:**
1. Did you search issues to find if somebody asked this question before?
2. If your question is about hang, did you read [this doc](https://github.com/horovod/horovod/blob/master/docs/running.rst)?
3. If your question is about docker, did you read [this doc](https://github.com/horovod/horovod/blob/master/docs/docker.rst)?
4. Did you check if you question is answered in the [troubleshooting guide](https://github.com/horovod/horovod/blob/master/docs/troubleshooting.rst)?
**Bug report:**
Please describe erroneous behavior you're observing and steps to reproduce it.
| closed | 2024-11-18T23:34:44Z | 2024-11-20T12:23:50Z | https://github.com/horovod/horovod/issues/4131 | [
"bug"
] | mudasirbrohi | 0 |
ets-labs/python-dependency-injector | asyncio | 760 | Custom ResourceFactory provider help | Hi! I'm trying to handle SQLAlchemy sessions as a resource provider instead of using FastAPI's built-in `Depends`.
Let me share some code:
The database class:
```python
class Database:
"""Class to handle database connections and sessions"""
def __init__(self, db_url: str, **kwargs):
"""Initialize the Database class with a database URL and optional keyword arguments"""
self.db_url = db_url
self.engine = create_engine(self.db_url, **kwargs, echo=settings.DEBUG_MODE, connect_args={"check_same_thread": False}, pool_size=0)
self.local_session = sessionmaker(autocommit=False, autoflush=False, bind=self.engine)
@contextmanager
def session(self):
"""Context manager to handle sessions"""
session: Session = self.local_session()
try:
yield session
except Exception as e:
session.rollback()
finally:
session.close()
```
The main container where I create these resources:
```python
def get_db_session(session_factory):
with session_factory() as db:
yield db
class AppContainer(containers.DeclarativeContainer):
"""Container to serve all the containers related to the app"""
# ..... other injections
# Setup container for talent services
db = providers.Factory(Database, db_url=settings.DB_URL)
db_sess = providers.Resource(get_db_session, session_factory=db.provided.session)
user_container = providers.Container(
UserContainer, # and this container has "UserService" that also uses the db session dependency
db_sess=db_sess,
)
# ....
```
So, I inject the session into the services directly (or in the future in one repository). I don't like injecting the session into the routers directly that's why I prefer not using the FastAPI `Depends`.
The code provided works as expected, it creates 1 session per request and then closes it, the thing is that this doesn't work in a concurrent environment, I made some local testing with locust, and once I start having many requests executing with multi-threads (I'm using `sync` endpoints), the sessions don't work as expected, it seems that the same session is being used in more than 1 request at the same time, so I guess this is something that we could solve if we had something like `FactoryResource` instead of just a `Resource` that works as a `Singleton`. I tried to implement this myself by creating a `CustomProvider` but I couldn't make it, so if anyone has any advice I'd appreciate it.
P.S: I checked all the related issues regarding FastAPI and SQLAlchemy sessions in this repo and tried some approaches but none of them worked correctly
| open | 2023-11-01T21:19:15Z | 2025-01-07T16:23:39Z | https://github.com/ets-labs/python-dependency-injector/issues/760 | [] | agusmdev | 7 |
jpadilla/django-rest-framework-jwt | django | 307 | How can I add user data to the Response on login? | I'm trying to add information from the user model to the Response that is returns when a user logs-in with their username and password. Right now the `rest_framework_jwt.views.obtain_jwt_token` only returns the token. I've considered subclassing `ObtainJSONWebToken`, in an effort to add the user data by overriding the `post` method. Is this the recommended approach? | closed | 2017-02-12T01:31:50Z | 2017-02-13T13:31:18Z | https://github.com/jpadilla/django-rest-framework-jwt/issues/307 | [] | JadSayegh | 2 |
widgetti/solara | fastapi | 850 | Inconsistent issue when removing a split map control from an ipyleaflet map | I am facing a very difficult issue to trace, so I came up with this [almost reproducible example](https://py.cafe/app/lopezv.oliver/solara-issue-850).
This animation shows the demo initially working as expected. Then, I refresh the app and there is an issue with the demo: the split-map control does not get removed correctly.
## Setup
Let's start with defining a custom class inheriting from ipyleaflet.Map that can dynamically change between a split map and a "stack" map (layers stacked on top of each other).
```python
import ipyleaflet
import traitlets
from traitlets import observe
class Map(ipyleaflet.Map,):
map_type = traitlets.Unicode().tag(sync=True)
@observe("map_type")
def _on_map_type_change(self, change):
if hasattr(self, "split_map_control"):
if change.new=="stack":
self.remove(self.split_map_control)
self.set_stack_mode()
if change.new=="split":
self.set_split_mode()
def set_stack_mode(self):
self.layers = tuple([
self.esri_layer,
self.topo_layer
])
def set_split_mode(self):
self.layers = ()
self.add(self.left_layer)
self.add(self.right_layer)
self.add(self.split_map_control)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.osm = self.layers[0]
esri_url=ipyleaflet.basemaps.Esri.WorldImagery.build_url()
topo_url = ipyleaflet.basemaps.OpenTopoMap.build_url()
self.left_layer = ipyleaflet.TileLayer(url = topo_url, name="left")
self.right_layer = ipyleaflet.TileLayer(url = esri_url, name="right")
self.topo_layer = ipyleaflet.TileLayer(url=topo_url, name="topo", opacity=0.25)
self.esri_layer = ipyleaflet.TileLayer(url=esri_url, name="esri")
self.stack_layers = [
self.esri_layer,
self.topo_layer,
]
self.split_map_control = ipyleaflet.SplitMapControl(
left_layer=self.left_layer,
right_layer=self.right_layer)
if self.map_type=="split":
self.set_split_mode()
if self.map_type=="stack":
self.set_stack_mode()
```
I haven't encountered the issue when testing the ipyleaflet code without solara.
Now let's add solara to the equation:
```python
import solara
import ipyleaflet
import traitlets
from traitlets import observe
zoom=solara.reactive(4)
map_type = solara.reactive("stack")
class Map(ipyleaflet.Map,):
.... (same code defining Map as above)
....
@solara.component
def Page():
with solara.ToggleButtonsSingle(value=map_type):
solara.Button("Stack", icon_name="mdi-layers-triple", value="stack", text=True)
solara.Button("Split", icon_name="mdi-arrow-split-vertical", value="split", text=True)
Map.element(
zoom=zoom.value,
on_zoom=zoom.set,
map_type=map_type.value
)
Page()
```
Could you please help diagnose this problem?
> Here's a [live version of an app](https://halo-maps.kaust.edu.sa/biodiversity) where I am facing this issue (also inconsistently! try refreshing until you encounter the issue). | open | 2024-11-05T07:01:22Z | 2024-11-17T14:08:02Z | https://github.com/widgetti/solara/issues/850 | [] | lopezvoliver | 2 |
ionelmc/pytest-benchmark | pytest | 192 | side by side compare tables when doing parameterized benchmarks | in zokusei i compare different implementations of features using pytest-benchmark
with the standard output the long lists don't provide much of what iwant,
and id much rather see compact tables that pick specific values and line them up between test name and parameter value
a initial example is posted on https://gist.github.com/RonnyPfannschmidt/b0cd7a497bdfc69c1c72d8610acc4230
after some coordination with @ionelmc i'm happy to ustream this at a later point and will create a poc implementation in a conftest for zokusei as a starter | open | 2021-02-14T20:16:51Z | 2021-04-22T20:07:47Z | https://github.com/ionelmc/pytest-benchmark/issues/192 | [] | RonnyPfannschmidt | 9 |
iterative/dvc | data-science | 9,700 | Binary installers no longer attached to Github releases | # Bug Report
## Binaries no longer attached to release
## Description
For all releases until dvc [v3.1.0](https://github.com/iterative/dvc/releases/tag/3.1.0), the tagged release on Github had binary installers attached as assets:
For releases since then ([v3.2.0](https://github.com/iterative/dvc/releases/tag/3.2.0) onward), no binary assets are attached:
### Reproduce
View [dvc releases page](https://github.com/iterative/dvc/releases/).
### Expected
I hope this regression was unintentional, and there will be interest in restoring the versioned binary installers associated with each release.
In my own experience, these installers are very useful for CI/CD environments. It is helpful to pin to a specific version and test upgrades before deploying. I'm sure other users have other uses as well. | closed | 2023-07-04T13:34:39Z | 2023-07-04T23:28:07Z | https://github.com/iterative/dvc/issues/9700 | [] | michaelstepner | 8 |
holoviz/colorcet | plotly | 67 | color palette name is not recognized | Thanks for contacting us! Please read and follow these instructions carefully, then delete this introductory text to keep your issue easy to read. Note that the issue tracker is NOT the place for usage questions and technical assistance; post those at [Discourse](https://discourse.holoviz.org) instead. Issues without the required information below may be closed immediately.
#### ALL software version info
(this library, plus any other relevant software, e.g. bokeh, python, notebook, OS, browser, etc)
python 3.6.5
matplotlib 3.2.2
colorcet 2.0.6
#### Description of expected behavior and the observed behavior
I want to generate a 3d scatter plot using a rainbow color palette from colorcet package
#### Complete, minimal, self-contained example code that reproduces the issue
< code to generate a 3d data set>
fig= plt.figure(figsize= (10,10))
ax= plt.axes(projection= '3d')
ax.grid(b = True, color ='grey', linestyle ='-.', linewidth = 0.3, alpha = 0.2)
map1= ax.scatter(clusterable_embedding[:,0], clusterable_embedding[:,1], clusterable_embedding[:,2],
c= num_points, cmap= cc.cm.rainbow_bgyr_35_85_c72, alpha= 0.7)
ax.set_title('n_neighbors 10 min_dist 0.1 spread 1.0 learning rate 1.0')
ax.set_xlabel('X-axis', fontweight ='bold')
ax.set_ylabel('Y-axis', fontweight ='bold')
ax.set_zlabel('Z-axis', fontweight ='bold')
fig.colorbar(map1, ax= ax, shrink= 0.5, aspect= 5)
plt.show()
```
# code goes here between backticks
```
#### Stack traceback and/or browser JavaScript console output
Traceback (most recent call last):
File "test_umap_hdbscan.py", line 130, in <module>
c= num_points, cmap= cc.rainbow_bgyr_35_85_c72, alpha= 0.7)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\mpl_toolkits\mplot3d\axes3d.py", line 2239, in scatter
patches = super().scatter(xs, ys, s=s, c=c, *args, **kwargs)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\matplotlib\__init__.py", line 1565, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\matplotlib\cbook\deprecation.py", line 358, in wrapper
return func(*args, **kwargs)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\matplotlib\axes\_axes.py", line 4447, in scatter
collection.set_cmap(cmap)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\matplotlib\cm.py", line 313, in set_cmap
cmap = get_cmap(cmap)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\matplotlib\cm.py", line 134, in get_cmap
cbook._check_in_list(sorted(cmap_d), name=name)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\matplotlib\cbook\__init__.py", line 2145, in _check_in_list
.format(v, k, ', '.join(map(repr, values))))
ValueError: [[0, 0.20387, 0.96251], [0, 0.21524, 0.9514], [0, 0.22613, 0.94031], [0, 0.23654, 0.92923], [0, 0.24654, 0.91817], [0, 0.2562, 0.90712], [0, 0.26557, 0.89608], [0, 0.27465, 0.88506], [0, 0.28348, 0.87405], [0, 0.29209, 0.86305], [0, 0.30047, 0.85206], [0, 0.3087, 0.84109], [0, 0.31672, 0.83013], [0, 0.32458, 0.81917], [0, 0.33232, 0.80823], [0, 0.3399, 0.7973]
.....
is not a valid value for name; supported values are 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r',
....
#### Screenshots or screencasts of the bug in action
Note that I also tried this command:
map1= ax.scatter(clusterable_embedding[:,0], clusterable_embedding[:,1], clusterable_embedding[:,2],
c= num_points, cmap= cc.rainbow_bgyr_35_85_c72, alpha= 0.7)
and I still got the same problem
Many thanks,
Ivan | closed | 2021-05-11T21:09:40Z | 2021-05-12T13:33:14Z | https://github.com/holoviz/colorcet/issues/67 | [] | ivan-marroquin | 3 |
youfou/wxpy | api | 384 | 发送的文件中含中文字符报错 | os检查文件存在，只是文件名含有中文字符，最后发送失败，err_code是1，请问如何解决？ | open | 2019-05-13T14:30:36Z | 2019-06-23T10:07:50Z | https://github.com/youfou/wxpy/issues/384 | [] | Nicolecnu | 4 |
erdewit/ib_insync | asyncio | 693 | Getting Error 478 on options expiring in Feb - leap year. | I have some option positions in my portfolio and extract it using the following function:
```python
def quick_pf(ib):
pf = ib.portfolio()
if pf != []:
df_pf = util.df(pf)
df_pf = df_pf.assign(conttract=df_pf.contract)
else:
df_pf = None
return df_pf
```
when an option contract in df_pf is qualified using `ib.qualifyContracts`, it works fine and gives me, like this one below:
`Contract(secType='OPT', conId=679139121, symbol='ABNB', lastTradeDateOrContractMonth='20240217', strike=124.0, right='P', multiplier='100', exchange='SMART')`. Note that the expiry date is wrong. ref: [this yahoo link](https://finance.yahoo.com/quote/ABNB240216P00127000)
... but when I remove the `conId` , the qualifyContracts fails and it gives me an `Unknown contracts` error.
... running the wrong expiry dated conId contract through `ib.whatIfOrderAsync(wrong_expiry_dt_contract, Order('SELL', 100))`
throws the undocumented Error 478 exception:
`Parameters in request conflicts with contract parameters received by contract id: requested expiry 20240217, in contract 20240216;`
I don't get the error in options expiring in March 2024.
1. Is this error in the portfolio due to leap-year?
2. Can ib.qualifyContracts / ib.qualifyContractsAsync be tightened to replace the lastTradeDateOrContractMonth with correct expiry, if Contract(conId=679139121, lastTradeDateOrContractMonth='20240217') is given? | closed | 2024-02-13T13:19:02Z | 2024-02-16T12:14:36Z | https://github.com/erdewit/ib_insync/issues/693 | [] | reservoirinvest | 1 |
yt-dlp/yt-dlp | python | 12,131 | lower bitrate YT m4a audio formats disappeared in 2025.01.12 | ### DO NOT REMOVE OR SKIP THE ISSUE TEMPLATE
- [x] I understand that I will be **blocked** if I *intentionally* remove or skip any mandatory\* field
### Checklist
- [x] I'm reporting that yt-dlp is broken on a **supported** site
- [x] I've verified that I have **updated yt-dlp to nightly or master** ([update instructions](https://github.com/yt-dlp/yt-dlp#update-channels))
- [x] I've checked that all provided URLs are playable in a browser with the same IP and same login details
- [x] I've checked that all URLs and arguments with special characters are [properly quoted or escaped](https://github.com/yt-dlp/yt-dlp/wiki/FAQ#video-url-contains-an-ampersand--and-im-getting-some-strange-output-1-2839-or-v-is-not-recognized-as-an-internal-or-external-command)
- [x] I've searched [known issues](https://github.com/yt-dlp/yt-dlp/issues/3766) and the [bugtracker](https://github.com/yt-dlp/yt-dlp/issues?q=) for similar issues **including closed ones**. DO NOT post duplicates
- [x] I've read the [guidelines for opening an issue](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#opening-an-issue)
- [x] I've read about [sharing account credentials](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#are-you-willing-to-share-account-details-if-needed) and I'm willing to share it if required
### Region
US
### Provide a description that is worded well enough to be understood
`yt-dlp_2024.12.23.exe -F skyiiabNBQk`
```
ID EXT RESOLUTION FPS CH │ FILESIZE TBR PROTO │ VCODEC VBR ACODEC ABR ASR MORE INFO
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
sb3 mhtml 48x27 0 │ mhtml │ images storyboard
sb2 mhtml 80x45 0 │ mhtml │ images storyboard
sb1 mhtml 160x90 0 │ mhtml │ images storyboard
sb0 mhtml 320x180 0 │ mhtml │ images storyboard
233 mp4 audio only │ m3u8 │ audio only unknown Default, IOS
234 mp4 audio only │ m3u8 │ audio only unknown Default, IOS
599-drc m4a audio only 2 │ 4.12MiB 31k https │ audio only mp4a.40.5 31k 22k ultralow, DRC, MWEB, m4a_dash
600-drc webm audio only 2 │ 4.80MiB 36k https │ audio only opus 36k 48k ultralow, DRC, MWEB, webm_dash
599 m4a audio only 2 │ 4.12MiB 31k https │ audio only mp4a.40.5 31k 22k ultralow, MWEB, m4a_dash
600 webm audio only 2 │ 4.78MiB 36k https │ audio only opus 36k 48k ultralow, MWEB, webm_dash
249-drc webm audio only 2 │ 7.07MiB 53k https │ audio only opus 53k 48k low, DRC, MWEB, webm_dash
250-drc webm audio only 2 │ 9.24MiB 69k https │ audio only opus 69k 48k low, DRC, MWEB, webm_dash
249 webm audio only 2 │ 7.05MiB 53k https │ audio only opus 53k 48k low, MWEB, webm_dash
250 webm audio only 2 │ 9.23MiB 69k https │ audio only opus 69k 48k low, MWEB, webm_dash
140-drc m4a audio only 2 │ 17.31MiB 129k https │ audio only mp4a.40.2 129k 44k medium, DRC, MWEB, m4a_dash
251-drc webm audio only 2 │ 18.11MiB 135k https │ audio only opus 135k 48k medium, DRC, MWEB, webm_dash
140 m4a audio only 2 │ 17.31MiB 129k https │ audio only mp4a.40.2 129k 44k medium, MWEB, m4a_dash
251 webm audio only 2 │ 18.10MiB 135k https │ audio only opus 135k 48k medium, MWEB, webm_dash
```
`yt-dlp_2025.01.12.exe -F skyiiabNBQk`
```
ID EXT RESOLUTION FPS CH │ FILESIZE TBR PROTO │ VCODEC VBR ACODEC ABR ASR MORE INFO
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
sb3 mhtml 48x27 0 │ mhtml │ images storyboard
sb2 mhtml 80x45 0 │ mhtml │ images storyboard
sb1 mhtml 160x90 0 │ mhtml │ images storyboard
sb0 mhtml 320x180 0 │ mhtml │ images storyboard
233 mp4 audio only │ m3u8 │ audio only unknown Default
234 mp4 audio only │ m3u8 │ audio only unknown Default
249-drc webm audio only 2 │ 7.07MiB 53k https │ audio only opus 53k 48k low, DRC, webm_dash
250-drc webm audio only 2 │ 9.24MiB 69k https │ audio only opus 69k 48k low, DRC, webm_dash
249 webm audio only 2 │ 7.05MiB 53k https │ audio only opus 53k 48k low, webm_dash
250 webm audio only 2 │ 9.23MiB 69k https │ audio only opus 69k 48k low, webm_dash
140-drc m4a audio only 2 │ 17.31MiB 129k https │ audio only mp4a.40.2 129k 44k medium, DRC, m4a_dash
251-drc webm audio only 2 │ 18.11MiB 135k https │ audio only opus 135k 48k medium, DRC, webm_dash
140 m4a audio only 2 │ 17.31MiB 129k https │ audio only mp4a.40.2 129k 44k medium, m4a_dash
251 webm audio only 2 │ 18.10MiB 135k https │ audio only opus 135k 48k medium, webm_dash
```
For m4a everything below 129K is gone. This example doesn't show 48K which some videos have, but I think its also gone. I wasn't paying attention to opus initially but it looks like it's also affected.
### Provide verbose output that clearly demonstrates the problem
- [x] Run **your** yt-dlp command with **-vU** flag added (`yt-dlp -vU <your command line>`)
- [ ] If using API, add `'verbose': True` to `YoutubeDL` params instead
- [x] Copy the WHOLE output (starting with `[debug] Command-line config`) and insert it below
### Complete Verbose Output
```shell
N/A
``` | closed | 2025-01-19T05:00:11Z | 2025-01-19T05:18:21Z | https://github.com/yt-dlp/yt-dlp/issues/12131 | [
"question",
"site:youtube"
] | bughit | 3 |
jupyter/nbgrader | jupyter | 1,091 | Error when creating student version of Assignment | Hi,
I'm using nbgrader Version 0.5.5, Jupyterhub 0.9.6, Jupyter Notebook 5.7.8
When trying to create a student-version of an assignment using the formgrader, I get an error.
It looks like this:
Log Output:
>[INFO] Updating/creating assignment 'aufgabe1': {}
[INFO] Converting notebook kurs2/source/./aufgabe1/aufgabe1.ipynb
[ERROR] There was an error processing assignment: kurs2/source/./aufgabe1
[ERROR] Traceback (most recent call last):
File "/opt/tljh/user/lib/python3.6/site-packages/nbgrader/converters/base.py", line 293, in convert_notebooks
self.convert_single_notebook(notebook_filename)
File "/opt/tljh/user/lib/python3.6/site-packages/nbgrader/converters/base.py", line 249, in convert_single_notebook
output, resources = self.exporter.from_filename(notebook_filename, resources=resources)
File "/opt/tljh/user/lib/python3.6/site-packages/nbconvert/exporters/exporter.py", line 175, in from_filename
modified_date = datetime.datetime.fromtimestamp(os.path.getmtime(filename))
File "/opt/tljh/user/lib/python3.6/genericpath.py", line 55, in getmtime
return os.stat(filename).st_mtime
FileNotFoundError: [Errno 2] No such file or directory: 'kurs2/source/./aufgabe1/aufgabe1.ipynb'
[ERROR] There was an error processing assignment 'aufgabe1' for student '.'
[ERROR] Please see the the above traceback for details on the specific errors on the above failures.
Traceback:
>Traceback (most recent call last):
File "/opt/tljh/user/lib/python3.6/site-packages/nbgrader/utils.py", line 371, in capture_log
app.start()
File "/opt/tljh/user/lib/python3.6/site-packages/nbgrader/converters/assign.py", line 146, in start
super(Assign, self).start()
File "/opt/tljh/user/lib/python3.6/site-packages/nbgrader/converters/base.py", line 72, in start
self.convert_notebooks()
File "/opt/tljh/user/lib/python3.6/site-packages/nbgrader/converters/base.py", line 350, in convert_notebooks
raise NbGraderException(msg)
nbgrader.converters.base.NbGraderException: Please see the the above traceback for details on the specific errors on the above failures.
So these are the exact steps i followed to get to this problem:
1. I set up Jupyterhub exactly like in this tutorial https://tljh.jupyter.org/en/latest/install/amazon.html
2. I installed nbgrader:
- pip install nbgrader --user
- jupyter nbextension install --user --py nbgrader --overwrite
- jupyter nbextension enable --user --py nbgrader
- jupyter serverextension enable --user --py nbgrader
3. Navigate to srv in the terminal
- sudo mkdir nbgrader
- cd nbgrader
- sudo mkdir exchange
- sudo chmod ugo+rw exchange
4. Navigate to ~/.jupyter
5. Create nbgrader_config.py
>c = get_config()
c.CourseDirectory.root = 'kurs2'
6. Create an empty folder "kurs2"
7. Reboot the instance on aws
8. Open the formgrader and add new assignment "aufgabe1"
9. Go to the assignment-folder and upload a notebook that is already finished.
10. Use the formgrader to add a student "12345678", "Max", "Mustermann"
11. Also add this student to Jupyterhub using the control panel
12. Create nbgrader_config.py in ~/kurs2
>c = get_config()
c.Exchange.course_id = "kurs2"
c.Exchange.root = "/srv/nbgrader/exchange"
c.CourseDirectory.db_assignments = [dict(name="aufgabe1")]
c.CourseDirectory.db_students = [dict(id="12345678", first_name="Max", last_name="Muster")]
13. Click on the generate-assignment-button.
And this is where it fails.
Thank you for your time. | closed | 2019-05-27T14:09:09Z | 2019-05-30T14:27:15Z | https://github.com/jupyter/nbgrader/issues/1091 | [
"question"
] | DerFerdi | 4 |
koxudaxi/datamodel-code-generator | fastapi | 2,076 | in python could we sometimes use `default_factory` instead of `default`? | When generating a definitions from a schema, if defaults are used for dictionaries or arrays, **datamodel-code-generator** currently creates models using the `default` keyword for fields. In my opinion, this should really be the `default_factory` keyword.
NOTE: Fortunately, when using **pydantic** the use of `default` as opposed to `default_factory` does not result in the typical issue of the shared instances. However, I am not entirely sure of this is intentional or merely a happy coincidence. If were to switch things to factories, `datamodel-code-generator` would be a tick safer.
## Example ##
Suppose I have a definition
```yaml
openapi: 3.0.3
info:
version: x.y.z
title: My schema
servers:
- url: "https://company.org"
paths: {}
components:
schemas:
Book:
description: |-
A class about books
type: object
required:
- title
properties:
title:
type: string
isbn:
description: |-
The book's ISBN
type: string
default: "0000"
authors:
description: |-
The book author or authors
type: array
items:
type: string
default: []
editors:
description: |-
The editors of the book
type: array
items:
type: string
default: ["nobody"]
additionalProperties: false
```
then, when using **datamodel-code-generator**, I expect that the defaults of lists be treated as _factories_. Instead, when one calls, e.g.
```bash
python3 -m datamodel_code_generator \
--input-file-type openapi \
--output-model-type pydantic_v2.BaseModel \
--encoding "UTF-8" \
--disable-timestamp \
--use-schema-description \
--use-standard-collections \
--collapse-root-models \
--use-default-kwarg \
--field-constraints \
--capitalise-enum-members \
--enum-field-as-literal one \
--set-default-enum-member \
--use-subclass-enum \
--allow-population-by-field-name \
--snake-case-field \
--strict-nullable \
--use-double-quotes \
--target-python-version 3.11 \
--input myschema.yaml \
--output src/mymodels.py
```
one gets
```py
# generated by datamodel-codegen:
# filename: myschema.yaml
from __future__ import annotations
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
"""
A class about books
"""
model_config = ConfigDict(
extra="forbid",
populate_by_name=True,
)
title: str
isbn: str = Field(default="0000", description="The book's ISBN")
authors: list[str] = Field(default=[], description="The book author or authors")
editors: list[str] = Field(default=["nobody"], description="The editors of the book")
```
The **request** here, is that this be changed to:
```py
authors: list[str] = Field(default_factory=list, description="The book author or authors")
editors: list[str] = Field(default_factory=lambda: ["nobody"], description="The editors of the book")
```
## What _could_ go wrong ##
By not using factories, two instances of a class can end up sharing the _same_ attribute. E.g.
```py
class Book:
"""
A class about books
"""
title: str
isbn: str = "0000"
authors: list[str] = []
editors: list[str] = ["nobody"]
book1 = Book()
book1.title = "The Hobbit"
book1.isbn = "9780007440849"
book1.authors.append("Tolkien, JRR")
print(book1.authors) # ["Tolkien, JRR"]
book2 = Book()
book2.title = "Harry Potter and the Philospher's Stone"
book2.isbn = "9781408855652"
book2.authors.append("Rowling, JK")
print(book1.authors) # ["Tolkien, JRR", "Rowling, JK"]
print(book2.authors) # ["Tolkien, JRR", "Rowling, JK"]
```
Note again, that I am not claiming that this happens with the **pydantic** models. In fact, the above does not happen. But, as stated at the start, it is not clear to me whether this be by design (of **pydantic**'s `BaseModel`s/`Field`s) simply a happy coincidence.
## Further context ##
- OS: irrelevant (as same behaviour on linux, OSX, windows)
- Language: `python>=3.11`
- Packages:
- `datamodel-code-generator>=0.25.9`
- `pydantic>=2.8.2`
| open | 2024-08-20T05:50:14Z | 2024-08-23T06:02:16Z | https://github.com/koxudaxi/datamodel-code-generator/issues/2076 | [] | raj-open | 1 |
OFA-Sys/Chinese-CLIP | computer-vision | 99 | 执行bash命令报错 | 您好，我在执行bash命令时报错，具体如下：
![Uploading 1683526387801.png…]()
望您提供解决办法，期待您的回复！
| closed | 2023-05-08T06:15:26Z | 2023-05-08T10:04:03Z | https://github.com/OFA-Sys/Chinese-CLIP/issues/99 | [] | Sally6651 | 2 |
ultralytics/yolov5 | machine-learning | 12,429 | Multi-node multi-GPU training wont run after loading images | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
Hello,
Thank you for taking the time to read my post. I have trying to run training on a supercomputer using torch.distributed.run for a multi-node, multi-GPU setup for over 130,000 images with 1536x2048 resolution. It seems that I am having an issue with the nodes communicating with each other to actually start the training. In this example, I have 2 nodes, 1 GPU-per-node. I use the following bash script with SLURM commands to request the resources necessary for this job:
----------------------------------------------------------------------------------------------------------
#!/bin/bash
#SBATCH --job-name=yolov5_training
#SBATCH --partition=xeon-g6-volta
#SBATCH --output=./jobs/train%A.out
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:volta:1
#SBATCH --exclusive
#Load necessary modules
source /etc/profile
module load anaconda/2023a-pytorch cuda/11.8
srun --nodes=$SLURM_NNODES --ntasks-per-node=$SLURM_NTASKS_PER_NODE bash -c '
#Get the total number of nodes allocated
N=$(scontrol show hostname | wc -l)
#Get the hostname of the current node
current_node=$(hostname)
#Assign node_rank based on the current node
if [ "$current_node" = "$(scontrol show hostnames | head -n1)" ]; then
node_rank=0 # Set node_rank to 0 for the master node
else
# Determine the node rank for non-master nodes
node_rank=$(($(scontrol show hostnames | grep -n "$current_node" | cut -d":" -f1) - 1))
fi
#Print the node_rank for each node
echo "Node $current_node has rank $node_rank"
#Set the master address and port only for the first task (index 0)
if [ $node_rank -eq 0 ]; then
MASTER_ADDR=$(hostname -I)
rm -f shared_ip.sh
echo "export MASTER_ADDR=$MASTER_ADDR" > shared_ip.sh
else
# For other tasks, wait for a short duration to ensure the master has set the variable
sleep 5
fi
#Wait for the master to set the variable
while [ ! -f shared_ip.sh ]; do
sleep 5
done
#Source the shared file to set the MASTER_ADDR variable
source shared_ip.sh
echo "MASTER_ADDR="$MASTER_ADDR
MY_ADDR=$(hostname -I)
echo "MY_ADDRESS="$MY_ADDR
MASTER_PORT=43829
echo python -m torch.distributed.run \
--nproc_per_node $SLURM_NTASKS_PER_NODE \
--nnodes $SLURM_NNODES \
--node_rank $node_rank \
--master_addr "$MASTER_ADDR" \
--master_port $MASTER_PORT \
train.py --data training.yaml --weights yolov5s.pt --img 2048 --project 'runs/train/11-15'
echo "Begin Training: Node "$node_rank
python -m torch.distributed.run \
--nproc_per_node $SLURM_NTASKS_PER_NODE \
--nnodes $SLURM_NNODES \
--node_rank $node_rank \
--master_addr "$MASTER_ADDR" \
--master_port $MASTER_PORT \
train.py --data training.yaml --weights yolov5s.pt --img 2048 --project 'runs/train/11-15'
----------------------------------------------------------------------------------------------------------
In this bash script, I allocate the resources, retrieve the Master Address for the master node and share it with the secondary node. Here are the outputs for each node:
----------------------------------------------------------------------------------------------------------
Node 0:
Node d-8-3-2 has rank 0
MASTER_ADDR=172.31.130.37
MY_ADDRESS=172.31.130.37
python -m torch.distributed.run --nproc_per_node 1 --nnodes 2 --node_rank 0 --master_addr 172.31.130.37 --master_port 43829 train.py --data training.yaml --weights yolov5s.pt --img 2048 --project runs/train/11-15
Begin Training: Node 0
Node 1:
Node d-8-4-1 has rank 1
MASTER_ADDR=172.31.130.37
MY_ADDRESS=172.31.130.38
python -m torch.distributed.run --nproc_per_node 1 --nnodes 2 --node_rank 1 --master_addr 172.31.130.37 --master_port 43829 train.py --data training.yaml --weights yolov5s.pt --img 2048 --project runs/train/11-15
Begin Training: Node 1
----------------------------------------------------------------------------------------------------------
Everything seems to be correct so far. The master address is being shared correctly and the node ranks are displayed properly. The master node then outputs the training (weights, data, epochs, etc.) and loads in the images.
----------------------------------------------------------------------------------------------------------
train: weights=yolov5s.pt, cfg=, data=training.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=2048, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train/11-15, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (offline), for updates see https://github.com/ultralytics/yolov5
YOLOv5 🚀 v7.0-232-g1c60c53 Python-3.9.16 torch-2.0.0+cu117 CUDA:0 (Tesla V100-PCIE-32GB, 32501MiB)
hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs/train/11-15', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs
Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
train: Scanning /data1/groups/arclab_optnav/yolov5/training/labels/train2023/trial-11-02-23/povray_images.cache... 133669 images, 44 backgrounds, 0 cotrain: Scanning /data1/groups/arclab_optnav/yolov5/training/labels/train2023/trial-11-02-23/povray_images.cache... 133669 images, 44 backgrounds, 0 corrupt: 100%|██████████| 133713/133713 [00:00<?, ?it/s]
train: Scanning /data1/groups/arclab_optnav/yolov5/training/labels/train2023/trial-11-02-23/povray_images.cache... 133669 images, 44 backgrounds, 0 cotrain: Scanning /data1/groups/arclab_optnav/yolov5/training/labels/train2023/trial-11-02-23/povray_images.cache... 133669 images, 44 backgrounds, 0 corrupt: 100%|██████████| 133713/133713 [00:00<?, ?it/s]
val: Scanning /data1/groups/arclab_optnav/yolov5/training/labels/validation/trial-11-02-23.cache... 32550 images, 13121 backgrounds, 0 corrupt: 100%|█val: Scanning /data1/groups/arclab_optnav/yolov5/training/labels/validation/trial-11-02-23.cache... 32550 images, 13121 backgrounds, 0 corrupt: 100%|██████████| 45671/45671 [00:00<?, ?it/s]
AutoAnchor: 4.03 anchors/target, 0.995 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/11-15/exp7/labels.jpg...
----------------------------------------------------------------------------------------------------------
One thing I'll ask real quick: Given that we are doing multi-gpu training and that the master node is the only machine outputting information, should it show all the GPUs? It only shows one for the output. Also, I noticed that it outputs the scanning for train 4x and val 2x. Is that correct?
Anyway, the error occurs after all of that due to a time out:
----------------------------------------------------------------------------------------------------------
[E ProcessGroupNCCL.cpp:828] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=4, OpType=ALLGATHER, Timeout(ms)=1800000) ran for 1800081 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:455] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:460] To avoid data inconsistency, we are taking the entire process down.
terminate called after throwing an instance of 'std::runtime_error'
what(): [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=4, OpType=ALLGATHER, Timeout(ms)=1800000) ran for 1800081 milliseconds before timing out.
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -6) local_rank: 0 (pid: 95313) of binary: /state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/bin/python
[E ProcessGroupNCCL.cpp:455] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:460] To avoid data inconsistency, we are taking the entire process down.
terminate called after throwing an instance of 'std::runtime_error'
what(): NCCL error: remote process exited or there was a network error, NCCL version 2.14.3
ncclRemoteError: A call failed possibly due to a network error or a remote process exiting prematurely.
Last error:
NET/IB : Got completion from peer 172.31.130.38<42217> with error 12, opcode 32753, len 0, vendor err 129
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -6) local_rank: 0 (pid: 95884) of binary: /state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/bin/python
Traceback (most recent call last):
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/run.py", line 798, in <module>
Traceback (most recent call last):
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/run.py", line 798, in <module>
main()
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
main()
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/run.py", line 794, in main
return f(*args, **kwargs)
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/run.py", line 794, in main
run(args)
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/run.py", line 785, in run
run(args)
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
elastic_launch(
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
return launch_agent(self._config, self._entrypoint, list(args))
File "/state/partition1/llgrid/pkg/anaconda/anaconda3-2023a-pytorch/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
======================================================
train.py FAILED
------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-11-15_23:13:49
host : d-8-3-2.supercloud.mit.edu
rank : 0 (local_rank: 0)
exitcode : -6 (pid: 95884)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 95884
======================================================
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
======================================================
train.py FAILED
------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-11-15_23:09:34
host : d-8-4-1.supercloud.mit.edu
rank : 1 (local_rank: 0)
exitcode : -6 (pid: 95313)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 95313
======================================================
srun: error: d-8-4-1: task 1: Exited with exit code 1
srun: error: d-8-3-2: task 0: Exited with exit code 1
----------------------------------------------------------------------------------------------------------
Would somebody be able to assist me in figuring out the issue? My guess is that the nodes are not correctly communicating with each other. I have been struggling with this for weeks now :( Training on a single node works no problem, but multi-node seems to be an issue.
----------------------------------------------------------------------------------------------------------
### Additional
_No response_ | closed | 2023-11-25T20:46:20Z | 2024-01-21T00:23:40Z | https://github.com/ultralytics/yolov5/issues/12429 | [
"question",
"Stale"
] | Tmkilduff | 8 |
sqlalchemy/alembic | sqlalchemy | 322 | Typo in docs | **Migrated issue, originally created by Tim Mitchell**
https://alembic.readthedocs.org/en/latest/api/autogenerate.html#fine-grained-autogenerate-generation-with-rewriters
The code sample has
```
from alembic import ops
```
instead of
```
from alembic.operations import ops
```
also the second argument to AlterColumnOp should be op.column.name not op.column_name
| closed | 2015-08-27T06:02:27Z | 2018-11-30T15:39:16Z | https://github.com/sqlalchemy/alembic/issues/322 | [
"bug",
"documentation",
"easy"
] | sqlalchemy-bot | 4 |
NullArray/AutoSploit | automation | 1,155 | Unhandled Exception (7702fc06b) | Autosploit version: `2.2.3`
OS information: `Linux-3.4.113-armv7l-with-libc`
Running context: `/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/autosploit.py`
Error meesage: `[Errno 2] No such file or directory: 'tinchi.hau.edu.vn'`
Error traceback:
```
Traceback (most recent call):
File "/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/autosploit/main.py", line 123, in main
terminal.terminal_main_display(loaded_exploits)
File "/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/lib/term/terminal.py", line 331, in terminal_main_display
self.custom_host_list(loaded_mods)
File "/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/lib/term/terminal.py", line 277, in custom_host_list
self.exploit_gathered_hosts(mods, hosts=provided_host_file)
File "/data/data/com.thecrackertechnology.andrax/ANDRAX/AutoSploit/lib/term/terminal.py", line 217, in exploit_gathered_hosts
host_file = open(hosts).readlines()
IOError: [Errno 2] No such file or directory: 'tinchi.hau.edu.vn'
```
Metasploit launched: `False`
| closed | 2019-08-17T07:44:02Z | 2019-08-30T21:59:26Z | https://github.com/NullArray/AutoSploit/issues/1155 | [] | AutosploitReporter | 0 |
ymcui/Chinese-BERT-wwm | tensorflow | 98 | 关于chinese-roberta-wwm-ext-large模型的问题 | 您好，我在用您的chinese-roberta-wwm-ext-large模型做MLM任务时发现好像有bug。我分别尝试过google/bert的inference代码以及huggingface的Transformers工具的inference代码，好像都有明显的问题。以下是调用Transformers的代码：
```python3
from transformers import *
import torch
from torch.nn.functional import softmax
tokenizer = BertTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
model = AutoModelWithLMHead.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
inputtext = "今天[MASK]情很好"
maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
print(predicted_token,logit_prob[predicted_index])
```
输出是：
```
##覆 0.00043045077472925186
```
| closed | 2020-03-31T10:34:24Z | 2021-02-13T15:06:37Z | https://github.com/ymcui/Chinese-BERT-wwm/issues/98 | [] | pxxgogo | 3 |
modelscope/modelscope | nlp | 442 | _batch_encode_plus() got an unexpected keyword argument 'label2id' | /opt/homebrew/Caskroom/miniconda/base/envs/modelscope/bin/python /Users/edy/PycharmProjects/pythonProject2/test_structbert.py
2023-08-07 09:45:45,874 - modelscope - INFO - PyTorch version 1.11.0 Found.
2023-08-07 09:45:45,876 - modelscope - INFO - Loading ast index from /Users/edy/.cache/modelscope/ast_indexer
2023-08-07 09:45:45,914 - modelscope - INFO - Loading done! Current index file version is 1.6.1, with md5 dfe0c44a1cbd1117ff92d95368f3a1fd and a total number of 849 components indexed
2023-08-07 09:45:47,016 - modelscope - INFO - Model revision not specified, use the latest revision: v1.0.0
2023-08-07 09:45:47,416 - modelscope - INFO - initiate model from /Users/edy/.cache/modelscope/hub/damo/nlp_structbert_alimeeting_action-classification_chinese-base
2023-08-07 09:45:47,416 - modelscope - INFO - initiate model from location /Users/edy/.cache/modelscope/hub/damo/nlp_structbert_alimeeting_action-classification_chinese-base.
2023-08-07 09:45:47,418 - modelscope - INFO - initialize model from /Users/edy/.cache/modelscope/hub/damo/nlp_structbert_alimeeting_action-classification_chinese-base
2023-08-07 09:45:51,144 - modelscope - INFO - cuda is not available, using cpu instead.
Traceback (most recent call last):
File "/Users/edy/PycharmProjects/pythonProject2/test_structbert.py", line 6, in <module>
output = text_classification("今天会议的第一个结论是明天先收集用户的需求吗？")
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/modelscope/pipelines/base.py", line 212, in __call__
output = self._process_single(input, *args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/modelscope/pipelines/base.py", line 240, in _process_single
out = self.preprocess(input, **preprocess_params)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/modelscope/pipelines/base.py", line 381, in preprocess
return self.preprocessor(inputs, **preprocess_params)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/modelscope/preprocessors/nlp/fill_mask_preprocessor.py", line 54, in __call__
output = self._tokenize_text(text_a, text_b, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/modelscope/preprocessors/nlp/fill_mask_preprocessor.py", line 144, in _tokenize_text
return self.nlp_tokenizer(sequence1, sequence2, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/modelscope/preprocessors/nlp/transformers_tokenizer.py", line 108, in __call__
return self.tokenizer(text, text_pair, **tokenize_kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 2577, in __call__
encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 2683, in _call_one
return self.encode_plus(
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 2756, in encode_plus
return self._encode_plus(
File "/opt/homebrew/Caskroom/miniconda/base/envs/modelscope/lib/python3.8/site-packages/transformers/tokenization_utils_fast.py", line 497, in _encode_plus
batched_output = self._batch_encode_plus(
TypeError: _batch_encode_plus() got an unexpected keyword argument 'label2id'
求救
| closed | 2023-08-07T01:46:24Z | 2024-07-20T01:52:14Z | https://github.com/modelscope/modelscope/issues/442 | [
"Stale"
] | 1034662268 | 3 |
kubeflow/katib | scikit-learn | 2,395 | [SDK] grpc-related bugs in Python SDK | ### What happened?
When I called `report_metrics` in the SDK built by myself, the first error above occurred.
After I modified `grpc.beta.implementations.insecure_channel` to `grpc.insecure_channel`, the second error above occurred.
### What did you expect to happen?
After PR #2344 was merged to the main branch, `beta_create_DBManager_stub` was removed in `api_pb2.py`. But we did not make changes in the following part of codes so that the first error occurred
https://github.com/kubeflow/katib/blob/a6c37e4f3a64ca7f626a4d80f1c3f88362bf03a4/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py#L1288-L1294
https://github.com/kubeflow/katib/blob/a6c37e4f3a64ca7f626a4d80f1c3f88362bf03a4/sdk/python/v1beta1/kubeflow/katib/api/report_metrics.py#L66-L69
Also grpcio package after `1.0.0` has removed beta version. Sometimes we may get errors when we use beta version.
https://github.com/kubeflow/katib/blob/a6c37e4f3a64ca7f626a4d80f1c3f88362bf03a4/sdk/python/v1beta1/kubeflow/katib/api/report_metrics.py#L57-L59
https://github.com/kubeflow/katib/blob/a6c37e4f3a64ca7f626a4d80f1c3f88362bf03a4/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py#L1284-L1286
I think we need to may some changes to Python SDK. WDYT👀 @andreyvelich @tenzen-y @johnugeorge
### Environment
Kubernetes version:
```
Client Version: v1.30.2
Kustomize Version: v5.0.4-0.20230601165947-6ce0bf390ce3
Server Version: v1.30.1
```
Katib controller version:
```
Built by myself
```
Katib Python SDK version:
```
(Also built by myself)
Name: kubeflow-katib
Version: 0.17.0
Summary: Katib Python SDK for APIVersion v1beta1
Home-page: https://github.com/kubeflow/katib/tree/master/sdk/python/v1beta1
Author: Kubeflow Authors
Author-email: premnath.vel@gmail.com
License: Apache License Version 2.0
Location: /home/xxx/.local/lib/python3.10/site-packages/kubeflow_katib-0.17.0-py3.10.egg
Requires: certifi, grpcio, kubernetes, protobuf, setuptools, six, urllib3
Required-by:
```
### Impacted by this bug?
Give it a 👍 We prioritize the issues with most 👍 | closed | 2024-07-25T06:49:57Z | 2024-08-23T11:13:00Z | https://github.com/kubeflow/katib/issues/2395 | [
"kind/bug",
"area/sdk"
] | Electronic-Waste | 10 |
AntonOsika/gpt-engineer | python | 771 | KeyError: "File 'roadmap' could not be found in '/content/gpt-engineer/gpt_engineer/cli/preprompts'" | ## Expected Behavior
gpt-engineer is reading the folder 'preprompt' from './gpt_engineer'.
## Current Behavior
gpt_engineer is expecting the folder 'preprompt' in './gpt_engineer/cli'. Most likely because main.py was earlier located in the same path as the folder preprompt.
## Failure Information
KeyError: "File 'roadmap' could not be found in
'/content/gpt-engineer/gpt_engineer/cli/preprompts'"
### Steps to Reproduce
clone the developer edition and try to run the example prompt
| closed | 2023-10-06T23:10:26Z | 2023-10-07T17:21:51Z | https://github.com/AntonOsika/gpt-engineer/issues/771 | [
"bug",
"triage"
] | chrisivogt | 1 |
sloria/TextBlob | nlp | 425 | AttributeError: module 'numpy' has no attribute '_no_nep50_warning' | Tried to ran:
```python
from textblob import TextBlob
s = TextBlob('Test Text.')
s
```
And this error came out.
Potentially about the versions of numpy? | closed | 2024-02-13T13:39:48Z | 2024-02-13T14:45:43Z | https://github.com/sloria/TextBlob/issues/425 | [] | austinmyc | 1 |
Sanster/IOPaint | pytorch | 306 | [Feature Request] Combing GroundingDINO and SAM for more automatic inpaint | Hello! Thanks for this great open-source project~
I think it will be more powerful to combine GroundingDINO and SAM and Lama-Cleaner together to build a powerful automatic inpaint system~
Here's our GroundingDINO and Grounded-SAM project:
- https://github.com/IDEA-Research/Grounded-Segment-Anything
- https://github.com/IDEA-Research/GroundingDINO
And I think it will be very interesting to build such functions~
| open | 2023-05-14T07:02:49Z | 2024-02-26T10:27:09Z | https://github.com/Sanster/IOPaint/issues/306 | [] | rentainhe | 6 |
kubeflow/katib | scikit-learn | 1,930 | Implement Readiness/LivenessProbe endpoint for katib-controller | /kind feature
**Describe the solution you'd like**
Even though katib-controller pod's status became ready. It does not mean that katib-controller is available for all feature.
For example, if user request `/validate-experiment` API to katib-controller, right after katib-controller was just created, It returns 200 response without actually processing the real logic.
First, we need to define how to determine if katib-controller is ready or live. and then implement it.
In usual cases, readiness endpoint checks whether app could process all requests (usually try connecting to the database).
Also, liveness endpoint checks just the container is alive or not.
**Anything else you would like to add:**
Raised from https://github.com/kubeflow/katib/pull/1921#discussion_r944579336
---
<!-- Don't delete this message to encourage users to support your issue! -->
Love this feature? Give it a 👍 We prioritize the features with the most 👍
| closed | 2022-08-17T14:01:00Z | 2022-10-11T16:50:28Z | https://github.com/kubeflow/katib/issues/1930 | [
"kind/feature"
] | anencore94 | 3 |
skforecast/skforecast | scikit-learn | 909 | Error when plotting plot_residuals | Hola chicos,
al tratar de hacer el plot residuals en Data Brics , me salta el error OptionError: No such keys(s): 'mode.use_inf_as_null', estoy usando las librerias como ustedes
Versión skforecast: 0.14.0
Versión scikit-learn: 1.5.2
Versión lightgbm: 4.4.0
Versión pandas: 2.2.3
Versión numpy: 1.24.4
Detalle del error
OptionError: No such keys(s): 'mode.use_inf_as_null'
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-a7232911-a34c-44b0-a99c-1ea24de91142/lib/python3.10/site-packages/skforecast/plot/plot.py:70, in plot_residuals(residuals, y_true, y_pred, fig, **fig_kw)
67 ax3 = plt.subplot(gs[1, 1])
69 ax1.plot(residuals)
---> 70 sns.histplot(residuals, kde=True, bins=30, ax=ax2)
71 plot_acf(residuals, ax=ax3, lags=60)
Saludos
Alejandro | closed | 2025-01-17T14:19:04Z | 2025-01-20T13:45:55Z | https://github.com/skforecast/skforecast/issues/909 | [] | azevallos | 3 |
ibis-project/ibis | pandas | 9,998 | feat: support unnest of struct with trino backend | ### Is your feature request related to a problem?
Disclaimer: I am quite new to ibis, so perhaps I missed something.
Let's say I have a table with a column that contains arrays of struct. For example, a struct with two fields:
| some_col | my_nested_column |
|---------- |----------------------------------------- |
| 1 | [{foo: 10, bar: 20}, {foo:11, bar: 21}] |
| 2 | [{foo: 12, bar: 22}] |
I have this SQL query that works well on my trino DB to unnest this column:
```sql
SELECT
t.foo,
t.bar
FROM my_db.my_table
CROSS JOIN UNNEST(my_nested_column) AS t (foo, bar)
LIMIT 10
```
I am trying to do the same with ibis with this code:
```python
t = con.table(name="my_table", database="my_db")
tmp = t.unnest(t.my_nested_column).head(10)
tmp.to_pandas()
```
Unfortunately, I get the following exception:
```
TrinoUserError: TrinoUserError(type=USER_ERROR, name=MISMATCHED_COLUMN_ALIASES, message="line 1:391: Column alias list has 1 entries but '"ibis_table_unnest_kelyvy3bprd4dimwx7rk4ia5hi"' has 2 columns available", query_id=20240902_151758_26511_qgqzg)
```
My guess is that it is because the code produced by the trino backend from ibis does not list the fields of the struct. Calling `ibis.to_sql(tmp)` produces this query:
```sql
SELECT
"ibis_table_unnest_column_wt6chn4jxra6dbfrnunq7tplom" AS "my_nested_column"
FROM "my_db.my_table" AS "t0"
CROSS JOIN UNNEST("t0"."my_nested_column") AS "ibis_table_unnest_bcm5qmv32bgjtlum4uclrclvpq"("ibis_table_unnest_column_wt6chn4jxra6dbfrnunq7tplom")
LIMIT 10
```
I found [this related SO question](https://stackoverflow.com/questions/75473521/how-do-i-get-rid-of-this-errorcolumn-alias-list-has-1-entries-but-t-has-4-col), writing explicitly all the fields of the struct seems to be needed.
Note that I tried a similar code on another column that has simple lists of integers instead of lists of structs and it works well, so the struct seem to be the issue.
### What is the motivation behind your request?
_No response_
### Describe the solution you'd like
I guess a possible solution would be for the user to give the list of the fields in the unnest function. For instance:
```python
tmp = t.unnest(t.my_nested_column, fields=["foo", "bar"]).head(10)
```
But I am not sure what the other back-ends should do when this argument is provided. Perhaps just ignore it?
### What version of ibis are you running?
9.3.0
### What backend(s) are you using, if any?
Trino
### Code of Conduct
- [X] I agree to follow this project's Code of Conduct | open | 2024-09-02T15:55:22Z | 2024-09-03T18:40:47Z | https://github.com/ibis-project/ibis/issues/9998 | [
"bug",
"feature",
"trino"
] | Ezibenroc | 4 |
unit8co/darts | data-science | 1,779 | Deterministic evaluation of probabilistic models (e.g. nbeats) | When predicting a time series, one can turn some models probabilistic by defining a likelihood parameter.
E.g.:
NBEATSModel(
...
likelihood=GaussianLikelihood(),
random_state=42
)
If done this way, the prediction is not deterministic any more. Two subsequent calls to
predict(series=.., n=1, num_samples=1)
yield different results.
For developing purposes, it would be nice to be able to fix the random state of the likelihood as well so that, independent of the num_samples defined, the result of the prediction always stays the same. | open | 2023-05-17T13:56:50Z | 2023-08-02T11:33:54Z | https://github.com/unit8co/darts/issues/1779 | [
"feature request",
"good first issue"
] | mrtn37 | 2 |
HumanSignal/labelImg | deep-learning | 986 | LabelImg not working on M1 mac | <!--
-->
I am using M1 mac. How to use labelimg? I tried all the possible ways mentioned in the readme file but it does't work for me. Please help.
- **OS:**
- **PyQt version:**
| closed | 2023-03-24T15:12:17Z | 2023-03-24T16:17:23Z | https://github.com/HumanSignal/labelImg/issues/986 | [] | Siddharth-2382 | 0 |
pytest-dev/pytest-xdist | pytest | 441 | the number of interpreters created with auto should be at most number of tests | At the moment even with 1 test, on a 12 core CPU 12 interpreters are spun up. | closed | 2019-06-13T14:23:06Z | 2019-06-14T15:32:41Z | https://github.com/pytest-dev/pytest-xdist/issues/441 | [] | gaborbernat | 3 |
Evil0ctal/Douyin_TikTok_Download_API | web-scraping | 550 | 现在获取单个作品数据接口不行了吗 | 
我本地测试报错,Cookie和User-Agent换过
报错信息如下
 | closed | 2025-02-12T06:28:51Z | 2025-02-14T02:41:17Z | https://github.com/Evil0ctal/Douyin_TikTok_Download_API/issues/550 | [] | lao-wen | 4 |
lepture/authlib | flask | 724 | RFC9126 Pushed Authorization Requests (PAR) | It would be nice for Authlib to support [Pushed Authorization Requests](https://www.rfc-editor.org/rfc/rfc9126.html) described in RFC9126. This would be a complement to #723
Basically, it consists in letting clients push a JWT with the authentication request detail to the authentication server, and get an URL on which the authentication process can be performed. | open | 2025-03-19T14:47:52Z | 2025-03-19T14:49:54Z | https://github.com/lepture/authlib/issues/724 | [
"spec"
] | azmeuk | 0 |
matterport/Mask_RCNN | tensorflow | 2,822 | Random results in demo and custom dataset detection | I used the code from this <a href="https://github.com/leekunhee/Mask_RCNN">fork</a> and the results are so random even in the demo.ipynb file. What might be the issue and what can I do to fix it. Thank you.
<img src="https://i.imgur.com/sUrZYsD.png"> | open | 2022-05-07T16:19:20Z | 2022-06-29T19:33:13Z | https://github.com/matterport/Mask_RCNN/issues/2822 | [] | MedAmine121 | 5 |
httpie/cli | rest-api | 1,418 | --help fails with a traceback when pip is not installed | ## Checklist
- [x] I've searched for similar issues.
- [x] I'm using the latest version of HTTPie.
---
## Minimal reproduction code and steps
1. `http --help` or `https --help`
## Current result
<details><summary>output with traceback</summary>
```py
$ http --help
Traceback (most recent call last):
File "/usr/bin/http", line 33, in <module>
sys.exit(load_entry_point('httpie==3.2.1', 'console_scripts', 'http')())
File "/usr/lib/python3.10/site-packages/httpie/__main__.py", line 9, in main
exit_status = main()
File "/usr/lib/python3.10/site-packages/httpie/core.py", line 162, in main
return raw_main(
File "/usr/lib/python3.10/site-packages/httpie/core.py", line 77, in raw_main
parsed_args = parser.parse_args(
File "/usr/lib/python3.10/site-packages/httpie/cli/argparser.py", line 159, in parse_args
self.args, no_options = super().parse_known_args(args, namespace)
File "/usr/lib/python3.10/argparse.py", line 1859, in parse_known_args
namespace, args = self._parse_known_args(args, namespace)
File "/usr/lib/python3.10/argparse.py", line 2068, in _parse_known_args
start_index = consume_optional(start_index)
File "/usr/lib/python3.10/argparse.py", line 2008, in consume_optional
take_action(action, args, option_string)
File "/usr/lib/python3.10/argparse.py", line 1936, in take_action
action(self, namespace, argument_values, option_string)
File "/usr/lib/python3.10/argparse.py", line 1099, in __call__
parser.print_help()
File "/usr/lib/python3.10/argparse.py", line 2556, in print_help
self._print_message(self.format_help(), file)
File "/usr/lib/python3.10/argparse.py", line 2533, in format_help
formatter.add_arguments(action_group._group_actions)
File "/usr/lib/python3.10/argparse.py", line 277, in add_arguments
self.add_argument(action)
File "/usr/lib/python3.10/argparse.py", line 258, in add_argument
if action.help is not SUPPRESS:
File "/usr/lib/python3.10/site-packages/httpie/cli/utils.py", line 60, in help
self.load(),
File "/usr/lib/python3.10/site-packages/httpie/cli/utils.py", line 51, in load
self._obj = self.getter()
File "/usr/lib/python3.10/site-packages/httpie/output/formatters/colors.py", line 37, in get_available_styles
return sorted(BUNDLED_STYLES | set(pygments.styles.get_all_styles()))
File "/usr/lib/python3.10/site-packages/pygments/styles/__init__.py", line 92, in get_all_styles
for name, _ in find_plugin_styles():
File "/usr/lib/python3.10/site-packages/pygments/plugin.py", line 63, in find_plugin_styles
for entrypoint in iter_entry_points(STYLE_ENTRY_POINT):
File "/usr/lib/python3.10/site-packages/pygments/plugin.py", line 45, in iter_entry_points
import pkg_resources
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 3260, in <module>
def _initialize_master_working_set():
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 3234, in _call_aside
f(*args, **kwargs)
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 3272, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 581, in _build_master
ws.require(__requires__)
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 909, in require
needed = self.resolve(parse_requirements(requirements))
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 795, in resolve
raise DistributionNotFound(req, requirers)
pkg_resources.DistributionNotFound: The 'pip' distribution was not found and is required by httpie
```
</details>
## Expected result
The help message
---
## Debug output
Please re-run the command with `--debug`, then copy the entire command & output and paste both below:
<details><summary>debug output</summary>
```py
http --debug --help
HTTPie 3.2.1
Requests 2.27.1
Pygments 2.12.0
Python 3.10.5 (main, Jun 8 2022, 02:00:39) [GCC 10.2.1 20201203]
/usr/bin/python3
Linux 5.15.45_1
<Environment {'apply_warnings_filter': <function Environment.apply_warnings_filter at 0x7f46416fc160>,
'args': Namespace(),
'as_silent': <function Environment.as_silent at 0x7f46416fc040>,
'colors': 256,
'config': {'default_options': []},
'config_dir': PosixPath('/home/abby/.config/httpie'),
'devnull': <property object at 0x7f46416e8310>,
'is_windows': False,
'log_error': <function Environment.log_error at 0x7f46416fc0d0>,
'program_name': 'http',
'quiet': 0,
'rich_console': <functools.cached_property object at 0x7f46416c8f10>,
'rich_error_console': <functools.cached_property object at 0x7f46416caa40>,
'show_displays': True,
'stderr': <_io.TextIOWrapper name='<stderr>' mode='w' encoding='utf-8'>,
'stderr_isatty': True,
'stdin': <_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>,
'stdin_encoding': 'utf-8',
'stdin_isatty': True,
'stdout': <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>,
'stdout_encoding': 'utf-8',
'stdout_isatty': True}>
<PluginManager {'adapters': [],
'auth': [<class 'httpie.plugins.builtin.BasicAuthPlugin'>,
<class 'httpie.plugins.builtin.DigestAuthPlugin'>,
<class 'httpie.plugins.builtin.BearerAuthPlugin'>],
'converters': [],
'formatters': [<class 'httpie.output.formatters.headers.HeadersFormatter'>,
<class 'httpie.output.formatters.json.JSONFormatter'>,
<class 'httpie.output.formatters.xml.XMLFormatter'>,
<class 'httpie.output.formatters.colors.ColorFormatter'>]}>
Traceback (most recent call last):
File "/usr/bin/http", line 33, in <module>
sys.exit(load_entry_point('httpie==3.2.1', 'console_scripts', 'http')())
File "/usr/lib/python3.10/site-packages/httpie/__main__.py", line 9, in main
exit_status = main()
File "/usr/lib/python3.10/site-packages/httpie/core.py", line 162, in main
return raw_main(
File "/usr/lib/python3.10/site-packages/httpie/core.py", line 77, in raw_main
parsed_args = parser.parse_args(
File "/usr/lib/python3.10/site-packages/httpie/cli/argparser.py", line 159, in parse_args
self.args, no_options = super().parse_known_args(args, namespace)
File "/usr/lib/python3.10/argparse.py", line 1859, in parse_known_args
namespace, args = self._parse_known_args(args, namespace)
File "/usr/lib/python3.10/argparse.py", line 2068, in _parse_known_args
start_index = consume_optional(start_index)
File "/usr/lib/python3.10/argparse.py", line 2008, in consume_optional
take_action(action, args, option_string)
File "/usr/lib/python3.10/argparse.py", line 1936, in take_action
action(self, namespace, argument_values, option_string)
File "/usr/lib/python3.10/argparse.py", line 1099, in __call__
parser.print_help()
File "/usr/lib/python3.10/argparse.py", line 2556, in print_help
self._print_message(self.format_help(), file)
File "/usr/lib/python3.10/argparse.py", line 2533, in format_help
formatter.add_arguments(action_group._group_actions)
File "/usr/lib/python3.10/argparse.py", line 277, in add_arguments
self.add_argument(action)
File "/usr/lib/python3.10/argparse.py", line 258, in add_argument
if action.help is not SUPPRESS:
File "/usr/lib/python3.10/site-packages/httpie/cli/utils.py", line 60, in help
self.load(),
File "/usr/lib/python3.10/site-packages/httpie/cli/utils.py", line 51, in load
self._obj = self.getter()
File "/usr/lib/python3.10/site-packages/httpie/output/formatters/colors.py", line 37, in get_available_styles
return sorted(BUNDLED_STYLES | set(pygments.styles.get_all_styles()))
File "/usr/lib/python3.10/site-packages/pygments/styles/__init__.py", line 92, in get_all_styles
for name, _ in find_plugin_styles():
File "/usr/lib/python3.10/site-packages/pygments/plugin.py", line 63, in find_plugin_styles
for entrypoint in iter_entry_points(STYLE_ENTRY_POINT):
File "/usr/lib/python3.10/site-packages/pygments/plugin.py", line 45, in iter_entry_points
import pkg_resources
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 3260, in <module>
def _initialize_master_working_set():
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 3234, in _call_aside
f(*args, **kwargs)
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 3272, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 581, in _build_master
ws.require(__requires__)
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 909, in require
needed = self.resolve(parse_requirements(requirements))
File "/usr/lib/python3.10/site-packages/pkg_resources/__init__.py", line 795, in resolve
raise DistributionNotFound(req, requirers)
pkg_resources.DistributionNotFound: The 'pip' distribution was not found and is required by httpie
```
</details>
## Additional information, screenshots, or code examples
This was done using [void linux's httpie package](https://github.com/void-linux/void-packages/blob/master/srcpkgs/httpie/template)
| closed | 2022-06-22T16:16:32Z | 2022-07-21T04:07:41Z | https://github.com/httpie/cli/issues/1418 | [
"bug",
"new"
] | classabbyamp | 1 |
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