Checkpointing Like a Pro: Designing AI Workflows for Pre-emption

Pre-emption terrifies teams that were raised on fixed instances and long-running processes. In micro-compute environments, pre-emption is a design assumption, not a failure mode. The key is systematic checkpointing.

Principles

• Make progress atomic. Save state at natural task boundaries: per epoch, per N steps, per video chunk.

• Separate state from compute. Persist to durable storage independent of the worker (object store or replicated volume).

• Idempotent replays. On restart, detect the latest valid checkpoint and resume without side effects.

• Checksum and verify. Each checkpoint should include hashes for model weights, optimizer state, and data offsets.

• Metadata matters. Store runtime config (seed, LR schedule, SHA of code) alongside artifacts to guarantee reproducibility.

Reference patterns

• Training: save {weights, optimizer, dataloader position} every X minutes; validate on resume.

• Batch inference: chunk the queue; write success markers; retry only missing chunks.

• RAG pipelines: cache embeddings and retrieval indices; only recompute invalidated partitions.

Observability tips

• Expose checkpoint latency and wasted work metrics (redo minutes after pre-emption).

• Alert when redo > target (e.g., >5% of wall time).

• Keep receipts—verifiable usage proofs make finance comfortable with dynamic pricing.

Once checkpointing is boring, pre-emption stops being scary. You’ll take the cheaper capacity, accept occasional interruptions, and still finish sooner and cheaper.