Pushing the Fix: Deploying OOM Resilience to Production

"Build succeeded — memprobe compiled and included. Now push:"

With those words, message 4018 marks the culmination of an intense debugging and development session focused on one of the most stubborn problems in production systems: out-of-memory (OOM) kills on memory-constrained GPU instances. This message is the deployment step — the moment when all the fixes, utilities, and recovery logic that had been built over the preceding rounds were packaged into a Docker image and pushed to a registry, making them available for live vast.ai instances. It is a short message, but it carries the weight of everything that came before it.

The Context: A Memory Crisis on vast.ai

To understand why this message exists, we must step back into the problem that drove it. The team was running a GPU-accelerated proving system (cuzk) on vast.ai instances — rented cloud machines with NVIDIA GPUs used for Filecoin proof generation. These instances operate under cgroup memory limits imposed by the vast.ai platform, meaning a machine might report 503 GiB of host RAM via /proc/meminfo but only have 342 GiB available due to cgroup constraints. Earlier in segment 29, the team had already fixed the cgroup-aware memory detection in detect_system_memory() (see [msg 4016]), preventing the system from reading host RAM and over-allocating. But even with correct cgroup detection, a 342 GiB instance was still crashing with OOM kills during GPU processing.

The root cause was multi-layered. First, the CUDA pinned memory pool (PinnedPool) operated outside the MemoryBudget system — pinned buffers returned to the pool after GPU work were never freed from actual RSS, creating a massive accounting discrepancy between what the system thought it was using and what the kernel actually held. Second, kernel and driver overhead — glibc memory arenas, page tables, GPU driver allocations — consumed cgroup budget invisibly, with no tool reporting it. Third, the transient SRS loading spike (simultaneous mmap + cudaHostAlloc) could push memory usage past the limit during initialization. The 10 GiB safety margin that had seemed generous was empirically insufficient.

The team devised a two-pronged strategy. First, a memprobe utility — a tiny C program that allocates 1 GiB chunks via mmap/memset until it nears the cgroup limit, providing a data-driven safety margin that accounts for hidden kernel overhead. Second, an OOM recovery loop in benchmark.sh: if the cuzk daemon is killed (exit code 137, the OOM signal), the budget is reduced by 10% and the benchmark retried up to three times, with a 30-second wait between retries for kernel memory reclaim. The entrypoint.sh was updated to run memprobe after memcheck and use the more conservative of the two budget estimates.

All of this work — the C source for memprobe.c, the shell script rewrites, the Dockerfile amendments — was committed in [msg 4016] with the message "cuzk: add memprobe utility and benchmark OOM recovery", touching 4 files with 374 insertions and 113 deletions. Then the Docker image was built in [msg 4017], where the build log confirmed that memprobe was compiled in the builder stage and copied to the runtime image at /usr/local/bin/memprobe.

Message 4018: The Push

Message 4018 is the logical next step after a successful build. The assistant writes:

Build succeeded — memprobe compiled and included. Now push:

Then executes a docker push command targeting theuser/curio-cuzk:latest. The output shows the layer-by-layer progress of the push:

830e168a4e6c: Pushed
b7ec8fd7c485: Layer already exists
dab2da33fe40: Layer already exists
b5c4de646bba: Layer already exists
18241330a2d1: Layer already exists
ec8006f1aa66: Pushed
073ec47a8c22: Layer already exists
a5618bfa14b2: Pushed
90e36cf1587f: Pushed
latest: digest: sha256:6981c6bcc09b68781b8f7d368aa15fe4173ddeb78fcc6a2e8923352d79ce4b70 size: 5346

The output reveals something important about the Docker layer caching: several layers already existed in the registry (Layer already exists), while others were newly pushed. This tells us that the image shares a base with a previous build — the runtime base image, Go toolchain, and dependency layers were unchanged. Only the layers containing the new binaries (memprobe, updated entrypoint.sh, updated benchmark.sh) and the updated Dockerfile.cuzk needed to be pushed. This is efficient: the team is iterating on the same image tag, and Docker's layer caching means only the changed layers traverse the network.

The final digest — sha256:6981c6bcc09b68781b8f7d368aa15fe4173ddeb78fcc6a2e8923352d79ce4b70 — is the cryptographic fingerprint of the complete image. Any node that pulls this digest will get exactly the same filesystem content. This is the artifact that will be deployed to the vast.ai instances.

Why This Message Matters

On the surface, message 4018 is mundane: a Docker push that succeeded. But in the context of the session, it represents a critical inflection point. The team had spent hours diagnosing a crash that was intermittent, environment-dependent, and caused by the interaction of multiple subsystems (cgroup limits, pinned memory pools, kernel overhead, GPU driver allocations). The fix was not a single line change but a coordinated set of improvements across four files and two programming languages (C for memprobe, Bash for the scripts). The push makes all of that work real — it transforms local commits and a local Docker build into a deployable artifact that can be pulled onto live instances.

The message also demonstrates the assistant's operational maturity. The assistant does not just write code and declare victory; it sees the process through to deployment. The sequence is: write → commit → build → push → deploy. Message 4018 is the "push" step. The very next message ([msg 4019]) shows the assistant SSHing into the live 256 GiB vast.ai instance to deploy the updated scripts and test memprobe in production. The push is the bridge between development and operations.

Assumptions and Input Knowledge

Several assumptions underpin this message. The assistant assumes that Docker credentials are already configured on the build machine — docker push to a private registry (the theuser/ namespace suggests a personal or team registry on Docker Hub) requires authentication. The push succeeding confirms this assumption was correct. The assistant also assumes that the latest tag is the appropriate target for deployment — this is conventional but implies that the team is comfortable with overwriting the latest tag with each build, which is typical for active development but would be inappropriate for a release pipeline with version pinning.

The input knowledge required to understand this message includes: familiarity with Docker image tagging and layer caching; understanding that docker push uploads layers to a registry and that Layer already exists indicates a cache hit; knowledge that the digest sha256:... uniquely identifies the image content; and awareness that theuser/curio-cuzk:latest is the deployment target for the vast.ai instances.

The output knowledge created by this message is the published image. Any vast.ai instance configured to pull theuser/curio-cuzk:latest will now receive the image containing memprobe, the OOM recovery loop, and the cgroup-aware memory detection. The digest provides a verifiable record of exactly what was deployed.

The Thinking Process

The assistant's reasoning is visible in the structure of the message. The opening line — "Build succeeded — memprobe compiled and included." — is a status check. The assistant verified that the build completed successfully before proceeding to push. This is a critical gate: pushing a failed build would waste time and potentially deploy a broken image. The assistant then states the action — "Now push:" — and executes it. The thinking is linear and cautious: verify, then act.

The choice to show the full docker push output (rather than just a success message) is deliberate. The output provides evidence that the push worked, shows which layers were new vs. cached, and records the digest for future reference. In a debugging session where reproducibility matters, having the exact image digest in the conversation history is valuable — if the same crash occurs after deployment, the team can verify that the running image matches this digest.

Conclusion

Message 4018 is a small but pivotal message in a larger narrative about taming memory management on constrained GPU instances. It is the deployment step that turns local fixes into production reality. The memprobe utility, the OOM recovery loop, and the cgroup-aware detection are no longer just code on disk — they are now available as a Docker image ready to be pulled onto any vast.ai instance. The push succeeded, the digest was recorded, and the next message would deploy these fixes to a live instance that was already running a benchmark at 99% of its cgroup limit, with zero headroom. The fixes arrived just in time.