Operate JAX workloads

Who this path is for

Engineers interested in reproducibility, failure recovery and operating accelerator workloads.

Preparation

Shared foundations and a runnable training system

Your first useful artifact

An interrupted CPU run whose next data batches, losses and full state match uninterrupted execution.

How to study

First reproduce the next update after a local interruption. Then inspect workload traces and placement so an incident has interpretable signals. Work through the operations project’s process, event, restore, release and capacity stages. Keep a runbook naming the accepted checkpoint and rollback decision; validate an actual cluster separately before claiming accelerator operations experience. After the deployment bridge, complete engineering-release to connect MLflow, a verified container and application/release policy. Keep local Docker evidence separate from cloud and accelerator qualification.

Course phases and lessons

1. Setup & first steps

   Run a saved program and explain its environment, array output and device placement. Start on one CPU, practice sharding on four logical CPU devices, and learn how to get a TPU running, launch a TPU job, and run course experiments on TPU in parallel from Phase 00 onward.

   • Set up your learning workspace
   • Meet your arrays and devices
   • Practice with four virtual CPU devices
   • Move your experiment to a TPU
2. Arrays & pure functions

   Treat every axis as part of a contract. Work through normalization, broadcasting and immutable updates, then diagnose mistakes that still return plausible numbers.

   • From NumPy to jax.numpy
   • Shapes, broadcasting, and dtypes
   • Pure functions and explicit inputs
   • Immutable updates and indexing
3. JAX transformations

   Begin with a scalar function, then differentiate it, batch it and compile it. Compare each transformed result with a simple reference so you can explain what changed.

   • Your first gradient
   • Losses and value_and_grad
   • Batch a function with vmap
   • Compile a function with jit
   • Tracing, static arguments, and recompilation
4. State, randomness & control flow

   Make randomness, parameters and loop state explicit. Learn how to repeat an experiment without accidentally reusing randomness or mutating a caller’s state.

   • Random keys without surprises
   • Pytrees and structured parameters
   • Compiled loops with lax.scan
   • Branches with lax.cond
5. Math & optimization

   Connect geometry and probability to the updates in a training loop. Begin with linear algebra, gradient checks and gradient descent, then investigate conditioning, curvature, likelihoods, regularization and optimizer behavior.

   • Vectors, norms, and projections
   • Linear algebra and loss intuition
   • Least squares, rank, and conditioning
   • The chain rule, Jacobians, and directions
   • Gradient checking and numerical accuracy
   • Curvature, Hessians, and learning rates
   • Write gradient descent yourself
   • Probability, likelihood, and stable losses
   • Regularization and honest validation
   • Minibatches, expectation, and gradient noise
   • Optimize with Optax
   • Adam, clipping, and learning-rate schedules
6. Neural network training

   Build the same ideas into small neural networks. Understand hidden layers, parameter state, stable objectives and evaluation before moving to a small image classifier.

   • Build a tiny multilayer perceptron
   • Model and state with Flax NNX
   • A compiled train and evaluation step
   • Train a CNN on a small dataset
   • Debug unstable learning
7. Data & checkpoint recovery

   Follow examples from the dataset into a training update, then save enough information to reproduce the next update after interruption. Track experiment configuration, data identity and metric histories with MLflow alongside complete checkpoints.

   • Design an input pipeline
   • Load, batch, and checkpoint order with Grain
   • Save model, optimizer, and random state
   • Recover data position and resume
   • Asynchronous checkpoints and failure boundaries
   • Track experiments and lineage with MLflow
8. Performance diagnosis

   Measure completed work, inspect a real profiler trace and relate compiler output to memory traffic. Compare rematerialization choices while separating saved residuals, compiler memory estimates and observed latency.

   • Benchmark asynchronous work correctly
   • Diagnose recompilation and host synchronization
   • Read an XProf trace
   • Reason with HLO and the roofline model
   • Memory, rematerialization, and optimization tradeoffs
9. Distributed training

   Partition arrays and training updates, compare all-reduce with reduce-scatter, and restore a complete sharded state across an epoch boundary. Verify CPU placement and arithmetic before separate cluster validation.

   • Arrays, meshes, and sharding
   • A sharded training step
   • Communication-efficient algorithms
   • Resilient distributed training
10. Deployment, interoperability & edge AI

    Adapt a trained model, verify framework and export contracts, compare precision policies and measure local inference before planning server or edge capacity. Package a model service in a reproducible container and verify readiness, artifact identity and request contracts.

    • Adapt a pretrained model and choose a post-training objective
    • Move models between JAX, Keras, TensorFlow and PyTorch
    • Convert PyTorch weights to Flax and locate numerical errors
    • Export a computation and verify its serving contract
    • Choose weight, activation and accumulation precision
    • Containerize a model service and verify its boundary
    • Inference capacity, batching, and autoscaling
    • Deploy at the edge: conversion, budgets and device checks
11. Workload operations

    Launch and observe real local jobs, detect failures and stalls, restore complete training state, verify artifact quality and rehearse rollback before planning capacity. Connect MLOps data/release gates, LLMOps evaluation/traces and ModelOps ownership/approval to the workload runbook.

    • Accelerator jobs and runtime lifecycle
    • Health, logs, and workload observability
    • Failure recovery and operational runbooks
    • Changes, rollback, and artifact provenance
    • Capacity, utilization, and operating cost
    • MLOps: data contracts, CI gates and monitoring
    • LLMOps: version prompts, evaluate behavior and trace requests
    • ModelOps: own, approve, roll back and retire a model

An observable, restartable local workload

A guided project is available for this route.

Diagnose real local subprocess failures and timeouts, restore the accepted complete state, reject a degraded artifact and defend rollback and capacity decisions. Accelerator and cluster operation require additional target evidence.

• Versioned configuration, structured progress signals and an incident timeline
• A controlled failure followed by a verified restore of the next update
• An accepted artifact, rollback criteria and capacity calculations separating measurements from assumptions

Read the portfolio project guide

Work through the synthesis assessment

Use this path in your learning workspace