The Narrow Window: Deploying OOM Fixes to a Running Benchmark Instance

Introduction

In the high-stakes world of GPU-accelerated proof generation for the Filecoin network, memory management is not merely a matter of efficiency—it is a matter of survival. When a proving node runs out of memory, the operating system's OOM killer terminates the process, wasting hours of computation and potentially disrupting the network's proof submission deadlines. Message <msg id=4019> captures a pivotal moment in the deployment of a comprehensive OOM mitigation strategy: the assistant SSHes into a live 256 GiB vast.ai instance to check its status, intending to deploy critical fixes before the benchmark begins its proving workload.

This message, seemingly a simple status check, is in fact a carefully timed reconnaissance operation. The assistant has just completed a multi-hour development cycle—writing a memprobe utility in C, adding an OOM recovery retry loop to benchmark.sh, updating entrypoint.sh to use empirically measured safety margins, and building/pushing a new Docker image (<msg id=4015> through <msg id=4018>). Now, the question is whether these fixes can reach the running instance before it crashes.

The OOM Crisis in Context

To understand why this SSH check matters, we must appreciate the problem it addresses. Throughout Segment 29 of the coding session, the assistant and user have been wrestling with a persistent OOM crash on memory-constrained vast.ai instances. The root cause was a perfect storm of factors:

  1. Pinned memory pool accounting gap: The CUDA pinned memory pool (PinnedPool) operated outside the MemoryBudget system. Buffers returned to the pool after GPU work were never freed from actual RSS, creating a massive discrepancy between what the budget tracked and what the kernel reported.
  2. Kernel and driver overhead: Hidden memory consumers—glibc arenas, page tables, GPU driver allocations—consumed cgroup budget invisibly. The system believed it had headroom when it had none.
  3. Transient SRS loading spikes: Simultaneous mmap operations combined with cudaHostAlloc calls created momentary memory demand spikes that exceeded the available budget. The 10 GiB safety margin that had been hardcoded was empirically insufficient. On a 342 GiB cgroup-limited instance, the system was operating at 99% of its limit with only 14 GiB of additional allocatable space and 6 GiB of kernel/driver overhead—zero headroom for any transient spike. The solution was two-pronged: a memprobe utility that empirically measures usable memory by allocating 1 GiB chunks until it nears the cgroup limit, providing a data-driven safety margin that accounts for hidden overhead; and an OOM recovery loop in benchmark.sh that catches exit code 137 (OOM kill), reduces the budget by 10%, and retries up to three times with a 30-second wait for kernel memory reclaim.

The Timing Window

The assistant's opening line—"Now let me deploy the fixed scripts to the running 256 GiB instance (32897009) and test. It's still downloading params, so we have time"—reveals a critical assumption about timing. The assistant believes the instance is still in its parameter download phase, which would provide a window to deploy updated scripts before the proving workload begins.

The Filecoin proving system requires approximately 100 GB of parameters (the "proving parameters" or "params") to be downloaded before any proof generation can occur. On vast.ai instances, this download happens at startup and can take 20-30 minutes depending on network conditions. The assistant's reasoning is sound: if the instance is still downloading, there is time to SSH in, update the entrypoint and benchmark scripts, and ensure the new OOM protections are in place before the first proof is attempted.

However, the SSH output reveals a more nuanced reality. The log timestamps show:

[entrypoint] 09:19:52 Proving parameters not found — fetching 32GiB params...
[entrypoint] 09:19:52 This will download ~100GB and may take a while.
[entrypoint] 09:41:52 Parameter fetch complete.
[entrypoint] 09:41:52 Signaled param-done
[entrypoint] 09:41:52 Starting benchmark: 10 proofs, concurrency 4

The parameter download completed at 09:41:52, and the benchmark started at the same second. The 159 GB of parameters already on disk confirms the download is complete. The assistant's assumption that "it's still downloading params" is technically incorrect—the download finished, and the benchmark has just launched.

This creates a subtle but important tension. The window hasn't closed entirely—the benchmark just started, so there may still be time to intervene before the first OOM crash—but it is far narrower than the assistant anticipated. The message captures this moment of discovery: the assistant expected to find a system still in its setup phase but instead found one already racing toward its first proving attempt.

What the SSH Output Reveals

The SSH command itself is carefully constructed to gather maximum information in a single invocation:

ssh -o StrictHostKeyChecking=no -p 17008 root@ssh6.vast.ai \
  "grep '^\[entrypoint\]' /tmp/setup.log | tail -5; echo '==='; \
   du -sh /var/tmp/filecoin-proof-parameters/ 2>/dev/null"

The first command (grep '^\[entrypoint\]' /tmp/setup.log | tail -5) extracts the last five entrypoint log lines, showing the most recent state transitions. The second (du -sh /var/tmp/filecoin-proof-parameters/) reports the disk usage of the parameter cache, confirming whether the download truly completed.

The output provides several pieces of actionable intelligence:

Assumptions and Their Consequences

Beyond the timing assumption, the message rests on several other implicit assumptions:

That SSH access will remain stable: The assistant uses StrictHostKeyChecking=no, accepting the risk of man-in-the-middle attacks for operational convenience. This is a pragmatic choice for cloud instances where host keys change frequently, but it reflects the operational reality of managing remote GPU instances.

That the instance is running the old Docker image: The assistant assumes the running instance has the previous version of the scripts (without memprobe and OOM recovery). This is a safe assumption given that the new Docker image was just built and pushed in <msg id=4018>.

That scripts can be updated on a running instance: The entrypoint and benchmark scripts live inside the Docker container. Updating them requires either rebuilding the container (which would restart the benchmark) or finding a way to modify files on the running container. The assistant's plan to "deploy the fixed scripts" may involve copying updated files into the container or modifying the host-level setup.

That the benchmark will crash without the fixes: The entire OOM mitigation effort is predicated on the assumption that the current configuration is insufficient for memory-constrained instances. The 342 GiB instance crash in the previous chunk validated this assumption empirically.

The Thinking Process Visible in the Message

The assistant's reasoning is laid bare in the structure of this message. The opening statement—"Now let me deploy the fixed scripts to the running 256 GiB instance (32897009) and test"—reveals a decision point. The assistant has completed the development and build cycle. The next logical step is deployment and validation. But deployment requires knowing the state of the target system.

The parenthetical "It's still downloading params, so we have time" is a hypothesis being tested. The assistant expects to find a system in its setup phase, which would provide a comfortable window for intervention. The SSH command is the test of this hypothesis.

The choice of tail -5 rather than tail -20 or cat is deliberate: the assistant wants the most recent state, not the full history. The echo '===' separator creates a visual boundary between the log output and the disk usage report. These are small but meaningful design decisions that reflect an engineer thinking clearly about what information is needed and how to present it.

The output, when it arrives, is a moment of revelation. The assistant learns that the hypothesis was partially wrong—the params are done, the benchmark has started—but the situation is not yet critical. The benchmark just started; there may still be time. The message ends without a follow-up action, leaving the reader (and the user) to wonder: will the assistant deploy the fixes now, or has the window already closed?

Output Knowledge Created

This message produces several pieces of knowledge that inform subsequent actions:

  1. Instance 32897009 is operational: The SSH connection succeeded, confirming the instance is running and accessible.
  2. Parameter download completed successfully: 159 GB of proving parameters are cached on disk.
  3. Benchmark has started: The proving engine is running with 10 proofs at concurrency 4.
  4. The timing window is narrower than expected: The assistant must act immediately or risk the benchmark crashing before fixes are deployed.
  5. The vast.ai SSH gateway is functioning: The connection through ssh6.vast.ai with port 17008 works correctly. This knowledge transforms the deployment strategy from a leisurely update to a race against the clock. Every second the benchmark runs without the OOM recovery loop increases the probability of a crash that wastes the 22-minute parameter download and the benchmark's progress.

Conclusion

Message <msg id=4019> is a masterclass in operational reasoning under uncertainty. It captures the moment when a developer, having completed a complex engineering effort, reaches out to touch a remote system and discover its true state. The SSH command is not merely a status check—it is a reconnaissance mission that tests assumptions, gathers intelligence, and informs the next move.

The tension between the assistant's expectation ("still downloading params") and the reality ("benchmark already started") is the heart of this message. It reminds us that in distributed systems, the gap between what we believe and what is true can be the difference between a successful deployment and a crash. The assistant's calm, methodical approach—state the intention, test the hypothesis, interpret the results—is a model for how to operate in the uncertain terrain of cloud GPU computing.

The window is narrow, but it is not yet closed. The next message will reveal whether the assistant can deploy the fixes in time, or whether the 256 GiB instance will become another OOM casualty on the path to a robust proving system.