ai-experiments

How to launch, resume, and serve model training runs on this stack — checkpoint layout, the memory-fit calculator, and the train-and-serve round trip. Skeleton reference; grows once the training tier lands.

Container debugging runbook for this inference stack — per-service healthcheck endpoints, common CUDA/driver failure signatures, log locations, and the safe restart procedure. Use when a service is unhealthy or failing to start.

The reproducible benchmark protocol for this workstation — which entry points to run, warm-up policy, how results are recorded and compared, and what counts as a regression. Use before reporting any latency/throughput/memory number.

RunPod endpoint operations runbook — checking worker state, forcing scale-down, reading the spend query, and cold-start expectations. Use before and after cloud inference sessions. Forward-looking — RunPod tooling lives outside this repo.