The Moment of Truth: Monitoring a Distributed Proving System in the Wild

Introduction

In the complex ecosystem of distributed GPU proving for Filecoin, the gap between a system that should work and one that does work is measured in moments of careful observation. Message 1035 captures one such moment—a brief diagnostic snapshot taken by the AI assistant during the deployment and validation of a distributed cuzk proving network on Vast.ai GPU instances. At first glance, it appears to be a routine status check: two SSH commands, one checking a management API and another checking an instance's log output. But beneath this surface lies a rich tapestry of system architecture, debugging methodology, and the constant tension between assumptions and reality that defines infrastructure engineering.

The Message

[assistant] Interesting! The Norway instance just finished downloading params and is now starting the benchmark. And the API seems to be returning something unexpected. Let me check the raw API response and the Norway instance progress.
[bash] ssh 10.1.2.104 'curl -s http://127.0.0.1:1235/api/instances' 2>/dev/null | head -200
404 page not found

[bash] ssh -p 41122 root@141.195.21.87 'cat /var/log/entrypoint.log 2>/dev/null | tail -20' 2>/dev/null
[#bdd1a3 56GiB/56GiB(99%) CN:13 DL:110MiB ETA:4s]
[#bdd1a3 56GiB/56GiB(99%) CN:13 DL:110MiB ETA:3s]
[#bdd1a3 56GiB/56GiB(99%) CN:12 DL:111MiB ETA:2s]
[#bdd1a3 56GiB/56GiB(99%) CN:9 DL:110MiB ETA:1s]
[#bdd1a3 56GiB/56GiB(99%) CN:5 DL:109MiB]

03/12 01:36:23 [NOTICE] Download complete: /var/tmp/filecoin-proof-parameters/v28-proof-of-spacetime-fallback-merkletree-poseidon_hasher-8-8-0-0377ded656c6f524f1618760bffe4e0a1c51d5a70c4509eedae8a27555733edc.params

Download Results:
gid   |stat|a...

WHY: The Reasoning and Motivation

This message was written at a critical inflection point in the deployment workflow. The assistant had just spent several rounds diagnosing and fixing an Out-of-Memory (OOM) crash that plagued the BC Canada instance (2× RTX 3090, 125GB RAM) during its first warmup proof. That crash was traced to the cuzk daemon spawning too many partition synthesis workers simultaneously when no Pre-Compiled Constraint Evaluator (PCE) cache existed, causing the operating system's OOM killer to terminate the process. The Norway instance (1× RTX 4090, 500GB RAM) was the control case—a machine with sufficient memory that should complete its benchmark successfully.

The assistant's motivation for writing message 1035 was twofold. First, it needed to verify that the Norway instance was progressing through its lifecycle correctly: parameter download → warmup proof → batch benchmark → transition to running state with the cuzk+curio supervisor loop. Second, it needed to investigate a newly surfaced problem with the vast-manager API, which had stopped returning expected JSON responses. The "404 page not found" error was alarming because the manager is the central orchestrator—it tracks instance states, enforces lifecycle transitions, kills underperforming instances, and provides the web dashboard. A broken API meant the entire management layer was potentially offline.

The timing is significant. The assistant's preceding messages (1030–1034) had established a todo list that included "Check Norway instance benchmark status" as the highest priority item. The Norway instance was the only successful deployment so far, and its benchmark results would determine whether the hardware-aware configuration logic (partition workers, concurrency) was correct for machines with adequate RAM. If Norway failed too, the entire deployment strategy would need fundamental rethinking.

HOW: Parallel Diagnostics and Decision Architecture

The message executes two diagnostic commands in parallel, a pattern that reveals the assistant's approach to efficient troubleshooting. Both commands are dispatched in the same round because they are independent—neither depends on the other's output. This parallelism is a hallmark of the opencode session model, where all tool calls in a single message are issued simultaneously, and the assistant waits for all results before proceeding.

The first command targets the vast-manager API on the controller host (10.1.2.104). The assistant uses curl -s http://127.0.0.1:1235/api/instances to query the instance list. Critically, it pipes through head -200 to capture the raw response, suggesting the assistant anticipated either a very large JSON payload or—more likely—an unexpected response format that would need manual inspection. The choice to query via localhost rather than the external IP is deliberate: earlier attempts (msg 1031) had failed when using the external address, and msg 1034 confirmed the port was listening but returned non-JSON data. By using 127.0.0.1, the assistant eliminates network routing as a variable.

The second command checks the Norway instance's entrypoint log via SSH. The assistant uses tail -20 to capture the most recent 20 lines, a standard technique for monitoring progress without overwhelming output. The log shows the final stages of a parameter download—the v28-proof-of-spacetime-fallback-merkletree-poseidon_hasher parameter file, 56GiB in size, at 99% completion. This is one of the large Filecoin proof parameters that must be downloaded at runtime (not baked into the Docker image) due to its size.

The assistant's commentary—"Interesting! The Norway instance just finished downloading params and is now starting the benchmark"—reveals its interpretation of the log output. However, this interpretation contains a subtle inaccuracy: the log shows the download at 99% (not yet complete) and the "Download Results" section is truncated, so the benchmark hasn't actually started yet. The assistant is projecting the expected next state based on the lifecycle logic.

Assumptions and Their Consequences

Several assumptions underpin this message, and some prove incorrect. The most significant assumption is that the vast-manager API should be returning a JSON array of instances at /api/instances. The "404 page not found" response contradicts this expectation. The assistant had previously (msg 1034) received a response from this endpoint that caused a JSON parse error, suggesting the API was returning something—just not valid JSON. Now it returns a pure 404. This could indicate:

1. The vast-manager binary was restarted with a different code version that lacks the API routes
2. A routing conflict with another service on port 1235
3. The API endpoint path changed The assistant does not immediately act on this finding within the same message—it cannot, because tool results are only available in the next round. But the 404 sets up the next diagnostic step: investigating why the manager is broken. Another assumption is that the Norway instance's parameter download is the initial download, happening as part of the benchmark preparation. But looking at the context, the Norway instance had already completed its warmup proof and started its batch benchmark in msg 1026. The fact that it's downloading parameters again suggests either: (a) the instance was destroyed and recreated, (b) the entrypoint restarted the daemon after a crash, triggering a fresh parameter fetch, or (c) the download shown is for a different parameter file needed for the next phase. The assistant assumes interpretation (c)—that this is the natural progression from params_done to benchmark—but the evidence is ambiguous.

Input Knowledge Required

To fully understand this message, one must be familiar with several layers of the system architecture:

The Vast.ai ecosystem: Vast.ai is a marketplace for renting GPU compute. Instances are created with Docker images, SSH access, and environment variables. The --onstart-cmd mechanism runs a custom entrypoint alongside SSH. The VAST_CONTAINERLABEL and CONTAINER_ID variables identify instances but are only visible to the entrypoint process, not SSH sessions.

The cuzk proving engine: cuzk is a CUDA-accelerated proof generation engine for Filecoin. It requires large parameter files (56GiB for PoRep proofs) downloaded at runtime to FIL_PROOFS_PARAMETER_CACHE. The PCE (Pre-Compiled Constraint Evaluator) cache is generated during the first proof and dramatically reduces memory usage for subsequent proofs.

The vast-manager service: A Go HTTP service running on the controller host (10.1.2.104) that manages instance lifecycle. It maintains a SQLite database of instances, runs a background monitor that syncs with Vast.ai's API, and provides both a REST API (port 1235) and a web UI (port 1236).

The entrypoint lifecycle: The Docker entrypoint script (entrypoint.sh) orchestrates the full instance lifecycle: tunnel setup → registration with vast-manager → parameter download → benchmark → transition to cuzk+curio supervisor mode. Each phase is logged to /var/log/entrypoint.log.

The SSH infrastructure: The assistant uses SSH to both the controller host and the Vast instances. The Norway instance is accessible via ssh -p 41122 root@141.195.21.87, using Vast.ai's port-forwarding mechanism.

Output Knowledge Created

This message produces two critical pieces of information that shape the subsequent conversation:

1. The vast-manager API is broken: The 404 response indicates a fundamental issue with the management layer. This is a blocking problem—without the manager, instance lifecycle automation (benchmark timeout killing, state transitions, bad host detection) cannot function. The assistant will need to diagnose and fix the manager before proceeding with further deployments.
2. The Norway instance's parameter download is completing: The log shows the 56GiB parameter file at 99% with an ETA of seconds. This confirms the runtime parameter fetch mechanism works correctly—the Docker image does not bundle parameters, relying instead on the entrypoint to download them at startup. The download speed (~110 MiB/s) is reasonable for a network transfer of this magnitude. The message also implicitly confirms that the Norway instance has not crashed or OOM-killed, unlike its BC Canada counterpart. The entrypoint process is alive, the download is progressing, and the GPU is presumably idle (0% utilization reported in msg 1032) while the CPU-bound download completes.

The Thinking Process Revealed

The assistant's reasoning is visible in both the commentary and the tool selection. The phrase "Interesting!" signals that the assistant has encountered an unexpected state—the API returning something other than the expected JSON. The decision to pipe through head -200 rather than parsing with python3 -m json.tool (as done in msg 1031) shows adaptive thinking: the assistant recognized that the previous parsing approach failed, so it switched to raw inspection.

The parallel dispatch of two commands reveals a prioritization: the manager API issue is potentially more critical than the Norway instance's progress, but both need checking simultaneously because they are independent. If the assistant had checked them sequentially, it would have wasted a round waiting for the first result before dispatching the second.

The truncated log output is also revealing. The assistant uses tail -20 to get the last 20 lines, but the download progress lines (showing 99% with ETA) dominate the output, and the "Download Results" section is cut off. The assistant cannot see whether the download completed successfully or what happens next. This limitation is inherent to the tail approach—it only shows the end of the file, not the full transition between lifecycle phases.

Conclusion

Message 1035 is a diagnostic snapshot that captures a system in transition. The Norway instance is on the verge of completing its parameter download and beginning the benchmark that will validate the entire deployment strategy. The vast-manager API has broken, threatening the orchestration layer that ties the distributed system together. The assistant's response—parallel checks, adaptive tooling, and cautious interpretation—reflects the方法论 of infrastructure debugging: gather data from multiple sources, compare against expectations, and prepare for the next round of investigation. In the broader narrative of this coding session, this message marks the moment when the deployment shifted from "watching things work" to "fixing things that broke," a transition that would ultimately lead to a fundamental redesign of the system architecture.