Accelerating Scientific Python: JAX, Numba, and Ray in Action

Materials for the Accelerating Scientific Python: JAX, Numba, and Ray in Action tutorial at EuroPython 2025.

Preparation with Google Colab

We will use Google Colab for the tutorial. Colab provides a ready-to-use Jupyter notebook environment with all the necessary libraries installed. And it provides free GPU and TPU access, which is crucial for the tutorial.

You will need to have a (free) Google account to use Colab. Please create one if you don't have one yet.

Please click the Open in Colab button below to open the preparation notebook directly in Google Colab:

(raw link: https://colab.research.google.com/github/coobas/europython-25/blob/main/01-preparation.ipynb)

The code can run on other environments, e.g. on your local machine, though we recommend using Google Colab as we cannot provide support for local environment issues.

Tutorial Notebooks

1. Optimisation with JAX and Numba: Directly Open in Colab
2. JAX on GPU: Directly Open in Colab
3. Parallelisation with Ray: Directly Open in Colab
4. Gradient-based optimisation: Directly Open in Colab