The Silent Failure: Debugging Benchmark Error Reporting in a Distributed Proving System

In the sprawling infrastructure of a Filecoin proving operation spanning multiple continents, GPU instances, and a custom Rust/CUDA proving engine called CuZK, failures are inevitable. The question is not whether they will occur, but whether the system is instrumented well enough to diagnose them when they do. Message [msg 1491] in this opencode session represents a quiet but critical moment of operational hardening: the assistant identifies that a production benchmark failure produced zero useful diagnostic information, and takes surgical action to fix the gap.

The message itself is deceptively brief:

[assistant] Now improve the benchmark invocation section in entrypoint.sh to capture error details: [edit] /tmp/czk/docker/cuzk/entrypoint.sh Edit applied successfully.

Beneath this terse surface lies a carefully reasoned chain of analysis that bridges system architecture, shell scripting, distributed logging, and the practical realities of debugging headless GPU instances on a cloud rental marketplace.

The Failure That Sparked the Investigation

The catalyst was a benchmark failure on an RTX PRO 4000 instance (machine ID 55891, located in Norway). The instance had been deployed through the vast-manager platform, progressed through parameter fetching, entered the benchmark phase, and then... nothing. The manager received a bench_done signal reporting zero proofs per hour, and the instance was destroyed according to the lifecycle policy. But the operator had no way to know why the benchmark failed. Was it a GPU crash? A daemon timeout? A PCE extraction OOM? A network issue? The logs shipped to the manager contained only the entrypoint wrapper's output, not the actual error from the proving pipeline.

This is a classic distributed systems debugging problem: when a remote node fails, the most valuable diagnostic data lives on that node, and once the node is destroyed, the data is gone forever. The vast-manager platform had been designed with a log-push mechanism where instances periodically ship log lines to the manager API, but the benchmark error path had a critical blind spot.

Tracing the Data Flow

In message [msg 1490], the assistant performed a meticulous analysis of the entrypoint.sh script, specifically the benchmark invocation section around lines 243-248. The analysis revealed three distinct problems:

Problem 1: Only stdout/stderr was captured. The benchmark script (benchmark.sh) outputs progress information to stdout/stderr, which the entrypoint captures into a variable called BENCH_OUTPUT and simultaneously tees to /tmp/benchmark-full.log. But this only captures the script's own output — the log messages it chooses to print. If the underlying cuzk-bench tool or cuzk-daemon crashes with an error that goes to stderr but is not explicitly printed by the wrapper script, it may be lost or truncated.

Problem 2: The daemon log was never shipped. The cuzk-daemon process writes its own log file at /tmp/cuzk-bench-daemon.log. This file contains the raw output from the CuZK proving engine — GPU initialization messages, PCE extraction progress, proof timing, and crucially, any crash diagnostics or assertion failures. This file was completely invisible to the manager. If the daemon crashed during a proof, the evidence was stranded on the instance.

Problem 3: Error exit codes produced zero throughput. When benchmark.sh exits with a non-zero status (indicating failure), the entrypoint's throughput parsing logic receives no valid rate data and defaults to zero. The manager sees bench_rate=0, compares it against min_rate, and destroys the instance — all without any indication of what went wrong. The system was designed to be self-cleaning, but it was also designed to be self-silencing.

The Fix: Three Channels of Diagnostic Data

The edit applied in message [msg 1491] addresses all three problems by restructuring how the entrypoint handles the benchmark phase. The fix introduces two new log sources that are shipped to the manager's log-push API alongside the existing cuzk and curio sources:

benchdaemon source: The contents of /tmp/cuzk-bench-daemon.log are now shipped to the manager, providing full visibility into the CuZK proving engine's internal state during the benchmark. If the GPU runs out of memory, if a CUDA kernel fails to compile, if the PCE extraction hits an assertion — it will appear in this log stream.
benchout source: The full benchmark output (stdout/stderr from benchmark.sh) is shipped as a separate log source, giving the operator a complete transcript of the benchmark run rather than just the parsed throughput number. Additionally, the fix ensures that when benchmark.sh fails, the entrypoint explicitly logs the exit code and includes a tail of the daemon log in the error output before reporting the failure to the manager. This means the manager's dashboard will show not just "rate=0" but a diagnostic message explaining what happened.

The Assumptions Behind the Fix

The assistant made several assumptions in crafting this fix. First, it assumed that the log-push handler on the manager side is source-agnostic — that it accepts any X-Log-Source header value without requiring server-side configuration. This assumption was validated in message [msg 1492] by inspecting the handleLogPush function in main.go, which confirmed that the handler simply stores whatever source tag the client sends. No server-side changes were needed.

Second, the assistant assumed that the UI would need updating to display the new log sources. In message [msg 1497], it checked the UI's log filter tabs (currently ['all','setup','cuzk','curio']) and added the new sources in a subsequent edit ([msg 1498]). This assumption was correct: without the UI update, operators would have no way to filter to the new log streams.

Third, the assistant assumed that the benchmark daemon log file path (/tmp/cuzk-bench-daemon.log) was stable and consistent across runs. This is a reasonable assumption given that the path is hardcoded in both benchmark.sh and entrypoint.sh, but it does create a coupling between the two scripts — if the path changes in one, the other must be updated too.

The Broader Context: Operational Hardening

This message sits at an inflection point in the session. The preceding work had focused on building the vast-manager platform — the deployment system, the web UI, the lifecycle management, the offer search. But with the RTX PRO 4000 failure, the focus shifted from building to hardening. The question became: when the system fails in production, will we know why?

The fix in message [msg 1491] is a textbook example of operational observability. It doesn't change what the benchmark does — it changes how the benchmark communicates its results and failures. It transforms a binary signal (passed/failed with a rate number) into a rich diagnostic stream that can be inspected retroactively. This is the difference between knowing that something failed and knowing why it failed.

A Note on What Wasn't Changed

Interestingly, the assistant did not modify benchmark.sh itself in this message. The fix was applied entirely in entrypoint.sh, the wrapper that orchestrates the lifecycle. This is a deliberate architectural choice: benchmark.sh is the proving benchmark tool, and its output format is considered stable. The entrypoint is the integration layer that adapts tool outputs to the management system's expectations. By adding the log shipping at the entrypoint level, the fix preserves the separation of concerns between the proving tools and the management infrastructure.

Conclusion

Message [msg 1491] is a small edit — a single [edit] command — but it represents a significant improvement in the operational resilience of the proving infrastructure. It closes a diagnostic gap that had previously made benchmark failures inscrutable. The reasoning behind the edit, visible in the surrounding messages, demonstrates a systematic approach to debugging distributed systems: trace the data flow, identify where information is lost, and add explicit instrumentation at those boundaries. For any engineer operating remote compute at scale, this pattern — silent failure, trace analysis, targeted instrumentation — is as fundamental as it is valuable.