From Configuration Fixes to Systematic OOM Prevention: A Full-Stack Engineering Sprint in GPU Proving Infrastructure

Introduction

In the span of a single coding session segment, an AI assistant and its human collaborator transformed a fragile, crash-prone GPU proving deployment into a robust, self-monitoring infrastructure. This chunk of the opencode conversation—spanning messages from the initial deployment firefight through the design and implementation of a comprehensive memory safety system—captures the full arc of production engineering: the discovery of critical configuration errors, the iterative refinement of measurement methodology, the diagnosis of a cluster-wide SSH meltdown, and the architectural response to systematic out-of-memory (OOM) failures. What emerges is a case study in how distributed systems are hardened not through grand architectural redesigns but through the accumulation of small, correct decisions, each grounded in observation and verified against real hardware.

Part I: The Deployment Firefight — Four Critical Fixes

The chunk opens with the assistant facing four issues discovered during live deployment testing of the CuZK proving daemon on vast.ai GPU instances. These were not theoretical problems—they were concrete failures observed on machines running real Filecoin proofs.

Fix 1: Synthesis Concurrency. The synthesis_concurrency parameter, which controls how many proof synthesis tasks run concurrently, was defaulting to 4. Through extensive empirical tuning in earlier segments, the assistant had established that 18 concurrent syntheses was the sweet spot for DDR5 systems with 64 cores. The fix was applied to both run.sh (the production daemon startup script) and benchmark.sh (the benchmarking script), ensuring consistency across operational modes [7].

Fix 2: Benchmark Concurrency Floor. The entrypoint.sh script calculated benchmark concurrency by dividing available memory by per-proof requirements times a safety factor. On 251GB machines, this calculation produced a value of 3—too low to saturate the GPU pipeline. The assistant added a floor of 4, a pragmatic override that ensured even memory-constrained machines would run enough concurrent proofs for meaningful measurement [9].

Fix 3: Missing Status Listener. The benchmark script's daemon configuration template omitted the status_listen directive, meaning the HTTP status API—which powers the vast-manager's real-time pipeline visualization—was unavailable during benchmarks. Operators were flying blind during the very activity designed to validate performance. The fix added status_listen = "0.0.0.0:9821" to the template [11].

Fix 4: ANSI Escape Code Stripping. The vast-manager web UI was rendering raw ANSI escape codes in its log displays, making logs unreadable. The assistant added a stripAnsi() JavaScript helper function and applied it in both renderInstanceLogs and renderManagerLogs before the existing HTML-escaping step [13][14][15].

These four fixes, applied across run.sh, benchmark.sh, entrypoint.sh, and ui.html, represent the difference between a system that works in development and one that works in production. Each fix was small—a number change, a floor clamp, a config key addition, a regex substitution—but collectively they addressed the gap between architectural design and operational reality.

Part II: Restructuring the Benchmark — The Three-Phase Model

With the four fixes applied and the Docker image rebuilt and pushed, the user raised a deeper concern about measurement methodology. The existing benchmark design involved starting the daemon in a special "warmup mode" with reduced concurrency for PCE (Pre-Compiled Constraint Evaluator) extraction, then restarting it with full production settings for the actual measurement. The restart forced the daemon to reload multi-gigabyte SRS files, adding minutes of overhead that dominated total benchmark time [28].

The user's insight was concise and transformative: "No restart after PCE warmup, for actual bench round let's do 5 proofs dispatched for warmup, 10 timed for throughput measurement, 3 cooldown." This three-phase model—warmup, timed, cooldown—eliminated the expensive daemon restart and introduced a rigorous measurement methodology that accounted for pipeline cold-start behavior and drain effects [28].

The assistant's implementation of this model required careful restructuring of benchmark.sh. The warmup mode was removed entirely; the daemon now starts once with full settings and runs all 18 proofs (5+10+3) in a single session. The timed phase captures only the middle 10 proofs, discarding both the ramp-up and ramp-down periods. This approach ensures that the measured throughput reflects steady-state behavior, not transient pipeline dynamics [29][30][31].

The restructuring spanned multiple edits across several messages, touching the help text, argument parsing, daemon startup, and benchmark execution logic. The entrypoint.sh was updated to set BENCH_PROOFS=10 (the timed count) instead of the previous total of 12. A new Docker image was built and pushed [41][42].

Part III: The SSH Crisis — Exit Status 255 Across All Nodes

Just as the benchmark restructuring was completing, the user reported a catastrophic failure: "vast-manager cuzk connection fails for all nodes with 'cuzk: ssh exec failed: exit status 255'" [45]. Every single remote instance was showing the same error. The SSH proxy—the mechanism by which the vast-manager server connects to remote GPU instances to fetch their CuZK status—had stopped working entirely.

Exit code 255 is SSH's universal "I couldn't even start the connection" error, distinct from command failure (exit code 1) or authentication errors (which SSH typically reports with specific messages). The fact that all nodes were failing simultaneously immediately ruled out per-instance problems and pointed to a systemic issue on the manager side [46].

The assistant's diagnostic approach was methodical. Rather than guessing at the root cause, it first examined the Go code in main.go to understand exactly how the SSH proxy worked. This revealed a critical flaw: the code used cmd.Output(), which captures only stdout. SSH's error messages—the very evidence needed to diagnose exit code 255—were being written to stderr and silently discarded. The assistant's reasoning notes this explicitly: "the actual SSH error (from stderr) is thrown away. All we see is 'exit status 255' with no detail" [46].

The fix was twofold. First, the Go code was modified to capture SSH stderr and include it in error messages, so future failures would be self-diagnosing. Second, a retry mechanism was added: on exit code 255, the code removes the stale ControlMaster socket and retries once, handling the common case where a dead master connection leaves a stale socket file [60][61][62].

But the stderr capture revealed the true root cause: the vast-manager host's SSH public key had never been added to the vast.ai instances' authorized_keys files. And when the assistant attempted to add it, a second bug emerged—the original authorized_keys file lacked a trailing newline, causing the new key to be concatenated onto the same line as the existing key, producing a malformed entry that SSH rejected [89][90][91][92].

The assistant fixed this on four running instances, using printf with explicit newlines to ensure each key occupied its own line. The new vast-manager binary was built, copied to the manager host via SCP, and deployed via systemd restart. Verification confirmed that SSH connectivity was restored across all nodes [94][95][96].

Part IV: The Memcheck System — From Reactive Firefighting to Proactive Prevention

With SSH connectivity restored, the user reported a deeper problem: CuZK was being OOM-killed on production nodes, including the largest 256GB machines. Benchmarks would pass, but production loads consumed all available memory and triggered the kernel's OOM killer. The user's request was specific and architecturally ambitious: build a utility that "understands memory constraints, including cgroups (docker) very well," understands pinning constraints, reports to vast-manager, and surfaces data in the UI [98].

The assistant's response was a comprehensive planning session that produced the memcheck.sh utility—a cgroup-aware memory analysis tool that became the cornerstone of OOM prevention across the proving fleet [101].

The Root Cause. The assistant's investigation confirmed that CuZK's detect_system_memory() function read total RAM from /proc/meminfo, which reports the host's physical memory. Inside Docker containers, this value is meaningless—the real constraint is the cgroup memory limit, which is invisible to /proc/meminfo. A 256GB machine running a Docker container with a 128GB limit would appear to CuZK as having 256GB available, causing it to allocate far beyond what the container could provide [100][101].

The Utility Design. The memcheck.sh script detects cgroup v1/v2 memory limits by reading /sys/fs/cgroup/memory/memory.limit_in_bytes (v1) or /sys/fs/cgroup/memory.max (v2), checks RLIMIT_MEMLOCK for pinning capability via ulimit -l, gathers GPU information via nvidia-smi, and calculates safe concurrency levels. The effective memory limit is computed as min(MemTotal, cgroup_limit)—taking the minimum of the host-reported value and the container-imposed constraint [101].

Full-Stack Integration. The memcheck system was not just a shell script. The assistant built a complete feedback loop spanning four layers:

Shell utility: memcheck.sh runs on each instance and outputs JSON
API endpoint: POST /memcheck added to the vast-manager Go backend, storing reports in SQLite
Dashboard UI: A renderMemcheck function and dedicated panel in the instance detail view
Entrypoint integration: The entrypoint script runs memcheck before benchmarks and uses results to dynamically configure BUDGET and BENCH_CONCURRENCY Additional improvements included auto-restart logic for CuZK/Curio after benchmark completion and on crash, and setting CURIO_NODE_NAME to the container hostname for easier node management [99][101][106][107][108].

Part V: Themes and Engineering Philosophy

Several themes recur throughout this chunk, revealing the engineering philosophy that guided the work.

Observation-Driven Development. Every fix in this chunk was grounded in observation from live deployment testing. The synthesis concurrency value of 18 was not guessed—it was discovered empirically. The three-phase benchmark model emerged from watching the system's actual warmup behavior. The SSH key concatenation bug was found by examining the actual authorized_keys file. The memcheck system was a direct response to observed OOM kills. This pattern—observe, hypothesize, test, implement—is the hallmark of disciplined engineering.

The Power of Small Fixes. The most impactful changes in this chunk were often the smallest. A floor of 4, a missing config key, a regex substitution, a single environment variable. None of these changes would be noteworthy in isolation, but together they transformed a system from barely functional to production-ready. This is a reminder that in complex systems, reliability is achieved not through grand architectural changes but through the accumulation of small, correct decisions.

Systematic Debugging. The SSH debugging session is a textbook example of systematic diagnosis. Faced with a cryptic error code affecting all nodes, the assistant resisted the urge to guess. Instead, it examined the code to understand the mechanism, identified the stderr capture gap, fixed the diagnostic infrastructure first, and then used the improved diagnostics to find the root cause. This approach—understand before changing, fix diagnosis before fixing symptoms—is the essence of professional debugging.

Full-Stack Thinking. The memcheck system exemplifies full-stack engineering. The assistant did not stop at writing a shell script. It designed an API endpoint, a database schema, a UI component, and an integration point in the entrypoint script. This end-to-end thinking ensured that the utility's output would be visible, persistent, and actionable—not buried in a log file that no one reads.

Conclusion

This chunk of the opencode session captures the full arc of production engineering: from configuration fixes to measurement methodology, from SSH crisis to systematic OOM prevention. The assistant and user together transformed a system that was crashing under production load into one that could self-diagnose its memory constraints, self-configure for safe operation, and self-recover from failures. The work demonstrates that building reliable distributed systems is not about avoiding problems—it is about building the infrastructure to detect, diagnose, and respond to them when they inevitably arise.## Part VI: Coordination and Workflow — The Invisible Infrastructure of Collaboration

Beyond the technical fixes, this chunk reveals a sophisticated pattern of human-AI collaboration. The assistant maintained a persistent todo list using the todowrite tool, updating it after each logical unit of work. This served multiple functions simultaneously: it externalized the assistant's working memory, communicated progress to the user, created an audit trail, and provided a recovery point if the conversation were interrupted [8][10][12][16].

The user's messages were equally strategic. The two-sentence "continue if you have next steps, or stop and ask for clarification" message at the beginning of the chunk [2] was a masterclass in delegation—authorizing execution while providing a safety valve for uncertainty. The three-phase benchmark proposal [28] demonstrated how a single observation-driven insight could reshape an entire measurement methodology. The memcheck mandate [98] was not just a bug report but an architectural specification, decomposing a complex problem into implementable components.

This pattern of interaction—the assistant proposing, the user validating, the assistant executing, the user observing—created a tight feedback loop that enabled rapid iteration. Each cycle of the loop produced new knowledge that informed the next cycle. The SSH debugging session, for example, started with a cryptic error code, progressed through code examination and stderr capture, and ultimately revealed both a missing SSH key and a concatenation bug—findings that would have been impossible to discover without the systematic approach.

Conclusion: The Architecture of Reliability

This chunk of the opencode session tells a story that is common in production engineering but rarely documented: the story of how a system goes from "works on my machine" to "works in production." The path is not a straight line. It involves configuration fixes that seem trivial in retrospect, measurement methodology that evolves as you understand the system's actual behavior, infrastructure failures that reveal hidden assumptions, and architectural responses that transform reactive firefighting into proactive prevention.

The CuZK proving system that emerged from this chunk was fundamentally different from the one that entered it. The synthesis concurrency was tuned to match the hardware. The benchmark methodology accounted for pipeline dynamics. The SSH proxy was hardened with stderr capture and retry logic. The memory detection was made cgroup-aware. The dashboard was cleaned up. The processes were set to auto-restart. Each change was small, but their cumulative effect was transformative.

In the end, reliability is not a feature you add—it is a property that emerges from the accumulation of correct decisions, each grounded in observation and verified against real systems. This chunk of the opencode session is a testament to that principle, and a case study in how AI-assisted engineering can accelerate the journey from fragile to robust.