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name: serialization-and-distribution description: "Serialize/export Sonnet modules and reason about TensorFlow distribution, mixed precision, and backend limits." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Serialization and Distribution

Use this sub-skill for TensorFlow checkpoints, SavedModel export/load, pickle or Keras interoperability caveats, XLA, mixed precision, and snt.distribute helpers.

Start here

• references/serialization.md: checkpoint, SavedModel, Keras/pickle, XLA, and mixed precision recipes.
• references/distribution-and-backends.md: Replicator, TpuReplicator, CrossReplicaBatchNorm, and backend limits.
• references/troubleshooting.md: restored object identity, SavedModel signatures, cross-replica context, and accelerator-runtime failures.
• scripts/serialization_smoke.py: CPU checkpoint and SavedModel smoke.

Boundaries

• Module construction and lazy variables: ../module-authoring/SKILL.md.
• Training-loop checkpoint timing: ../training-and-optimization/SKILL.md.
• Functional transforms and device helpers: ../functional-transforms/SKILL.md.

Backend policy

CPU verifies checkpoints, SavedModel export/load, pickle caveats, and mixed precision policy mechanics. CUDA, TPU, XLA performance, and distributed replicas require a matching TensorFlow runtime. Do not claim accelerator verification from a CPU-only run.