Chunk 5.0

In this chunk, the assistant completed the Docker build and pushed it to Docker Hub, iterating through several build blockers: removing the Debian-managed `python3-pip` to fix the SPDK pip upgrade conflict, adding `/usr/local/cuda/lib64` to `LIBRARY_PATH` for the `libcudart_static.a` linker error, and installing missing runtime libraries (`libconfig++`, `libaio`, `libfuse3`, `libarchive`). The resulting image (`theuser/curio-cuzk:latest`, ~3GB) contains `curio`, `sptool`, `cuzk-daemon`, and `cuzk-bench` binaries with CUDA 13 supraseal support. The assistant also created `benchmark.sh` and `run.sh` scripts with configurable GPU/partition parameters, added `portavailc` tunnel support to the entrypoint, and fixed a spurious `StorageMetaGC` error in curio that occurred on snark-only clusters with no storage paths. The assistant then designed a comprehensive vast.ai management system documented in `vast-cuzk-plan.md`. This includes a Go-based management service on the controller host that assigns monotonically increasing runner IDs via SQLite, tracks instance state through registration → param fetch → benchmark → running lifecycle, and runs a background monitor that kills orphaned, timed-out, or underperforming instances using the vast CLI. The entrypoint was planned to auto-detect RAM (<400GB → 10 partition workers, else 16), run 12 benchmark proofs for pipeline warmup, and only proceed to start `cuzk` and `curio` if the benchmark rate exceeds the `MIN_RATE` threshold. The design emphasizes self-cleanup with specific timeouts (15min unregistered, 90min param fetch, 20min benchmark) and bad-host enforcement, ensuring the fleet manages itself without manual intervention.

From Container to Fleet: The Complete Engineering Journey of a CUDA Proving Stack 2284 words

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