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Getting Started with JAX

Chapter 1: Introduction to JAX

Core Design Philosophy: Function Transformations

Installation and Setup

Working with JAX Arrays

Device Management: CPU, GPU, TPU

Practice: Basic Array Operations

Quiz for Chapter 1

Chapter 2: Accelerating Functions with JIT Compilation

The Need for Speed: Why Compile?

Introducing jax.jit

How JIT Works: Tracing and Compilation

Python Control Flow and jit

Static vs Traced Values

Common Challenges with jit

Hands-on Practical: Applying jit

Quiz for Chapter 2

Chapter 3: Automatic Differentiation with grad

Understanding Gradients

Introducing jax.grad

How Autodiff Works: Reverse Mode

Differentiating with Respect to Arguments

Higher-Order Derivatives (grad of grad)

Value and Gradient (jax.value_and_grad)

Differentiation and Control Flow

Limitations and Considerations

Hands-on Practical: Computing Gradients

Quiz for Chapter 3

Chapter 4: Automatic Vectorization with vmap

The Concept of Vectorization

Introducing jax.vmap

Mapping over Specific Arguments (in_axes, out_axes)

Handling Multiple Batched Arguments

Combining vmap with jit and grad

Performance Considerations for vmap

Hands-on Practical: Vectorizing Functions

Quiz for Chapter 4

Chapter 5: Parallelization Across Devices with pmap

Introduction to Data Parallelism (SPMD)

Introducing jax.pmap

Mapping Data to Devices (in_axes, out_axes)

Device Meshes and Axis Names

Collective Operations (lax.psum, lax.pmean, etc.)

Combining pmap with other Transformations

Debugging pmapped Functions

Hands-on Practical: Parallel Computation

Quiz for Chapter 5

Chapter 6: Managing State in JAX

Functional Purity and Side Effects

The Challenge of State in Functional Code

Pattern: Explicit State Passing

Using PyTrees for Structured State

Example: Stateful Counter

Example: Simple Optimizer State

Combining State Management with Transformations

Practice: Implementing Stateful Functions

Quiz for Chapter 6

Getting Started with JAX

Learn JAX for high-performance numerical computation and machine learning research. This course covers JAX fundamentals, including its NumPy API, function transformations like jit, grad, vmap, and pmap, and functional programming patterns for managing state. Gain practical experience accelerating and differentiating Python code for modern hardware (GPUs/TPUs).

Prerequisites Python and NumPy proficiency

Level:

Intermediate

JAX Fundamentals
Understand the core concepts of JAX, its relationship with NumPy, and its functional programming approach.
Function Transformations
Apply JAX's key transformations: jit for compilation, grad for automatic differentiation, vmap for vectorization, and pmap for parallelization.
High-Performance Code
Write JAX code that effectively utilizes modern accelerators like GPUs and TPUs.
Automatic Differentiation
Compute gradients of Python functions automatically using grad.
State Management
Implement stateful computations using functional programming patterns suitable for JAX.
Debugging and Profiling
Identify common pitfalls and basic techniques for debugging JAX code.

There are no prerequisite courses for this course.

There are no recommended next courses at the moment.

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