Learn to run & execute Jupyter Notebooks as a REST API in this series.
How do we run Jupyter Notebooks as a Rest API?
This is a question that I have wanted to solve learning how powerful Jupyter notebooks can be for programmers of all backgrounds. The reason: automation.
I love using jupyter notebooks to prototype rapidly or to write various machine learning apps. What I don't love is needing to open them every time to run them. What's more, I often need to share output results from other notebooks and find the current infrastructure not conducive.
So what to do? Turn any jupyter notebook (aka ipynb) into an executable REST API endpoint that I can trigger (or use) anywhere in the world.
Enter REST APIs.
REST APIs have become a de facto way for software developers to build rich user experiences across a wide range of applications: websites, iot/edge devices, smart phones, tvs, and so much more. Think of them as a way for two software applications to easily communicate with each other.
Jupyter notebooks have become a de facto tool for data scientists everywhere. You can render graphs & charts, write & run code, display images, and take user input all within a simple GUI that works in the browser.
In this series, we're going to combine the two ideas to make a automation powerhouse.
- REST API
- Papermill
- FastAPI
- Jupyter
- Webhooks
- Streaming Notebook Cell Results