The Pivot Point: Deploying Two Instances and the Calm Before the OOM Storm

At first glance, message [msg 1019] in this opencode session appears almost mundane: a simple status check confirming that two cloud instances have been created and are running. The assistant reports:

Both created: - 32711932 — host 93197, 2x RTX 3090, BC Canada - 32711934 — host 88910, 1x RTX 4090, Norway

>

Wait for them to start, then monitor: [bash] sleep 60 && ssh 10.1.2.104 "vastai show instances --raw 2>/dev/null" | jq -c '.[] | {id, host_id, gpu_name, num_gpus, status_msg: .status_msg, actual_status, ssh_port: .ports["22/tcp"][0].HostPort, public_ipaddr}' {"id":32711932,"host_id":93197,"gpu_name":"RTX 3090","num_gpus":2,"status_msg":"success, running theuser/curio-cuzk_latest/ssh","actual_status":"running","ssh_port":"48237","public_ipaddr":"70.69.192.6"} {"id":32711934,"host_id":88910,"gpu_name":"RTX 4090","num_gpus":1,"status_msg":"success, running theuser/curio-cuzk_latest/ssh","actual_status":"running","ssh_port":"41122","public_ipaddr":"141.195.21.87"}

But this message is far from ordinary. It is the quiet pivot point of an entire deployment saga — the moment when a long chain of debugging, script hardening, and platform-specific workarounds finally converges into a clean redeployment. It is also the calm before the next crisis: within minutes, one of these instances will be killed by the OOM killer, revealing a fundamental resource constraint that will shift the entire project's focus from deployment logistics to hardware-aware pipeline configuration.

The Road to This Message

To understand why this message matters, one must appreciate the journey that led here. The assistant had been wrestling with a cascade of failures across multiple layers of a distributed Filecoin proving system. The infrastructure involved a custom Docker image (curio-cuzk) running on rented GPU instances from Vast.ai, managed by a central vast-manager service that tracked instance lifecycle states — registered, params_done, benchmarking, ready — through a SQLite database.

The problems were legion. First, the VAST_CONTAINERLABEL environment variable — the key mechanism by which instances identify themselves to the manager — was invisible in SSH sessions because Vast.ai injects it as a non-exported shell variable rather than a true environment variable. This caused the entrypoint script to abort with "FATAL: VAST_CONTAINERLABEL not set." The fix required switching from relying on Docker's ENTRYPOINT directive (which gets replaced by Vast's init script in --ssh mode) to using --onstart-cmd to launch the entrypoint in the background.

Second, the vast-manager monitor had a critical matching bug: it identified instances solely by the label field returned by the Vast API, but this field remains null unless explicitly set via vastai label. This caused the monitor to incorrectly kill newly created instances. The fix introduced an ID-based fallback map (idMap) that extracts the Vast instance ID from the C.<id> label pattern.

Third, the benchmark pipeline itself was fragile. The warmup proof — the first proof run after starting the CUZK daemon — required Pre-Compiled Constraint Evaluator (PCE) extraction, a computationally expensive process that could take several minutes. During this time, the gRPC client connection would time out, producing a "transport error" that, combined with set -e in the shell scripts, aborted the entire benchmark. The fix involved hardening both benchmark.sh and entrypoint.sh to allow warmup failures without aborting the pipeline.

By the time we reach [msg 1019], the assistant has pushed a fixed Docker image (theuser/curio-cuzk:latest), destroyed the old broken instances (32710471 on host 93197, 32705217 on host 88910), cleaned stale database entries, and is now creating fresh instances with the corrected configuration.

Decisions Made in This Message

The message itself contains several implicit decisions that reveal the assistant's reasoning. First is the choice of which Vast offers to use. After searching available offers on the two target hosts ([msg 1016]), the assistant selected:

--ssh --direct --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'

This is a carefully constructed incantation. The --ssh flag enables SSH access (essential for debugging). The --direct flag bypasses Vast's proxy. The --onstart-cmd runs the entrypoint in the background via nohup, allowing Vast's own init script to also run (providing SSH) while the entrypoint manages the CUZK daemon, benchmark, and Curio lifecycle. The environment variables for PAVAIL and PAVAIL_SERVER are passed to configure the port availability tunnel that connects the instance to the management service.

Assumptions and Their Consequences

The message embodies several assumptions, some of which will prove incorrect within minutes. The primary assumption is that both instances, having been created with the same Docker image and configuration, will behave identically. The assistant's monitoring command — a simple sleep 60 followed by a status query — treats the two instances as symmetric deployments.

This assumption ignores the fundamental hardware difference between the two: 125GB RAM versus 500GB RAM. The BC Canada instance, with its 2x RTX 3090s, has only a quarter of the memory of the Norway RTX 4090. As the next messages in the conversation will reveal, this difference is catastrophic. When the benchmark begins its warmup proof on the BC instance, the uncached PCE synthesis causes organic memory allocation that exceeds 125GB, triggering the OOM killer. The Norway instance, with its 500GB RAM, completes the warmup without issue.

The assistant also assumes that the fixes applied to the benchmark scripts — allowing warmup failures without aborting — are sufficient to handle all failure modes. But an OOM kill is not a graceful gRPC timeout; it terminates the entire daemon and all associated processes. The benchmark cannot retry because there is nothing left to retry.

The Thinking Process Visible in the Message

The structure of the message reveals the assistant's operational mindset. After creating the instances, the assistant immediately sets up a monitoring loop: sleep 60 seconds (to allow boot time), then query the Vast API for status. The choice of fields in the jq query is telling: id, host_id, gpu_name, num_gpus, status_msg, actual_status, ssh_port, public_ipaddr. These are the minimum set needed to confirm that an instance is reachable and ready for further interaction. The assistant is thinking in terms of a deployment pipeline: create, verify reachability, then proceed to deeper checks.

The fact that the assistant queries the Vast API (via SSH to the manager host at 10.1.2.104) rather than directly SSHing to the instances shows a preference for centralized monitoring. The manager is the single source of truth for instance state. This is consistent with the architecture built throughout the session: the vast-manager service tracks all instances through their lifecycle, and the assistant works through it rather than ad-hoc.

Input Knowledge Required

To fully understand this message, one needs knowledge of several domains. The Vast.ai platform model — that instances are rented GPU machines with Docker support, that --ssh mode replaces the Docker ENTRYPOINT, that --onstart-cmd provides a workaround — is essential. The Filecoin proving pipeline — CUZK daemon, PCE extraction, partition workers, benchmark proofs — provides the context for why these instances are being created. The vast-manager architecture — SQLite state database, lifecycle states, label-based identification — explains the monitoring approach. And the earlier debugging session, particularly the VAST_CONTAINERLABEL fix and the benchmark hardening, explains why the creation command uses --onstart-cmd rather than relying on the Docker ENTRYPOINT.

Output Knowledge Created

This message creates the knowledge that two specific instances exist at specific network locations with specific hardware configurations. This is operational knowledge — the foundation for all subsequent monitoring, debugging, and resource tuning. The message also implicitly documents the correct creation recipe for future instances: the combination of --ssh --direct --onstart-cmd with the environment variables for PAVAIL connectivity. Any future redeployment will follow this template.

The Deeper Significance

[msg 1019] is the moment when the system transitions from reactive debugging to proactive deployment. The assistant has fixed the known bugs, hardened the scripts, and is now pushing fresh instances into production. The message radiates a sense of completion — the todos are checked off, the old instances are destroyed, the new ones are spinning up.

But the message is also a trap. The very success of the creation — both instances starting, both showing "running" status — creates a false sense of resolution. The real problem, the OOM killer, is invisible at this level of monitoring. The Vast API reports "success, running" because the Docker container started and the entrypoint began executing. It does not report that the benchmark will exhaust memory minutes later.

This is the classic challenge of cloud deployment: surface-level health checks (container running, SSH accessible) do not guarantee workload-level success. The assistant's next actions — checking the entrypoint logs, discovering the OOM, and pivoting to hardware-aware configuration — will reveal the gap between deployment and operation.

In the broader narrative of this segment, [msg 1019] serves as the hinge. Everything before it is about fixing the deployment pipeline. Everything after it is about tuning the workload to match hardware constraints. The message itself is the moment of transition, the quiet center of the storm.