Distributed Training with JAX and Flax

As deep learning models grow larger, training them on a single accelerator becomes impractical. Understanding how to distribute workloads efficiently across multiple GPUs and TPUs is now an essential skill for modern machine learning practitioners. This text-based course guides you through the core principles of distributed training using JAX and Flax, taking you from single-device code to highly scalable, parallelized training pipelines. What you'll learn: - Understand the foundational concepts of JAX, including pure functions, JIT compilation, and automatic differentiation. - Configure multi-device environments to run parallel computations across multiple GPUs and TPUs. - Apply modern JAX sharding APIs to distribute data and model parameters effectively. - Build scalable neural network architectures using the Flax Linen API. - Implement data-parallel and model-parallel training loops from scratch. - Debug and optimize distributed performance to minimize communication overhead. You will start with key terminology and the foundational architecture of JAX and Flax before exploring step-by-step code implementations. Through structured written explanations and clear code snippets, you will learn how to transition single-accelerator training pipelines into highly efficient distributed systems. This course is designed for machine learning developers, data scientists, and researchers who are new to distributed training. A basic understanding of Python and neural networks is recommended, but no prior experience with JAX or Flax is required. Start reading today to unlock the power of high-performance distributed machine learning.