The Silent Instances: Debugging a Log-Shipping Gap in Distributed Benchmark Infrastructure

In distributed systems, silence is often more alarming than error messages. When a fleet of remote machines goes quiet, the operator faces a fundamental uncertainty: are they still working, or have they silently failed? This is precisely the dilemma confronting the assistant in message 1518 of this opencode session, where four out of five active vast.ai GPU instances have fallen silent, stuck in a transitional state with no logs to explain why.

This message represents a critical operational moment in a larger effort to build a robust, automated GPU proving infrastructure. The assistant has just finished deploying a new Docker image with enhanced benchmark error reporting, pushed it to Docker Hub, and restarted the vast-manager management service. Now, in the natural rhythm of development-operations feedback, it checks on the existing instances to see how they're performing. What it finds is concerning—and the reasoning process it employs to diagnose the situation reveals much about the challenges of managing distributed GPU workloads.

The Operational Context

To understand this message, one must grasp the architecture being built. The vast-manager system orchestrates GPU instances rented from vast.ai, a marketplace for distributed compute. Each instance goes through a lifecycle: it is registered, downloads proving parameters (transitioning to params_done), runs a benchmark to measure proof-per-hour throughput (transitioning to bench_done), and finally enters a running state where it actively proves. The assistant has been refining this pipeline across multiple sessions, adding features like persistent deploy settings, bulk actions, backend refinements to keep the highest benchmark score per machine, and—most recently—improved error reporting that ships benchmark logs directly to the manager's log-push API.

The new logging mechanism is critical. Previously, when a benchmark failed, the vast-manager had no visibility into why it failed. The benchmark script would run, produce output locally on the instance, and if it crashed, the manager would simply never receive a bench_done signal. The instance would remain stuck in params_done indefinitely, a silent tombstone with no diagnostic information. The assistant's just-completed work (messages 1491–1506) addressed this by having benchmark.sh ship its output—including error messages—to the manager's /api/log-push endpoint under new log sources benchdaemon and benchout, and by adding corresponding filter tabs to the web UI.

The Discovery: Four Silent Instances

When the assistant queries the dashboard API at message 1513, it finds five active instances: one RTX 3090 in running state, and four others—an RTX 5090, an RTX 5000 Ada, and two RTX 4090s—all stuck in params_done. These four have finished downloading parameters but have not reported benchmark completion. The assistant immediately checks their logs via the instance-logs API endpoint, and the response is an empty array [] for every one of them.

This is the core problem that drives the message. The assistant must determine: are these instances still benchmarking (in which case the empty logs are a log-shipping bug), or have they crashed silently (in which case the empty logs are a symptom of the old image's lack of error reporting)?

Temporal Analysis and Estimation

The assistant's first analytical move is to calculate how long each instance has been in params_done. Using the param_done_at timestamps from the dashboard data, it computes:

RTX 5090 (instance 416e859e): param_done at 11:41 UTC — approximately 29 minutes ago at the time of the message
RTX 5000 Ada (instance d3e996ae): param_done at 11:58 UTC — approximately 12 minutes ago
RTX 4090 (instance 7bec818c): param_done at 11:58 UTC — approximately 12 minutes ago
RTX 4090 (instance d977dead): param_done at 11:59 UTC — approximately 11 minutes ago These timestamps are critical because they establish a time budget for the benchmark. The assistant then estimates expected benchmark duration using historical data: the RTX 4090 in Norway previously achieved 41 proofs per hour, which translates to approximately 1.5 minutes per proof. The benchmark runs 12 proofs, so at that rate, proof execution would take about 18 minutes. Adding warmup time—PCE extraction (2–5 minutes) plus post-restart warmup—brings the total to roughly 23 minutes. Under this estimate, the RTX 4090s (11–12 minutes in) and the RTX 5000 Ada (12 minutes in) could still be running. But the RTX 5090, at 29 minutes, has exceeded the expected duration by a significant margin. This suggests either the benchmark is taking much longer than expected on this GPU, or it has failed.

The Log-Shipping Gap

The assistant correctly identifies the root cause of the empty logs: these instances are running the old Docker image, which predates the log-shipping improvements. The old entrypoint.sh and benchmark.sh scripts do not push logs to the manager's API. The log shipper that was added in the new image—which sends cuzk-daemon logs, benchmark output, and other telemetry to the manager—simply doesn't exist in the running containers.

This creates a diagnostic blind spot. The assistant has improved the system's observability, but the improvement only applies to new instances deployed with the updated image. The existing instances, which were deployed hours earlier, remain opaque. This is a classic operational pattern: you fix the monitoring for future deployments, but the current deployments may already be failing in ways you cannot see.

The Control Check

To further isolate the problem, the assistant checks the logs of the one instance that is working: the RTX 3090 (instance 67cebc14), which successfully completed its benchmark and entered running state. Even this instance returns empty logs when queried via the instance-logs API. This confirms that the log-shipping mechanism is entirely absent from the old image—not just failing intermittently, but completely non-functional.

This control check is a sound debugging practice. By examining a known-good instance, the assistant establishes that the empty logs are a systemic property of the old image, not a symptom of failure specific to the stuck instances. The RTX 3090 worked fine (it's running and proving), yet it also has no logs. Therefore, the absence of logs cannot be used to infer anything about the health of the other instances.

Assumptions and Their Limitations

The assistant makes several assumptions in its reasoning, some of which warrant examination:

Assumption 1: The benchmark consists of warmup plus 12 proofs. This is based on the benchmark script's design, but the assistant does not verify that the old image's benchmark uses the same parameters. If the benchmark parameters changed between image versions, the time estimates would be invalid.

Assumption 2: The RTX 4090's previous rate of 41 proofs/hr applies to the current benchmark. This assumes consistent performance across runs, but GPU proving rates can vary due to thermal conditions, concurrent processes, memory bandwidth contention, or differences in proof complexity.

Assumption 3: The log shipper "died" or the benchmark "crashed." The assistant considers these possibilities but ultimately leans toward "they might still be running" for the shorter-duration instances. This is reasonable but highlights the fundamental uncertainty created by the observability gap.

Assumption 4: The instances are running the old image. This is almost certainly correct—the assistant deployed the new image only moments ago, and these instances were created earlier. However, the assistant does not explicitly verify the image tag running on each instance, which would be a useful cross-check.

The Thinking Process

The message reveals a structured, methodical diagnostic approach. The assistant:

Queries multiple data sources — the dashboard API for instance states and timestamps, the instance-logs API for telemetry, and vastai show instances for the provider-side view
Performs temporal calculations — converting timestamps to elapsed durations and comparing against expected benchmark times
Uses historical performance data — the 41 proofs/hr rate from a previous RTX 4090 benchmark
Executes a control experiment — checking logs on a known-good instance to distinguish between systemic issues and instance-specific failures
Formulates hypotheses — considering both "still running" and "crashed" explanations
Identifies the root cause — the old image lacks log shipping, creating an observability gap This is not the work of someone randomly poking at the system. It is a deliberate, hypothesis-driven investigation constrained by incomplete data.

The Broader Significance

This message sits at a pivot point in the session. The assistant has been working on platform hardening—improving error reporting, enhancing the UI, refining backend logic. But the discovery of four silent instances reveals that the hardening only applies to future deployments. The existing instances are running legacy code with no visibility into their internal state.

This tension—between improving the system and diagnosing issues in the current system—is a recurring theme in operational software engineering. The assistant must decide whether to wait for the existing instances to complete (or fail), or to proactively intervene by destroying them and redeploying with the new image. The message ends with the assistant checking the RTX 3090's logs and finding them empty, which confirms the observability gap and sets up the decision that will come in subsequent messages.

The message also demonstrates the importance of temporal reasoning in distributed systems. When you cannot observe a remote process directly, you must infer its state from timestamps, expected durations, and historical baselines. The assistant's calculation of benchmark duration—breaking it into warmup phases and per-proof execution time—is a textbook example of this kind of reasoning.

Conclusion

Message 1518 captures a moment of operational discovery. The assistant has just improved the system's observability, only to discover that the existing deployments are invisible to the new monitoring. Four GPU instances sit in limbo, their logs empty, their progress unknown. Through careful temporal analysis and a control experiment, the assistant establishes what it can know and, more importantly, what it cannot.

The message is a reminder that in distributed systems, observability is not a binary property—it must be designed into every component, and retrofitting it only helps future deployments. The silent instances will either emerge from their benchmark or remain forever stuck, and without logs, the assistant cannot tell which. This uncertainty, and the methodical reasoning used to manage it, is the heart of this message.