Optimized JAX

Basic idea

JAX lets you write NumPy-style Python code and run it fast on GPUs without writing CUDA. It does this by:

• NumPy on accelerators: Use jax.numpy just like NumPy, but arrays live on the GPU.
• Function transformations:
  • jit → Compiles your function into fast GPU code
  • grad → Gives you automatic differentiation
  • vmap → Vectorizes your function across batches
  • pmap → Runs across multiple GPUs in parallel
• XLA backend: JAX hands your code to XLA (Accelerated Linear Algebra compiler), which fuses operations and generates optimized GPU kernels.

What you'll accomplish

You'll set up a JAX development environment on NVIDIA Spark with Blackwell architecture that enables high-performance machine learning prototyping using familiar NumPy-like abstractions, complete with GPU acceleration and performance optimization capabilities.

What to know before starting

• Comfortable with Python and NumPy programming
• General understanding of machine learning workflows and techniques
• Experience working in a terminal
• Experience using and building containers
• Familiarity with different versions of CUDA
• Basic understanding of linear algebra (high-school level math sufficient)

Prerequisites

• NVIDIA Spark device with Blackwell architecture
• ARM64 (AArch64) processor architecture
• Docker or container runtime installed
• NVIDIA Container Toolkit configured
• Verify GPU access: nvidia-smi
• Port 8080 available for marimo notebook access

Ancillary files

All required assets can be found here on GitHub

• JAX introduction notebook — covers JAX programming model differences from NumPy and performance evaluation
• NumPy SOM implementation — reference implementation of self-organized map training algorithm in NumPy
• JAX SOM implementations — multiple iteratively refined implementations of SOM algorithm in JAX
• Environment configuration — package dependencies and container setup specifications

Time & risk

• Duration: 2-3 hours including setup, tutorial completion, and validation
• Risks:
  • Package dependency conflicts in Python environment
  • Performance validation may require architecture-specific optimizations
• Rollback: Container environments provide isolation; remove containers and restart to reset state.