Jax Training Recipe

JAX Training Recipe

JAX is not PyTorch with different syntax. Functions are pure, state is explicit, and transformations (jit, vmap, grad, pmap) compose only when the function boundaries are clean.

For concrete config/checklist details, read references/training-recipe-details.md.

Intake

Specify:

• task, dataset, loss, and metric;
• target device: CPU, single GPU, multi-GPU, TPU;
• model framework: Flax/Linen, Equinox, or raw JAX;
• randomness needs: dropout, sampling, augmentation;
• state needs: batch norm, optimizer state, EMA, checkpointing.

Workflow

1. Define the experiment and minimal data batch.
2. Plan PRNG key ownership and splitting.
3. Define model/state boundaries.
4. Initialize model and optimizer.
5. Write a pure train_step.
6. Add jit only after the un-jitted step works.
7. Add vmap/pmap only after single-device behavior is correct.
8. Add checkpointing and restore tests.
9. Run first-run safety checks.
10. Stage rollout from tiny batch to full training.

JAX-Specific Rules

• Never reuse a PRNG key.
• Keep state explicit; do not hide mutable state in closures.
• Avoid Python side effects inside jit.
• Treat shape changes under jit as design decisions.
• Debug without jit first, then re-enable transformations one at a time.
• Replicate state deliberately for multi-device training.

Common Failure Modes

• Reused PRNG keys causing repeated dropout/sampling.
• Tracer errors from Python control flow or side effects.
• Batch norm/dropout state not separated from params.
• Shape mismatch under vmap or pmap.
• NaNs hidden by compiled loops.
• Checkpoints missing optimizer or batch-stat state.

Output

Return:

• experiment summary;
• PRNG strategy;
• model/state design;
• train/eval step plan;
• minimal config;
• first-run checks;
• JAX-specific failure modes;
• scale-up plan.