uvicorn | 易学教程

How to send a progress of operation in a FastAPI app?

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问题 I have deployed a fastapi endpoint, from fastapi import FastAPI, UploadFile from typing import List app = FastAPI() @app.post('/work/test') async def testing(files: List(UploadFile)): for i in files: ....... # do a lot of operations on each file # after than I am just writing that processed data into mysql database # cur.execute(...) # cur.commit() ....... # just returning "OK" to confirm data is written into mysql return {"response" : "OK"} I can request output from the API endpoint and its

Gunicorn is not respecting timeout when using UvicornWorker

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问题 I am setting up a timeout check so I made and endpoint: @app.get("/tc", status_code=200) def timeout_check(): time.sleep(500) return "NOT OK" I am using the docker image tiangolo/uvicorn-gunicorn-fastapi:python3.7 and my command to run the server: CMD ["gunicorn","--log-level","debug","--keep-alive","15", "--reload", "-b", "0.0.0.0:8080", "--timeout", "15", "--worker-class=uvicorn.workers.UvicornH11Worker", "--workers=10", "myapp.main:app"] I am expecting the endpoint to fail after 15 seconds

Pyinstaller-compiled Uvicorn server does not start correctly

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问题 When I start the server.exe and it is trying to perform uvicorn.run() , the exception is being thrown: Traceback (most recent call last): File "logging\config.py", line 390, in resolve ModuleNotFoundError: No module named 'uvicorn.logging' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "logging\config.py", line 542, in configure File "logging\config.py", line 654, in configure_formatter File "logging\config.py", line 469, in

Pyinstaller-compiled Uvicorn server does not start correctly

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Uvicorn running with Unicorn, how?

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问题 While reading the docs of running Uvicorn: Gunicorn is a mature, fully featured server and process manager. Uvicorn includes a Gunicorn worker class allowing you to run ASGI applications, with all of Uvicorn's performance benefits, while also giving you Gunicorn's fully-featured process management. Is doc wrong or i didn't understand correctly? from my understanding, Gunicorn has a master slave structure to allow Uvicon class (async sever) to run in separate worker classes using command:

How to do multiprocessing in FastAPI

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问题 While serving a FastAPI request, I have a CPU-bound task to do on every element of a list. I'd like to do this processing on multiple CPU cores. What's the proper way to do this within FastAPI? Can I use the standard multiprocessing module? All the tutorials/questions I found so far only cover I/O-bound tasks like web requests. 回答1: TL;DR You could use loop.run_in_executor with ProcessPoolExecutor to start function at a separate process. loop = asyncio.get_event_loop() with concurrent.futures

FastAPI--路由(2)

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一、概述路由方法有 GET, POST, PUT, PATCH, DELETE 和 OPTIONS。 import uvicorn from fastapi import FastAPI app = FastAPI() @app.post( "/") @app.put("/") @app.delete("/") @app.get("/") @app.options("/") @app.head("/") @app.patch("/") @app.trace("/" ) async def root(): return { " message " : " Hello 454533333343433World " } if __name__ == ' __main__ ' : uvicorn.run(app = ' main:app ' , host= " 127.0.0.1 " , port=8000, reload=True, debug=True) 二、路由Route上参数获取和校验一般我们的路由分会静态和动态，静态路由就是参数是固定写死，也就是访问地址是写死的，而动态地址，就是需要动态的生成，类似简书的博文的地址94710ed35b92就是动态，其实和Bottle和Flask一样。 https://www.jianshu.com/p/94710ed35b92 代码如下：

执行运行FastAPI [WinError 10013]以一种访问权限不允许的方式做了一个访问套接字的尝试错误的解决办法

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执行uvicorn main:app --reload如下错误 [WinError 10013]以一种访问权限不允许的方式做了一个访问套接字的尝试说明要启动的端口被其他程序占用，只需执行 netstat -ano|findstr 8000 找到占用8000的端口即可，关闭即可先执行 PS C:\Users\xyzbo> netstat -ano|findstr 8000 TCP 0.0.0.0:8000 0.0.0.0:0 LISTENING 64988 TCP 192.168.0.2:35465 58.251.121.55:8000 SYN_SENT 58236 UDP 0.0.0.0:8000 *:* 64988 再执行 taskill 强行关闭对应端口 PS C:\Users\xyzbo> taskkill /pid 64988 /f 8000端口还是会自动启动，只是占用进程号发生了变化，最后强行退出酷狗后，再执行上面强行终止命令就好了。来源： oschina 链接： https://my.oschina.net/u/53041/blog/4302544

使用Python部署机器学习模型的10个实践经验

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云栖号资讯：【点击查看更多行业资讯】在这里您可以找到不同行业的第一手的上云资讯，还在等什么，快来！有时候，作为数据科学家，我们会忘记公司付钱让我们干什么。我们首先是开发人员，然后是研究人员，然后可能是数学家。我们的首要责任是快速开发无bug的解决方案。我们能做模型并不意味着我们就是神。它没有给我们写垃圾代码的自由。从一开始，我就犯了很多错误，我想和大家分享一下我所看到的ML工程中最常见的技能。在我看来，这也是目前这个行业最缺乏的技能。我称他们为“软件文盲”，因为他们中的很多人都是非计算机科学课程学习平台(Coursera)的工程师。我自己曾经就是😅 如果要在一个伟大的数据科学家和一个伟大的ML工程师之间招聘，我会选择后者。让我们开始吧。 1. 学会写抽象类一旦你开始编写抽象类，你就会知道它能给你的代码库带来多大的清晰度。它们执行相同的方法和方法名称。如果很多人都在同一个项目上工作，每个人都会开始使用不同的方法。这可能会造成无效率的混乱。 import os from abc import ABCMeta, abstractmethod class DataProcessor(metaclass=ABCMeta): """Base processor to be used for all preparation.""" def __init__(self, input

Nginx reverse proxy on unix socket for uvicorn not working

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问题 Files : # main.py: from fastapi import FastAPI app = FastAPI() @app.get("/") def read_root(): return {"Hello": "World"} - # nginx.conf: events { worker_connections 128; } http{ server { listen 0.0.0.0:8080; location / { include uwsgi_params; uwsgi_pass unix:/tmp/uvi.sock; } } } - # Dockerfile FROM python:3 COPY main.py . RUN apt-get -y update && apt-get install -y htop tmux vim nginx RUN pip install fastapi uvicorn COPY nginx.conf /etc/nginx/ Setup : docker build -t nginx-uvicorn:latest .