The Moment of Deployment: Checking the OOM Fix in Production

In the midst of a sprawling coding session dedicated to building a distributed GPU proving system for Filecoin's Curio/cuzk stack, message [msg 1136] arrives as a quiet pivot point. It is deceptively brief — two SSH commands and a simple declarative sentence — but it marks the transition from debugging to deployment, from fixing to validating. After an arduous sequence of OOM (Out of Memory) crashes, code rewrites, Docker rebuilds, and lifecycle fixes, the assistant is now checking whether the hardened system actually works on real hardware.

The Message

The message reads in full:

Both running. Let me check their progress:

>

[bash] ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -p 33896 root@ssh1.vast.ai 'echo "=== Czechia 2x RTX 3090 ==="; tail -10 /var/log/entrypoint.log 2>/dev/null || echo "No log yet"; ps aux | grep -v grep | grep -E "entry|portavail|curio|cuzk|fetch" | head -5' 2>/dev/null

>

[bash] ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -p 33902 root@ssh6.vast.ai 'echo "=== Belgium 2x A40 ==="; tail -10 /var/log/entrypoint.log 2>/dev/null || echo "No log yet"; ps aux | grep -v grep | grep -E "entry|portavail|curio|cuzk|fetch" | head -5' 2>/dev/null

Two parallel SSH probes, dispatched simultaneously, targeting two freshly created Vast.ai cloud instances in different geographic regions. The assistant is performing a health check on the just-deployed infrastructure, looking for signs that the entrypoint script has started executing, that the portavailc tunnel is active, that the daemon is running. The brevity of the message belies the weight of what it represents.

The OOM Saga That Led Here

To understand why this message matters, one must understand the crisis that preceded it. The session (Segment 8 of the broader conversation) had been consumed by a single, maddening problem: GPU instances with insufficient RAM were being killed by the operating system's OOM killer during the PoRep proving benchmark. The BC Canada instance — a 2x RTX 3090 machine with only 125GB of container-visible RAM — died repeatedly during warmup. The Norway instance, a single RTX 4090 with 500GB RAM, survived but produced a benchmark rate of 41.32 proofs/hour, falling below the 50 proofs/hour minimum threshold, triggering yet another lifecycle kill.

The root cause was a concurrency mismatch. The entrypoint script used a hardcoded BENCH_CONCURRENCY=5, meaning five proofs would run simultaneously. Each proof with partition_workers=10 consumed roughly 30–40GB of RAM during synthesis. Five concurrent proofs meant 150–200GB of demand, plus the SRS parameters (44GB), the PCE cache (26GB), and OS overhead. On a 125GB machine, this was catastrophic. On a 376GB machine, it was merely dangerous.

The assistant traced the problem through multiple layers. First, the warmup phase triggered PCE (Pre-Compiled Constraint Evaluator) extraction, which itself was memory-intensive. The fix in benchmark.sh detected the absence of a PCE cache and started the daemon with partition_workers=2 for the warmup proof, preventing the memory spike of simultaneous partition synthesis. After the PCE file was generated, the daemon restarted with the full partition count for the actual benchmark.

Second, entrypoint.sh was rewritten to dynamically scale benchmark concurrency based on available RAM and GPU count. The formula was carefully calibrated: it reserved 100GB as overhead for SRS/PCE/OS, then estimated memory per proof as partition_workers * 3 (later refined to * 4), and capped concurrency at num_gpus * 3 to prevent GPU oversubscription. For a 376GB machine with 2 GPUs and partition_workers=10, this yielded concurrency=6. For a 125GB machine, it yielded concurrency=1 — conservative but safe.

Third, a lifecycle bug was fixed in the vast-manager's handleBenchDone endpoint so that instances failing the benchmark were immediately destroyed via vastai destroy, preventing wasted expenditure on underperforming hardware.

The Strategic Bet: Two Instances, Two Tiers

The assistant's decision to create two instances simultaneously reveals strategic thinking. The Czechia instance (2x RTX 3090, listed RAM 251GB, $0.282/hr) was the budget option — cheap multi-GPU proving that could potentially meet the 50 proofs/hour threshold. The Belgium instance (2x A40 with 48GB VRAM each, 1TB RAM, $0.574/hr) was the premium reference — abundant memory guaranteed to avoid OOM, serving as a reliability baseline.

This parallel deployment is itself a form of experimentation. The assistant doesn't know which instance will succeed. The Czechia machine might OOM if the container's actual RAM limit is closer to the listed 251GB than the 376GB seen on similar hosts. The Belgium machine, with 1TB RAM, should be safe — but its A40 GPUs might have different performance characteristics. By launching both simultaneously, the assistant can compare results and gather data about real-world proving performance across different hardware configurations.

Assumptions Embedded in the Check

The SSH commands encode several assumptions. The assistant assumes the entrypoint script has started executing and written to /var/log/entrypoint.log. It assumes the relevant processes (entry, portavail, curio, cuzk, fetch) will appear in ps aux if the system is functioning correctly. It assumes the instances are reachable via the standard Vast.ai SSH gateway (ssh1.vast.ai and ssh6.vast.ai). It assumes the Docker image has been pulled and the container is running.

These are reasonable assumptions, but they are not guaranteed. The previous US instance (host 249079) had become completely unreachable after its OOM crash — the container was killed entirely, and vastai show instances returned an empty list. The assistant is checking early, before the benchmark begins, to catch any immediate startup failures.

The Thinking Process Visible in the Message

The message reveals a methodical, almost surgical approach to debugging. The assistant does not simply wait and hope — it actively probes. The two SSH commands are structured identically, allowing side-by-side comparison of the two instances. The grep -E pattern is carefully chosen to catch any of the key processes without false positives. The head -5 limits output to avoid flooding the conversation. The 2>/dev/null redirections suppress SSH noise.

This is the thinking of someone who has been burned by silent failures. The assistant has learned that instances can fail silently — OOM-killed, network-partitioned, or stuck in startup. The only way to know is to ask. And the only way to ask is to SSH in and look at the logs.

What Happens Next

The next message ([msg 1137]) reveals that both SSH commands returned empty — no log output, no process listings. The assistant correctly interprets this as "still starting up" (likely pulling the Docker image) and waits longer. The instances do eventually come online, but they will face new challenges: the Belgium instance will be killed by the manager's 20-minute benchmark timeout, and the Czechia instance will suffer a gRPC transport error on its first batch proof. The OOM fix is validated, but new failure modes emerge.

This is the nature of distributed systems debugging. Each fix reveals the next problem. The assistant's response — to increase the benchmark timeout, add a post-restart warmup proof, and refine the partition worker logic — demonstrates the iterative, data-driven approach that characterizes the entire session.

Conclusion

Message [msg 1136] is a moment of transition. It is the first breath after a long underwater swim — the assistant has surfaced from the depths of OOM debugging and is now checking whether the air is breathable. The two SSH commands are simple, but they carry the weight of the entire preceding effort: the code fixes, the Docker builds, the lifecycle management, the hardware-aware configuration. This message is the point where theory meets practice, where the fixes written in code are tested against the messy reality of cloud GPU instances. It is a small message, but it sits at the crux of the entire segment.