The Diagnostic Pivot: Investigating a 0-Proofs/Hour Benchmark Failure

Introduction

In the lifecycle of any distributed system, the moment a freshly deployed worker fails its benchmark is a moment of truth. It tests not only the robustness of the deployment pipeline but also the quality of the diagnostic reasoning brought to bear. Message 1476 in this opencode session captures precisely such a moment: a newly deployed Vast.ai GPU instance (1× RTX PRO 4000) has just reported a benchmark result of zero proofs per hour, and the assistant must determine why.

This message is a diagnostic pivot—a transition from the successful completion of a large-scale data integrity refactor (the bad_hosts/host_perf migration from host_id to machine_id) into an urgent operational investigation. The assistant's response is brief but dense with reasoning, hypothesis formation, and investigative methodology. It reveals how an experienced engineer approaches a production failure: formulate a leading hypothesis based on recent history, consider alternative explanations (deployment timing, image caching), and gather structured data before committing to a course of action.

Context: What Just Happened

To understand the significance of this message, one must appreciate what preceded it. The previous ~40 messages (1434–1474) were consumed by a meticulous, multi-file refactoring effort. The assistant had discovered a critical data integrity flaw: the bad_hosts and host_perf systems were keyed on host_id (the Vast.ai operator account number) rather than machine_id (the specific physical machine identifier). This meant that a single bad benchmark from one machine could unfairly penalize an entire operator who might own dozens of diverse machines. The fix required coordinated changes across the SQLite schema, the Go backend handlers, and the JavaScript UI—a classic full-stack data model migration.

The assistant completed this refactoring, deployed the new binary to the controller host (10.1.2.104), verified the migration ran correctly, and confirmed that the dashboard and offers API still functioned (22 instances, 64 offers, 3 bad machines). The todo list was checked off. Everything was clean.

Then, in message 1475, the user posted a raw log from a newly deployed instance:

[setup][entrypoint] 11:20:48 Starting benchmark: 12 proofs, concurrency 3, partition-workers 16
[setup][entrypoint] 11:31:08 benchmark.sh exited with error
[setup][entrypoint] 11:31:08 Benchmark result: 0 proofs/hour
[setup][entrypoint] 11:31:09 Benchmark FAILED: 0 proofs/hour < min_rate 29.0
[setup][entrypoint] 11:31:09 Instance will be destroyed by manager. -- C.32733029	1x RTX PRO 4000	33	0.0	$0.296	-	0%	-	504 GB	46m	SSH -- this one was at 0.0054

This is the event that triggers message 1476. The instance ran for about 10 minutes of benchmark time (from 11:20:48 to 11:31:08), produced zero proofs, and was immediately destroyed by the manager's lifecycle logic. The cost was $0.296/hour, and the minimum acceptable rate was 29 proofs/hour. Zero proofs is a catastrophic failure—it means the proving pipeline never got off the ground.

The Message: Reasoning and Hypothesis Formation

The assistant's response in message 1476 opens with a clear statement of the problem and an immediate hypothesis:

The benchmark failed with 0 proofs/hour. Let me check the benchmark script to understand what went wrong — it's likely the same curio-can't-connect-to-lotus issue (port 1234 not tunneled). The Docker image was rebuilt and pushed with the fix, but this instance may have been deployed before or pulled a cached image.

This is textbook diagnostic reasoning. The assistant does three things in rapid succession:

1. States the observed symptom: 0 proofs/hour.
2. Formulates a leading hypothesis: "it's likely the same curio-can't-connect-to-lotus issue (port 1234 not tunneled)."
3. Acknowledges a counter-hypothesis: "The Docker image was rebuilt and pushed with the fix, but this instance may have been deployed before or pulled a cached image." The leading hypothesis is grounded in recent history. In the previous chunk (Chunk 0 of Segment 9), the assistant had identified and fixed a critical bug where the portavailc tunnel on worker instances was not forwarding port 1234, which hosts the Lotus API. Without this port, curio (the proving daemon) could not connect to Lotus and would fail to produce proofs. The fix was applied to the entrypoint script and the Docker image was rebuilt and pushed. But the question is: did this particular instance get the fixed image? The assistant's reasoning reveals an understanding of deployment realities. Even though the Docker image was rebuilt and pushed, there are two scenarios where the instance might still have the broken version: - Deployment timing: The instance may have been provisioned before the fix was deployed, using an older image. - Image caching: Vast.ai instances may cache Docker images; a new docker push doesn't guarantee that a new instance will pull the latest version, especially if the image tag wasn't changed. This is a sophisticated operational insight. Many engineers assume that pushing a new image automatically means all future deployments use it. In practice, container orchestration platforms (especially decentralized ones like Vast.ai) have complex image caching behaviors, and stale images are a common source of "fixed it but it's still broken" confusion.

The Investigation: Gathering Structured Data

Having formed a hypothesis, the assistant immediately moves to gather data. The chosen method is to query the dashboard API for structured information about the failed instance:

ssh 10.1.2.104 'curl -s http://localhost:1235/api/dashboard | python3 -c "
import sys,json
d=json.load(sys.stdin)
for i in d[\"instances\"]:
    if \"32733029\" in i.get(\"label\",\"\"):
        print(json.dumps(i, indent=2))
"'

This is a deliberate choice. Rather than SSHing into the failed instance (which is already killed and gone), the assistant queries the centralized controller's API. The dashboard endpoint returns the full instance metadata, including the instance's vast_id (32733029), its label, runner ID, state, timestamps, and crucially, its bench_rate of 0.

The response reveals a rich dataset:

{
  "uuid": "6c4f2267-7033-4ad4-b968-5b7cd52642a7",
  "label": "C.32733029",
  "runner_id": 33,
  "state": "killed",
  "min_rate": 29,
  "registered_at": "2026-03-12T11:16:59Z",
  "param_done_at": "2026-03-12T11:20:48Z",
  "bench_done_at": "2026-03-12T11:31:09Z",
  "bench_rate": 0,
  "killed_at": "2026-03-12T11:31:09Z",
  "kill_reason": "bench_rate 0.0 below min_rate 29.0",
  "vast_id": 32733029,
  "gpu_name": "RTX PRO 4000",
  "num_gpus": 1,
  "dph_total": 0.2961111111111111,
  "price_per_proof...

The data tells a story. The instance was registered at 11:16:59, finished parameter fetching at 11:20:48 (about 4 minutes), then started benchmarking. The benchmark ran for about 10 minutes (11:20:48 to 11:31:08) before the benchmark script exited with an error. The instance was killed immediately at 11:31:09. The entire lifecycle from registration to destruction was about 14 minutes.

Notably, the assistant's message cuts off at "price_per_proof..."—the full JSON response is truncated in the conversation data. But the key information is already visible: the instance was killed because its benchmark rate was 0, below the minimum rate of 29.

Assumptions and Their Validity

The assistant makes several assumptions in this message, each worth examining:

Assumption 1: The root cause is the port 1234 tunnel issue. This is the strongest assumption. It's reasonable because (a) this exact failure mode was recently identified and fixed, (b) the symptom (0 proofs) is consistent with a connectivity failure between curio and Lotus, and (c) the benchmark script exited with an error rather than timing out, suggesting an immediate failure rather than slow performance.

However, this assumption could be wrong. Other possible causes include:

• A different configuration issue specific to the RTX PRO 4000 GPU
• A problem with the cuzk-daemon binary itself on this architecture
• Insufficient memory or other resource constraints
• A race condition in the startup sequence Assumption 2: The instance may have used a cached/stale Docker image. This is a reasonable operational concern, but the assistant doesn't yet have evidence for it. The next logical step (which would occur in subsequent messages) would be to check the instance's startup logs for the image digest or to verify when the instance was created relative to the Docker push. Assumption 3: The benchmark script itself is not the source of the error. The assistant says "benchmark.sh exited with error" but doesn't immediately suspect a bug in the benchmark script. This is a reasonable assumption given that the script was working on other instances, but it's worth noting that the RTX PRO 4000 is a new GPU model that may not have been tested before.

Input Knowledge Required

To fully understand this message, a reader needs knowledge of several systems and concepts:

1. The Vast.ai ecosystem: Vast.ai is a decentralized GPU rental marketplace. Instances are provisioned on remote hosts, and the vast_id (32733029) identifies a specific rental contract.
2. The Curio proving system: Curio is the Filecoin proving daemon that generates proofs (WinningPoSt, WindowPoSt, SnapDeals). It depends on Lotus (the Filecoin node) for chain state. The port 1234 tunnel is how curio communicates with Lotus.
3. The portavailc tunnel mechanism: The entrypoint script sets up reverse tunnels using portavailc to expose ports from the worker instance back to the controller. Port 1234 is Lotus's API port.
4. The benchmark pipeline: New instances run a benchmark (benchmark.sh) that attempts to generate proofs and measures the rate. If the rate is below min_rate, the instance is destroyed by the manager's lifecycle logic.
5. The vast-manager architecture: A centralized controller (running on 10.1.2.104) manages a fleet of worker instances. It tracks their state through a SQLite database and exposes a REST API (port 1235) for the dashboard and offers system.
6. The recent history of the port 1234 fix: Without knowing that the port 1234 tunnel was recently added to the entrypoint, the assistant's hypothesis would seem like a guess. The context of the previous chunk is essential.

Output Knowledge Created

This message produces several valuable outputs:

1. A confirmed failure signature: The dashboard data confirms that the instance was killed with bench_rate: 0 and kill_reason: "bench_rate 0.0 below min_rate 29.0". This is structured evidence that the failure is real and not a data glitch.
2. A refined timeline: The timestamps show that parameter fetching completed successfully (4 minutes), but the benchmark produced zero proofs in 10 minutes. This narrows the problem to the proving phase, not the setup phase.
3. A testable hypothesis: The assistant's hypothesis (port 1234 tunnel) is now documented and ready for verification. The next steps would involve checking the instance's startup logs for tunnel status, or deploying a new instance with explicit tunnel verification.
4. A diagnostic pattern: The assistant's approach—formulate a hypothesis based on recent fixes, consider deployment timing as a confounder, and gather structured data from the centralized API—establishes a reusable pattern for investigating future benchmark failures.

The Thinking Process

The assistant's reasoning in this message is a model of structured diagnostic thinking. Let me trace the logic step by step:

Step 1 — Symptom Recognition: The benchmark produced 0 proofs/hour. This is not a marginal failure (e.g., 28 proofs when 29 were needed); it's a complete failure. The proving pipeline produced nothing.

Step 2 — Hypothesis Generation: The assistant immediately connects this to the most recent relevant fix—the port 1234 tunnel. This is an application of the "recency heuristic" in debugging: recent changes are the most likely cause of new failures. But it's not just heuristic; it's logical. The port 1234 fix was specifically about enabling curio-to-Lotus connectivity, and without that connectivity, curio would produce exactly zero proofs.

Step 3 — Counter-Hypothesis Consideration: The assistant doesn't stop at the first hypothesis. It immediately considers an alternative: the instance may have been deployed with an old image. This shows awareness that the deployment pipeline is not instantaneous—there's a lag between pushing a fix and having all new instances use it.

Step 4 — Data Collection: Rather than speculating further, the assistant gathers data. The choice of data source (the dashboard API) is strategic: it provides a complete picture of the instance's lifecycle without requiring access to the (already destroyed) instance itself.

Step 5 — Pattern Matching: The assistant is implicitly comparing this failure to the previously observed failure mode. The log line "benchmark.sh exited with error" is consistent with the port 1234 issue, where curio would fail immediately rather than producing slow proofs.

The Broader Significance

This message, while brief, sits at a critical juncture in the session. The assistant has just completed a major refactoring (the machine_id migration) and was likely preparing to move on to other work. The benchmark failure pulls attention back to operational concerns. This is the reality of managing a distributed proving fleet: no amount of code cleanup matters if the instances can't actually prove.

The message also reveals the tension between two modes of work: development (refactoring, improving code quality) and operations (debugging production failures, fixing deployment issues). The assistant seamlessly switches between these modes, treating the operational investigation with the same systematic rigor as the code refactoring.

For the reader, this message illustrates a key principle of distributed systems debugging: when a new failure appears, always check whether it's a recurrence of a recently fixed problem before diving into novel diagnostics. The assistant's first instinct is to check if the fix was actually deployed to this instance—a simple, high-leverage diagnostic step that could save hours of investigation.

Conclusion

Message 1476 is a masterclass in concise, hypothesis-driven debugging. In a few sentences, the assistant establishes the problem, formulates a testable hypothesis, acknowledges alternative explanations, and initiates data collection. The message bridges two major phases of work—a completed data integrity refactor and an emerging operational investigation—and demonstrates the kind of structured reasoning that separates effective system management from reactive firefighting.

The investigation is not yet complete at the end of this message. The assistant has gathered data confirming the failure but has not yet determined whether the port 1234 hypothesis is correct. That work will unfold in subsequent messages. But the foundation is laid: a clear hypothesis, a structured dataset, and a diagnostic path forward. This is the essence of operational excellence in distributed systems.