From Platform Quirks to Resource Constraints: The Hardening of a Distributed GPU Proving System

Introduction

In the lifecycle of any distributed infrastructure project, there comes a phase where the focus shifts from "does it work at all?" to "does it keep working when things go wrong?" Segment 7 of this opencode session captures exactly that transition—and then pushes past it into an even more fundamental question: "does it work on all hardware, or only on the hardware we tested it on?"

Over the span of roughly 150 messages, the assistant and user navigated a gauntlet of platform-specific surprises, subtle shell semantics, race conditions, and resource constraints. The VAST_CONTAINERLABEL mystery was definitively resolved: Vast.ai injects it as a non-exported shell variable, invisible to env in SSH sessions but fully available to the Docker entrypoint, validating the original architectural design. A critical bug in the vast-manager monitor was identified and fixed—it matched database instances to Vast API instances solely by the API's label field, which remains null unless explicitly set, causing the monitor to incorrectly kill perfectly healthy workers. The benchmark and entrypoint scripts were hardened against a gRPC transport error that caused the warmup proof to fail silently. And a new deployment strategy emerged: using --onstart-cmd to run the lifecycle entrypoint in the background alongside SSH access, adapting to Vast.ai's SSH launch mode that replaces the Docker ENTRYPOINT entirely.

But the segment's most consequential discovery came at the very end. Two new instances—one in BC Canada with 2× RTX 3090s and 125GB RAM, one in Norway with 1× RTX 4090 and 500GB RAM—were created from the same Docker image with the same configuration. The Norway instance completed its warmup proof, validating the entire pipeline. The BC Canada instance was silently killed by the Linux OOM killer, its daemon terminated mid-synthesis because 125GB of RAM was insufficient for 10 concurrent uncached partition syntheses.

This article traces the arc of Segment 7, examining how each discovery fed into the next, and how the system evolved from a collection of brittle assumptions into a resilient, deployable infrastructure that could survive both platform quirks and hardware heterogeneity.

The VAST_CONTAINERLABEL Resolution: A Lesson in Unix Semantics

The segment opens with the assistant at a moment of maximum uncertainty. A sprawling status report documents the belief that VAST_CONTAINERLABEL—the environment variable that Vast.ai was supposed to inject into every container—simply did not exist. The assistant had SSHed into running instances, run env | grep -i vast, and found nothing. The conclusion seemed inescapable: the variable was missing, and the entire registration architecture was built on a false premise.

But the very next messages shattered this conclusion. The user ran echo $VAST_CONTAINERLABEL inside a container and got C.32709851. The variable existed. It was simply not exported—a distinction between shell variables and environment variables that is fundamental to Unix but easy to overlook when debugging remotely. Vast.ai's runtime was setting VAST_CONTAINERLABEL as a regular shell variable, likely injected via a profile script that ran before the SSH session began. The env command, which only shows exported variables, was blind to it. But the Docker ENTRYPOINT, running as PID 1 with the full container environment, would see it perfectly.

This discovery validated the original design. The entrypoint did not need to be rewritten. No workaround was necessary. The assistant's earlier debugging methodology—using non-interactive SSH commands to probe the environment—had a blind spot that cost hours of investigation. The lesson was clear: when probing remote environments, echo $VAR is more reliable than env | grep VAR, and the distinction between interactive and non-interactive shell sessions matters enormously.

The resolution of this mystery cleared the way for the next phase: actually deploying the system and watching it run. But the platform had more surprises in store.

The SSH Mode Revelation: When the Entrypoint Vanished

With the VAST_CONTAINERLABEL mystery resolved, the assistant rebuilt the Docker image, pushed it to Docker Hub, destroyed the old instance 32709851, and created a new one. The creation command used the --ssh flag, which had been included deliberately to provide SSH access for debugging. The assumption was that --ssh would layer SSH access on top of the existing Docker ENTRYPOINT, allowing both the automated lifecycle and interactive debugging to coexist.

This assumption proved spectacularly wrong. When the assistant SSHed into the new instance and ran ps aux | head -30, the output revealed a stark truth: PID 1 was not the Docker ENTRYPOINT. It was Vast.ai's own init script, /.launch. The --ssh launch mode had replaced the ENTRYPOINT entirely.

This discovery was a turning point. The assistant now understood that Vast.ai's SSH mode works by injecting its own init process that sets up SSH and runs an optional onstart.sh script. The carefully crafted entrypoint.sh, designed to run the full lifecycle (tunnel → register → params → benchmark → supervisor), was never executed. The instance was alive, SSH was accessible, but the automated proving pipeline was dead in the water.

The fix was elegant: use --onstart-cmd to run the entrypoint in the background. This Vast.ai feature allows specifying a command that runs after the SSH init script starts, providing both SSH access and automated lifecycle management. The assistant destroyed the instance and created a new one with the --onstart-cmd workaround, and the entrypoint finally ran.

The Monitor Matching Bug: When Labels Lie

With the entrypoint running, the instance registered with the vast-manager and began downloading Filecoin proof parameters. But then the user reported a new problem: the instance had been marked as "killed" by the monitor, even though it was actively running and fetching data. This was not a cosmetic issue—a killed instance would be removed from the active pool, its proving capacity lost.

The assistant's investigation traced the root cause to a fundamental mismatch in how the vast-manager matched instances. The monitor built a labelMap keyed by the vi.Label field returned by the Vast API. However, Vast.ai only populates this API-level label field when a user explicitly runs vastai label instance <id> <label>. The VAST_CONTAINERLABEL environment variable—which Vast.ai does inject into containers automatically—is a separate mechanism. It exists inside the container but is not reflected in the API's label field. The monitor was looking at the wrong source of truth.

The fix required building a secondary lookup mechanism. Since DB labels follow the pattern C.<vast_instance_id>, the assistant extracted the numeric Vast instance ID from the DB label and matched it against the Vast API's instance ID (vi.ID), which is always available regardless of whether a label has been set. The fix introduced an idMap—a map from Vast instance ID to VastInstance—built alongside the labelMap in the monitor function. A lookupVast helper function tried the label lookup first, then fell back to ID-based matching.

This fix touched multiple paths in the monitor: the disappearance check, the unregistered instance check, the bad hosts check, the killTimedOut function, and the dashboard data merge. Each location that previously did a simple labelMap[db.Label] lookup was updated to use the new fallback logic. The fix was compiled, deployed, and validated.

The Silent Benchmark: When gRPC Connections Break

With the monitor bug fixed and the instance running correctly, the system faced its next test: the benchmark phase. The instance had downloaded all proof parameters and signaled "param-done" to the manager. The entrypoint began the benchmark: "12 proofs, concurrency 5, partition-workers 10." And then—silence. No benchmark logs appeared. No GPU activity was visible in nvidia-smi. The instance was stuck.

The assistant's investigation revealed a cascade of failures rooted in a single gRPC transport error. The warmup proof—the very first proof run after the daemon starts—triggered PCE (Pre-Compiled Constraint Evaluator) extraction, a computationally intensive process that synthesizes circuit constraints for the first time. This extraction took several minutes, during which the gRPC connection between the cuzk-bench client and the cuzk-daemon was severed. The error message read: "transport error: connection error: stream closed because of a broken pipe."

The fallout was cascading. Because benchmark.sh used set -euo pipefail, the warmup failure caused the entire benchmark script to abort. The cleanup trap killed the daemon. The entrypoint, which had invoked the benchmark inside a command substitution piped through tee, ended up in an ambiguous state—alive but stuck in a supervisor loop, trying to restart components that had never been properly initialized.

The fix was threefold. First, the benchmark script was modified to not use set -e for the warmup call—allowing the warmup proof to fail without aborting the entire script. Second, after the warmup attempt, the script checks whether the PCE was extracted despite the client timeout (since the daemon may continue processing after the client disconnects). Third, if the warmup failed, the script retries it—this time expecting success because the PCE cache is now available.

The entrypoint was also hardened. Rather than relying on set -e to catch benchmark failures (which had proven unreliable due to subtle bash semantics around command substitution), the entrypoint now explicitly checks the benchmark exit code and exits cleanly if the benchmark fails, rather than falling through to the supervisor loop with an uninitialized proving stack.

The Fork in the Pipeline: In-Place Recovery vs. Clean Rebuild

With the fixes designed, the assistant faced a tactical decision: should it rebuild the entire Docker image and restart the instance cleanly, or should it surgically intervene on the running machine to salvage the deployment?

The clean path—rebuild the image, push it, destroy the old instance, create a new one—would incur significant costs. The Docker build takes minutes. The push takes minutes. The new instance would need to re-download all 56GB of proving parameters from scratch. Meanwhile, the old instance continues to bill while sitting idle.

The assistant chose a hybrid approach: fix the running instance in-place by copying the fixed scripts via SCP, while simultaneously rebuilding the Docker image for future instances. This was a classic "repair while building" strategy. The instance could be back to proving within minutes, while the new image would be ready for the next instance that gets created.

The in-place recovery involved killing the stuck entrypoint process, sweeping for orphan processes, copying the fixed benchmark.sh and entrypoint.sh to the instance, and restarting the entrypoint using a wrapper script that explicitly exported VAST_CONTAINERLABEL before launching. The wrapper script was necessary because when the entrypoint was launched via SSH (rather than as the Docker ENTRYPOINT), the VAST_CONTAINERLABEL variable wasn't in the environment—it was only available to the container's init process.

The assistant used setsid to launch the wrapper script, ensuring it ran in a new session and wouldn't be killed when the SSH connection closed. This was necessary because nohup alone wasn't sufficient—the SSH session was blocking on the child process.

The result of all this work was captured in a single log output:

[entrypoint] 01:05:23 Starting portavailc tunnel to [REDACTED]:22222 ...
[entrypoint] 01:05:23 portavailc started (PID=8368, attempt=1)
[entrypoint] 01:05:23 Tunnel ready (management service reachable)
[entrypoint] 01:05:23 RAM=251GB (<400GB), using partition-workers=10
[entrypoint] 01:05:24 Registered: uuid=[REDACTED] runner_id=6
[entrypoint] 01:05:24 Log shipper started...

Six lines of log output. Each line tells a story of assumptions tested, bugs uncovered, and a system slowly bending toward correctness. The tunnel connected. The instance registered. The log shipper started. The benchmark was about to begin—this time with the hardened scripts that could survive a warmup failure.

With the running instance fixed in-place, the assistant turned to the long-term artifact. The fixed scripts—the hardened benchmark.sh and entrypoint.sh—needed to be baked into the Docker image so that future instances would benefit from them automatically. The Docker tag and push command sealed the fix: nineteen layers pushed to the registry, each layer containing not just code but the accumulated knowledge of a debugging journey that spanned multiple subsystems and uncovered half a dozen distinct failure modes.

Clearing the Decks: Infrastructure Cleanup

With the Docker image pushed and the first instance running correctly, the user issued a non-interactive directive: "Work with the two hosts 93197,88910, the two instances, possibly kill them and recreate with the correct image; You are now non-interactive for the next few hours, do not stop, do not ask questions until instances run correctly."

The assistant's first task was to reconcile the user's mental model with ground truth. The user had referenced an instance 32710979 on host 88910, but the Vast API returned a 404 error when the assistant attempted to destroy it. This instance simply did not exist in the assistant's account. The assistant correctly inferred that it must belong to a different account or had already been terminated, and pivoted to destroy the actual remaining old instance—32705217, the manually-managed RTX 4090 on host 88910.

This moment of reconciliation is emblematic of the entire segment. The assistant repeatedly had to bridge the gap between expectations and reality, whether those expectations came from the user, from the assistant's own mental model, or from the configuration defaults baked into the benchmark scripts.

The cleanup continued with a nuclear option: a full DELETE FROM instances on the vast-manager's SQLite database. Rather than surgically removing stale entries for the destroyed instances, the assistant chose to wipe the entire table and start from a clean slate. This decision reflected a preference for absolute certainty over surgical precision—a reasonable trade-off when the alternative was the risk of ghost entries confusing the management service's monitoring logic.

With the database clean, the assistant surveyed available GPU offers on both target hosts. The market snapshot revealed the hardware landscape: host 93197 in BC Canada offered a 2× RTX 3090 with 125GB RAM at $0.28/hr, a 1× RTX 3090 with 62GB RAM at $0.14/hr, and a 2× RTX 3070 with 167GB RAM at $0.17/hr. Host 88910 in Norway offered a 2× RTX 4090 with 1001GB RAM at $0.93/hr, a 1× RTX 4090 with 500GB RAM at $0.47/hr, and a 1× RTX 4060 Ti with 125GB RAM at $0.12/hr.

The assistant's selections—the 2× RTX 3090 on BC and the 1× RTX 4090 on Norway—represented a cost-performance trade-off. On BC, the dual-GPU option maximized compute power within a budget-friendly price. On Norway, the single 4090 with 500GB RAM provided ample memory for the memory-intensive proof synthesis. These choices would prove fateful.

The Redeployment: Creating Fresh Instances

With the old infrastructure cleared, the assistant executed the creation of two new instances. Each vastai create instance command was a dense packet of configuration decisions:

vastai create instance 29854362 \
  --image theuser/curio-cuzk:latest \
  --disk 250 \
  --env '-e PAVAIL=[REDACTED] -e PAVAIL_SERVER=[REDACTED]' \
  --ssh --direct \
  --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'

The --onstart-cmd workaround was the critical piece. Earlier in the segment, the assistant had discovered that Vast.ai's --ssh mode replaces the Docker ENTRYPOINT with its own init script. The VAST_CONTAINERLABEL environment variable—essential for the instance to identify itself to the management service—was injected as a non-exported shell variable, invisible to SSH sessions but available to the Docker entrypoint. The workaround was to launch the entrypoint in the background via nohup through --onstart-cmd, preserving SSH access while still executing the automated lifecycle.

Instance 32711932 (BC Canada, 2× RTX 3090, 125GB RAM) and instance 32711934 (Norway, 1× RTX 4090, 500GB RAM) were created successfully. Both returned success: True from the Vast API, and both received instance API keys for authentication with the management service.

The next several messages formed a verification chain, each step confirming a deeper layer of functionality. The assistant waited 60 seconds and queried the Vast API to confirm both instances were in running status with SSH ports and public IP addresses available. The BC instance was reachable at 70.69.192.6:48237; the Norway instance at 141.195.21.87:41122.

The assistant then drilled deeper, querying the vast-manager dashboard API to confirm that both instances had successfully registered with the management service. The response showed both in state: &#34;registered&#34; with runner IDs 7 and 8, each having shipped over 200 log lines. This was a critical validation: the entire chain from Docker image to instance creation to entrypoint execution to service registration was functioning correctly.

A monitoring loop followed, with the assistant checking progress at increasing intervals. Both instances were downloading Filecoin proof parameters—multi-gigabyte .params and .vk files required for proof generation. The BC instance showed download speeds of 19–35 MiB/s. The Norway instance had a slow download warning but aria2 was auto-retrying. The assistant's commentary revealed its mental model: "Both fetching params. The small .vk files are downloading. The big .params files will take ~10-20 minutes."

This waiting period was the calm before the storm. The assistant was operating under the assumption that both instances, having been created with the same image and configuration, would behave symmetrically. But the hardware beneath them was anything but symmetric.

The Moment the OOM Killer Struck

After a ten-minute wait, the assistant queried the manager dashboard and saw:

=== Manager State ===
{
  "label": "C.32711934",
  "state": "params_done",
  "bench_rate": null,
  "log_lines": 3987
}
{
  "label": "C.32711932",
  "state": "killed",
  "bench_rate": 0,
  "log_lines": 4013
}

The Norway instance had progressed to params_done—parameter download complete, benchmark about to begin. The BC Canada instance was in state killed with a bench_rate of 0. Something had gone catastrophically wrong.

The assistant's initial hypothesis was that the BC instance had suffered the same gRPC transport error that had plagued earlier runs. This was a reasonable assumption—the assistant had just spent significant effort hardening the benchmark scripts against that exact failure mode. But the symptom pattern was different this time. A transport error would leave the daemon running but unresponsive; a "killed" status meant the entire instance or its critical processes had been terminated.

The assistant SSH'd into the BC instance and read the benchmark log, but found only a fresh log showing the benchmark restarting from scratch—downloading the C1 data file again. The daemon was dead, and subsequent retry attempts naturally failed because there was no daemon to connect to.

The Diagnosis: When Memory Runs Out

The diagnostic breakthrough came when the assistant connected the dots:

"The BC instance daemon was Killed (OOM killed most likely — 125GB RAM with 10 partition synthesis all growing organically). The daemon received the proof, started 10 partition syntheses, and the OS killed it."

The root cause was elegant and brutal. The CuZK proving engine's first proof run performs Pre-Compiled Constraint Evaluator (PCE) extraction, which allocates memory organically as each partition worker synthesizes its constraint system. With 10 partition workers running simultaneously on a machine with only 125GB of RAM, the combined memory footprint exceeded available resources. The Linux kernel's OOM killer stepped in, terminated the CUZK daemon, and the benchmark failed.

The Norway instance's success provided the perfect counterpoint. With 500GB of RAM and 16 partition workers (the entrypoint script scaled workers based on available memory), the warmup proof completed successfully in approximately 128 seconds:

=== Proof Result ===
status:    COMPLETED
job_id:    d97b2e99-1ef4-4530-ba20-bb1ba188e91f
timings:   total=127497 ms (queue=407 ms, srs=0 ms, synth=715977 ms, gpu=102565 ms)
wall time: 128108 ms
proof:     1920 bytes

This contrast sharpened the diagnosis. The problem was not a software bug, not a configuration error, not a platform quirk—it was a pure resource constraint. The CuZK proving pipeline, when performing its first uncached synthesis, required more than 125GB of RAM when using 10 partition workers. The Norway instance's 500GB handled it without issue.

The assistant enumerated three possible solutions:

  1. Reduce partition-workers for the first warmup proof — Use only 1 or 2 workers during the initial uncached synthesis, then scale up once the PCE cache is built.
  2. Only use hosts with ≥ 256GB RAM — A constraint-based approach that limits the pool of available instances.
  3. Pre-extract PCE before running the full benchmark — A more architectural solution separating PCE extraction from proof generation. The assistant selected option 1 as "the best fix," and the reasoning was sound: it was the least disruptive change, it adapted to existing hardware rather than requiring new hardware, and it leveraged the known behavior that cached PCE uses pre-allocated buffers with lower memory requirements.

The Shift: From Platform Debugging to Resource Tuning

The OOM diagnosis marked a fundamental shift in the project's trajectory. The preceding work had been dominated by platform-specific debugging—working around Vast.ai's SSH environment quirks, fixing the monitor's matching logic, hardening scripts against transient errors. These were all bugs in the deployment infrastructure, the scaffolding around the actual proving engine.

The BC Canada OOM was different. It was a fundamental resource constraint of the proving pipeline itself. No amount of script hardening or platform workaround could make 125GB of RAM hold 10 concurrent uncached syntheses. The fix required changing the proving pipeline's behavior—specifically, how the benchmark script configured parallelism for the warmup proof.

This shift from "fixing the deployment" to "tuning the workload" is a natural progression in any production deployment. First, you make the system deploy reliably. Then, you make it run efficiently on the available hardware. The assistant's proposed fix—reducing partition workers for the warmup proof—was elegant because it acknowledged the asymmetric memory requirements of the proving pipeline. The first proof must allocate memory organically as it synthesizes constraints; subsequent proofs benefit from cached PCE data that uses pre-allocated buffers. By using fewer workers for the memory-intensive first synthesis, the system could stay within the available RAM, then scale up to full parallelism once the cache was populated.

Themes and Lessons

Several themes emerge from this segment that are broadly applicable to distributed systems deployment on heterogeneous infrastructure.

Platform-specific behavior must be discovered, not assumed. Vast.ai's SSH mode replaces the Docker ENTRYPOINT. Its environment variables are injected as non-exported shell variables. Its API label field is null unless explicitly set. None of these behaviors were documented in a way that the assistant had internalized—each had to be discovered through failure, investigated, and worked around.

Shell semantics matter at scale. The interaction between set -e, set -o pipefail, command substitution $(...), and tee is notoriously subtle. In a single script running on a developer's laptop, these subtleties rarely matter. In a distributed proving system where a failed benchmark can cascade into a stuck instance that costs $0.38/hr while doing nothing, they matter enormously.

Recovery is a first-class design concern. The assistant's decision to fix the running instance in-place while simultaneously rebuilding the Docker image reflects an operational maturity that goes beyond "make it work." It recognizes that in production, you don't always have the luxury of a clean rebuild—you need to repair the damaged instance while also investing in the long-term artifact.

The assumption of symmetry is dangerous. The assistant treated both instances as fungible deployments, issuing identical monitoring commands and expecting similar progress. But the 125GB vs 500GB RAM difference proved catastrophic. In heterogeneous marketplaces like Vast.ai, every instance is a unique combination of hardware characteristics, and assumptions of symmetry must be validated empirically.

Surface-level health checks do not guarantee workload-level success. Both instances showed "running" status in the Vast API. Both registered with the management service. Both began downloading parameters. But these indicators masked the latent memory constraint that would kill the BC instance minutes later. The gap between "the container is running" and "the workload is succeeding" is where the most interesting failure modes live.

Diagnostic discipline pays off. The assistant's initial hypothesis—that the BC failure was a recurrence of the transport error—was incorrect. But the assistant did not act on this hypothesis; it investigated further, reading the benchmark log, checking the daemon status, and ultimately tracing the failure to the OOM killer. This discipline, rather than premature action, led to the correct diagnosis and an appropriate fix.

The most elegant fixes acknowledge system physics. Reducing partition workers for the warmup proof is not a hack—it is a design insight that respects the memory dynamics of the CuZK engine. The uncached synthesis phase is the memory bottleneck; once that phase is complete, the system can operate at full capacity. By adapting the pipeline to this reality, the assistant transformed a hard constraint (125GB RAM) into a manageable configuration parameter.

Conclusion

This segment of the opencode session captures the transition from "can we build it?" to "can we keep it running?" to "can we run it on any hardware?" The VAST_CONTAINERLABEL mystery was resolved, the SSH mode entrypoint replacement was discovered and worked around, the monitor matching bug was fixed with an ID-based fallback, the benchmark pipeline was hardened against gRPC transport errors, and the system was deployed on two new instances with dramatically different hardware profiles.

Each discovery fed into the next. The VAST_CONTAINERLABEL resolution enabled the entrypoint to run. The entrypoint running revealed the SSH mode replacement. Fixing the SSH mode revealed the monitor matching bug. Fixing the monitor enabled the benchmark to run. The benchmark failure revealed the gRPC timeout. Fixing the timeout required hardening the scripts. Hardening the scripts required a tactical decision about in-place recovery versus clean rebuild. And the final deployment revealed the OOM constraint that would drive the next phase of work.

The Norway instance's successful warmup proof validated the entire software stack—the Docker image, the entrypoint, the benchmark script, the CuZK engine, the management service. The BC Canada instance's OOM death revealed that validation was only half the battle. The other half was understanding the resource profile of the workload and adapting the configuration to match the available hardware.

In the end, the assistant emerged from this segment with a deeper understanding of the system it was building. It knew not just that the pipeline worked, but under what conditions it worked, and why it failed when those conditions were not met. That knowledge—the product of careful diagnosis and systematic reasoning—is the true output of this segment, more valuable than any single instance or configuration change. The system that emerged was not just functional, but resilient—capable of surviving both the platform quirks of Vast.ai and the hardware heterogeneity of the GPU marketplace.

References

[1] "The Hardening: From Platform Quirks to Production Resilience in a Distributed GPU Proving System" — Chunk 0 of Segment 7, covering the VAST_CONTAINERLABEL resolution, SSH mode discovery, monitor matching bug, benchmark hardening, and in-place recovery.

[2] "The Great Divide: When 125GB of RAM Separates Success from Failure in Distributed GPU Deployment" — Chunk 1 of Segment 7, covering the infrastructure cleanup, redeployment, OOM diagnosis, and shift to hardware-aware configuration.