scipipe
quart
scipipe | quart | |
---|---|---|
1 | 5 | |
1,054 | 2,634 | |
0.2% | 1.8% | |
3.0 | 8.0 | |
10 months ago | 6 days ago | |
Go | Python | |
MIT License | MIT License |
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.
scipipe
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Ask HN: What have you created that deserves a second chance on HN?
https://scipipe.org - A pipeline tool for shell commands by a declarative flow-based API in Go
Github link: https://github.com/scipipe/scipipe
There are many pipeline tools for shell commands, but a majority has one or more limitations in their API which makes certain complex pipelines impossible or really hard to write.
We were pushing the limits of all the tools we tried, so developed our own, and implemented it in Go, with a declarative API for defining the data flow dependencies, instead of inventing yet another DSL. This has allowed us great flexibility in developing also complex pipelines, e.g. combining parameter sweeps nested with cross-validation implemented as workflow constructs.
SciPipe is also unique in providing an audit report for every single output of the workflow, in a structured JSON format. A helper tool allows converting these reports to either an HTML report, a PDF, or a Bash script that will generate the one accompanying output file from scratch.
An extra cool things is that, because the audit reports live alongside output files, if you run a scipipe workflow that uses files generated by another scipipe workflow, it will pick up also all the history for the input files generated by this earlier workflow, meaning that you get a 100% complete audit report, even if your analysis spans multiple workflows!
quart
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Ask HN: What have you created that deserves a second chance on HN?
I've three things :),
1. Quart, https://quart.palletsprojects.com, an ASGI (async/await) re-implementation of the Python web MicroFramework Flask. It is now maintained alongside, by the same people, as Flask.
2. Hypercorn, https://hypercorn.readthedocs.io, an ASGI/WSGI server that supports HTTP/1, HTTP/2, and HTTP/3.
3. My book "A Blueprint for Production-Ready Web Applications", which uses both of the above and shows a beginner how to build a full stack app (React frontend) running on AWS. See https://pgjones.dev/tozo/ for details, code, and link to the example app.
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How does a single running instance of a Flask application handle multiple requests at once? Is it all async or multithreaded? Or handled by the webserver (NGINX, gunicorn, etc)?
If you want async flask, you should use quart for now. The roadmap is to fully incorporate quart into flask at some point, but it’s unclear when that’s going to happen.
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This Week In Python
quart – An async Python micro framework for building web applications
- Quart: An async Python micro framework for building web applications
- Quart, the async implementation of Flask has joined Pallets
What are some alternatives?
codebase-visualizer-action - Visualize your codebase during CI.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
pytkml - Write tests for machine learning models
Flask - The Python micro framework for building web applications.
tripods-web - A puzzle game.
starlette - The little ASGI framework that shines. 🌟
UrlChecker - Android app by TrianguloY: URLCheck
flask-pydantic - flask extension for integration with the awesome pydantic package
dotfile - Simple version control made for tracking single files
flask-smorest - DB agnostic framework to build auto-documented REST APIs with Flask and marshmallow
osxphotos - Python app to work with pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata.
django-awl - Miscellaneous django tools