Chunk 32.0

The assistant began executing the user's directive to test the budget-integrated pinned pool on a constrained memory machine before committing the code. A new Docker image (`theuser/curio-cuzk:latest`) was built and pushed, and a test instance was provisioned on vast.ai, though the initial attempt had a PAVAIL configuration issue. The user subsequently created several new instances (RTX PRO 4000, RTX 5090s) running the new image, shifting the focus toward operational stability. The focus rapidly shifted to a critical production failure: multiple nodes reported cuzk daemon crashes with no automatic recovery. The assistant diagnosed the supervisor loop in `entrypoint.sh`, identifying a fundamental reliability bug where `wait -n "$CUZK_PID" "$CURIO_PID"` blocked indefinitely in a `do_wait` syscall even after the cuzk process had fully exited, completely defeating the restart logic. Daemon logs across the crashed nodes showed no panics, CUDA errors, or OOM kills, suggesting abrupt external termination (likely GPU driver faults). Across the fleet, 4 of 6 running nodes were effectively dead, running only curio. The assistant implemented a permanent fix by replacing the unreliable `wait -n` with a robust polling loop using `kill -0`, and pushed a new Docker image with the fix. As a tactical measure, the cuzk daemon was manually restarted on the three crashed nodes (RTX PRO 4000, RTX 5090 new2, RTX 4090) to restore proving capacity immediately. The themes of this chunk highlight the brittleness of shell-based process supervision in production GPU workloads, the necessity of testing resource-constrained configurations, and the value of deep systems-level debugging to uncover subtle failure modes in distributed proving infrastructure.

From Memory Pools to Production Crashes: The Arc of a GPU Infrastructure Crisis 3538 words

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