This repository provides an introduction to JAX, a high-performance numerical computing library from Google Research.

It is meant for practitioners that are already familiar with numpy and PyTorch.

• 00_from_zero_to_noob.ipynb: An introductory tutorial notebook that covers the essentials:

  • Numpy-style array operations
  • Automatic differentiation and optimization
  • Parallel execution with vmap
  • Introduction to PyTrees
  • Just-In-Time compilation with jit
  • Random numbers
  • Introduction to flax
  • Recurrent nets with scan

• 01_meta_learning_maml.ipynb Meta learning with MAML, implementation sketch

• 02_meta_learning_hypernet.ipynb Meta learning with Hypernets, implementation sketch

Worked out examples are available in the gallery folder.

1. Make sure you have Python installed
2. Install dependencies:

pip install jax jaxlib jupyter

3. Launch Jupyter Notebook:

jupyter notebook

4. Open 00_from_zero_to_noob.ipynb to begin learning JAX