Master advanced techniques in JAX for high-performance machine learning. This course covers JAX's internals, performance optimization strategies for GPUs and TPUs, distributed computing with pmap, advanced automatic differentiation, custom operations, and techniques for training large-scale models. Build sophisticated and efficient ML systems using JAX's functional programming and compilation capabilities.
Prerequisites Python, ML, Basic JAX
Level:
Performance Optimization
Profile and optimize JAX code for maximal performance on accelerators like GPUs and TPUs.
Distributed Computing
Implement data and model parallelism using pmap and other distributed primitives for multi-device execution.
Advanced Transformations
Utilize complex control flow primitives (scan, cond, while_loop) and understand their interaction with JAX transformations.
Custom Autodiff Rules
Define custom vector-Jacobian products (VJPs) and Jacobian-vector products (JVPs) for non-standard operations.
JAX Internals
Gain a deeper understanding of JAX's compilation process (XLA) and internal representations (jaxprs).
Large-Scale Training
Apply advanced JAX patterns and libraries (like Flax or Haiku) for training large neural networks efficiently.
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