LESSON 4.9 · Engineering · 120 min
Training observability and recovery
Training observability and recovery. Learn loss spikes, stragglers, checkpoints, and data drift and complete: Design a training runbook. Part of the “Turn Training into a System” l
Learning objectives
- Explain what problem “Training observability and recovery” solves without hiding behind terminology.
- Trace the variables and causal links across loss spikes, stragglers, checkpoints.
- Complete “Design a training runbook” and judge the result with evidence rather than intuition.
Core concepts
loss spikes
loss spikes is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment before moving to an optimized implementation.
stragglers
stragglers is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
checkpoints
checkpoints is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
and data drift
and data drift is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
Build and verify
Design a training runbook
- Predict: write the expected output, trend, or failure before running code.
- Build: implement only the minimum components needed to answer the question.
- Verify: compare with a baseline or trusted implementation; save seeds, parameters, and raw outputs.
- Transfer: change one shape, dataset, scale, or workload condition and explain whether the conclusion still holds.