gunicorn
copilot-cli
Our great sponsors
gunicorn | copilot-cli | |
---|---|---|
17 | 51 | |
9,475 | 3,304 | |
- | 1.2% | |
8.1 | 9.6 | |
15 days ago | 6 days ago | |
Python | Go | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gunicorn
-
Nginx Unit – Universal web app server
I'm hoping so – gunicorn has a long-open pull request that would fix `--reuse-port`, which currently does nothing
- SynchronousOnlyOperation from celery task using gevent execution pool on django orm
-
Deploying Django when using python-socketio
However, I'm curious about the best way to deploy, specifically with regard to WSGI. I've tried using the raw eventlet WSGI server (`eventlet.wsgi.server(eventlet.listen(("", 8000)), application)`). I then start it with `python manage.py runserver`. This has worked okay, but I'm unsure about how scalable it is. It seems like the standard stack is Django + Gunicorn + NGINX. Based on `python-socketio` documentation, this should be possible. I tried django + eventlet + gunicorn, but it seems like gunicorn a) [doesn't play nice with eventlet](https://github.com/benoitc/gunicorn/pull/2581) and b) only supports one worker. Gevent + Gunicorn doesn't have this bug, but still only supports one worker. Also, I'm not sure how actively maintained gevent is. So I'm not sure how scalable either Gunicorn + eventlet or Gunicorn + geventlet is as a WSGI server. So I'm not sure if Gunicorn is my best bet, or if it's too limited.
- The Django ecosystem is not so good
-
3 cool project ideas for Python programmers
For building your API, I recommend using the Flask library. It is very beginner-friendly, and you will be able to build a simple API in a matter of minutes! Keep in mind that, for a more serious project, you should definitely use something like gunicorn to run you API as a production server.
-
Django 4.1 Released
Interesting looks like it might actually be a python bug. Somehow just changing from sys.exit(0) -> os._exit(0) apparently fixes it.
-
Serverless Templates for AWS and Python
The cool thing is that you can easily migrate your WSGI- application such as Flask, Django, or Gunicorn to AWS.
-
Scope of database threads + connections + sessions
Yeah, that's kind of the impression I was getting. I stumbled across a github issue for gunicorn along these lines.
-
Running Django with Gunicorn - Best Practice
Taking a glimpse at gunicorn's code it looks like they pretty much all do the same: 2. seems to be creating a wsgi app using django's internals, and 3. uses 2.
copilot-cli
-
Use AWS Graviton processors on AWS Fargate with Copilot
AWS Copilot CLI is an open source command line interface for running containers on AWS App Runner, Amazon Elastic Container Service (ECS), and AWS Fargate.
-
AI Chatbot powered by Amazon Bedrock 🚀🤖
sudo curl -Lo /usr/local/bin/copilot https://github.com/aws/copilot-cli/releases/latest/download/copilot-linux && sudo chmod +x /usr/local/bin/copilot
-
Launch HN: Nullstone (YC W22) – An easier way to deploy and manage cloud apps
Check out AWS Copilot CLI: https://aws.github.io/copilot-cli/
This is by far the best way to deploy compute into AWS in containerized workloads.
The abstraction you want is Jobs: ttps://aws.github.io/copilot-cli/docs/concepts/jobs/
Building this any other way on AWS would require provisioning multiple artifacts. The Copilot Jobs abstraction basically encapsulates the provisioning of those artifacts into one repeatable pattern.
- Support of Lambda web adapter on AWS Copilot
-
AWS ECS Basics and Tips
AWS Copilot CLI is a tool that lets you deploy production-ready, scalable services on AWS from a Dockerfile in one command.
-
Need some help understanding pulling git code to ECS.
and here is the copilot page if you are interested https://aws.github.io/copilot-cli/
- AWS Copilot CLI
-
What is your production environment?
For larger high availability required things, AWS ECS with RDS, ElastiCache, CloudFront, S3, etc.. Really like Copilot for deployment/env/secret/sidecar management (probably needs a rename now): https://aws.github.io/copilot-cli/
-
Heroku Status – Dashboard/API Offline
We are spending about 60% less. Workload has actually lessened since AWS is so much more stable. Getting to a similar DX as Heroku was quite the lift, but once it's done, it's done. These days we generally only have outages when we screw something up ourselves. I recommend https://github.com/aws/copilot-cli for starting out on ECS.
-
Deploying on ECS
I'd recommend checking out AWS Copilot (https://aws.github.io/copilot-cli/)
What are some alternatives?
waitress - Waitress - A WSGI server for Python 3
TabNine - AI Code Completions
Werkzeug - The comprehensive WSGI web application library.
terraform-cdk - Define infrastructure resources using programming constructs and provision them using HashiCorp Terraform
bjoern - A screamingly fast Python 2/3 WSGI server written in C.
terraforming - Export existing AWS resources to Terraform style (tf, tfstate) / No longer actively maintained
uwsgi - Official uWSGI docs, examples, tutorials, tips and tricks
awesome-cdk - A collection of awesome things related to the AWS Cloud Development Kit (CDK)
meinheld - Meinheld is a high performance asynchronous WSGI Web Server (based on picoev)
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
hypercorn - Hypercorn is an ASGI and WSGI Server based on Hyper libraries and inspired by Gunicorn.
Pulumi - Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀