snakemake VS fastapi

Last year, my pull requests were on my own projects: two to for my Apple Watch telemetry recording app, one was for a workout Watch app, and the third was for the research project I was working on at the moment (this paper has since been published, "The origins and genetic interactions of KRAS mutations are allele- and tissue-specific" and the code is open source). This year, in order to enhance my learning, I challenged myself to contribute to others' projects. In my opinion, I have been quite successful with contributions to Fig, tldr, snakemake, and pymc3-examples. In addition, I have also taken up the challenge of learning about Appwrite, one of the sponsors of this year's Hacktoberfest, and producing educational content about the service. (With some encouragement by the offer of free stickers 🙃) I found this a great opportunity to learn about an essential world of programming that I had yet to deal with: backend services.

If you’re into data science, have you taken a look at Snakemake? I came across this quite recently, and now, I’m wondering why I hadn’t used it before (it really would’ve come in handy to automate a few data-wrangling processes).

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

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.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

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

Fast API.

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: