jupyter-book
fastapi
jupyter-book | fastapi | |
---|---|---|
15 | 467 | |
3,692 | 71,023 | |
0.8% | - | |
8.5 | 9.8 | |
6 days ago | 6 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" 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.
jupyter-book
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I don't always use LaTeX, but when I do, I compile to HTML (2013)
Sphinx supports ReStructuredText and Markdown.
MyST-Markdown supports MathJaX and Sphinx roles and directives. https://myst-parser.readthedocs.io/en/latest/
jupyter-book supports ReStructuredText, Jupyter Notebooks, and MyST-Markdown documents:
You can build Sphinx and Jupyter-Book projects with the ReadTheDocs container, which already has LaTeX installed: https://github.com/executablebooks/jupyter-book/issues/991
myst-templates/plain_latex_book:
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
your task is very very broad
you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))
that would cover a lot of use cases right away without next to no learning curve
If you don't want to deal with AWS or similar, in that case:
- if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)
- if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/
- otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )
there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing
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How to raise the quality of scientific Jupyter notebooks
Note: If you want to present a cleaner version of the notebook without assertions, you can use Jupyter book to render it into a site and use the remove-cell tag to omit assertions from the output.
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Sunday Daily Thread: What's everyone working on this week?
See this thread for example.
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Are there any frameworks/methodologies/libraries that can help to create a PDF printable professionally looking written report?
And maybe take a look at executablebooks/jupyter-book.
- [P] I Made An Easy-To-Use Python Package That Creates Beautiful Html Reports From Jupyter Notebooks
- RStudio Is Becoming Posit
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Python toolkits
Our team has transferred from Sphinx for documentation to JupyterBook. There have been some growing pains with it but I prefer the look of the output and being able to play with the examples on Colab or Binder at the click of a button is a great feature.
- Ask HN: Tools to generate coverage of user documentation for code
- Why does [::-1] reverse a list?
fastapi
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FastAPI Best Practices: A Condensed Guide with Examples
FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
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Analysing FastAPI Middleware Performance
Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696
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LangChain, Python, and Heroku
An API application framework (such as FastAPI)
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Litestar – powerful, flexible, and highly performant Python ASGI framework
It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.
This ends up made worse if you’re using sync routes.
If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.
This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759
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AI-Powered Image Search with CLIP, pgvector, and Fast API
Fast API.
- Ask HN: What is your go-to stack for the web?
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Fun with Avatars: Crafting the core engine | Part. 1
We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:
What are some alternatives?
Spyder - Official repository for Spyder - The Scientific Python Development Environment
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
sphinx-thebe - A Sphinx extension to convert static code into interactive code cells with Jupyter, Thebe, and Binder.
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
MyST-Parser - An extended commonmark compliant parser, with bridges to docutils/sphinx
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
Flask - The Python micro framework for building web applications.
heron
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.