The Parallel Autopsy: How a Fleet-Wide Crash Was Diagnosed in Six SSH Commands

In the life of any distributed system, there comes a moment when the abstractions fall away and an operator must reach directly into the machines to see what is actually happening. Message 4345 in this coding session is precisely such a moment. It is the turning point where a prolonged, frustrating struggle with authentication barriers, database queries, and indirect monitoring suddenly gives way to a burst of direct, parallel SSH connections — six simultaneous probes into six remote machines, each returning a verdict that would reshape the entire trajectory of the project.

The message is deceptively simple on its surface: a series of SSH commands, each checking process lists and log tails on a remote GPU instance. But beneath that technical veneer lies a rich story of diagnostic reasoning, architectural discovery, and the kind of systems-level debugging that separates effective infrastructure engineering from guesswork. This article examines that single message in depth — the context that demanded it, the reasoning that shaped it, the assumptions it validated and shattered, and the knowledge it produced.

The Crisis That Demanded This Diagnosis

To understand why message 4345 exists, we must first understand the crisis that preceded it. The user had deployed a fleet of GPU instances on vast.ai — a mix of RTX 5090s, RTX 4090s, an RTX PRO 4000, and a dual-A40 machine — all running a custom Docker image (theuser/curio-cuzk:latest) that combined the cuzk GPU proving daemon with curio, the Filecoin storage provider's task scheduler. These machines were the proving backbone of a decentralized storage operation, and they were failing silently.

The user's message at <msg id=4335> was terse and urgent: "Seems some node crashed and: 1. didn't recover (asked you to implement cuzk restart on crash, was that done?), 2. crashed - not good - go through vast-manager nodes that are running, see which ones are crashed and debug why." This was a production outage. Proving capacity was offline, and the supervisor loop that was supposed to automatically restart crashed daemons had apparently failed.

The assistant's first response (<msg id=4336>) was methodical: check the entrypoint script for crash recovery logic, enumerate all instances, and prepare to debug. But the next several messages revealed a frustrating obstacle: the vast-manager host (the central management server) did not have its SSH key authorized on the worker nodes. Every attempt to SSH from the manager to the workers was met with "Permission denied (publickey)." The assistant tried multiple approaches — direct IPs, vast.ai relay addresses, database queries, journalctl logs — but could not reach into the machines themselves.

This is the context that makes message 4345 so significant. After several rounds of failed SSH attempts through the vast-manager proxy, the assistant tried a different approach: connecting directly from its own machine (the machine running the coding session) rather than through the vast-manager. And it worked.

The Discovery That Changed Everything

The message opens with a critical realization: "This machine's key works." This single sentence represents a breakthrough. The assistant's own SSH key — the one belonging to the user's machine — was authorized on the vast.ai instances, even though the vast-manager's key was not. This asymmetry would prove decisive.

But the assistant does not stop to celebrate. It immediately observes something alarming about the first node it checks (the RTX 5090 test instance, ID 32790145): "The test node (32790145) has curio running but no cuzk daemon — only curio run." This is the first concrete evidence of the crash pattern. The cuzk GPU proving daemon is gone, but curio — the task scheduler — is still alive. The supervisor loop, which should have detected the crash and restarted cuzk, has done nothing.

The assistant then executes a remarkable diagnostic maneuver: six parallel SSH commands, one for each running instance. This is not a cautious, sequential probe. It is a full sweep, a simultaneous biopsy of the entire fleet. The parallelism is deliberate — when a production system is failing, time is critical, and the assistant needs a complete picture before it can formulate a fix.

What the Six Probes Revealed

Each SSH command follows the same template: check process lists for cuzk, curio, and entrypoint; tail the last 10 lines of the entrypoint log; tail the last 10 lines of the daemon log. The results paint a devastating picture:

RTX 5090 test (32790145): The entrypoint script (PID 366) is running. curio (PID 5518) is running. But cuzk is absent. The entrypoint log shows the last supervisor message — "Supervisor: monitoring cuzk (PID=4527) and curio (PID=5518)" — but no restart has occurred. The daemon log ends with proxy connection errors from March 13. cuzk has been dead for days.

RTX 4090 (32874928): Same pattern. Entrypoint (PID 173) and curio (PID 6906) are alive, but no cuzk process. The daemon log shows activity from March 14, then silence. The entrypoint log is polluted with proxy "CloseWrite" errors, obscuring whatever happened to the supervisor.

A40 (32854594): Permission denied. This node remains inaccessible, a blind spot in the diagnosis.

RTX PRO 4000 (32915747): Same pattern again. Entrypoint (PID 504) and curio (PID 6565) are alive. No cuzk. The daemon log ends with budget fallback messages — "budget fallback to unpinned" — suggesting the pinned memory pool ran into trouble before the process died.

RTX 5090 new1 (32919934): The sole healthy node. cuzk (PID 8913) is running at 2722% CPU — actively proving. curio (PID 9174) is alive. This is the exception that proves the rule.

RTX 5090 new2 (32919936): Back to the crash pattern. Entrypoint (PID 458) and curio (PID 7621) are alive. No cuzk. The daemon log shows the pool had 132 live buffers and 415 GiB allocated before the process vanished.

The pattern is unmistakable: 4 out of 6 accessible nodes have lost their cuzk daemon, and the supervisor has failed to restart it on every single one. Only one node is healthy. One is unreachable.

The Reasoning Behind the Probe Design

The assistant's thinking process, visible in the reasoning sections of preceding messages, reveals why these particular commands were chosen. The assistant had already established that the supervisor loop in entrypoint.sh used wait -n "$CUZK_PID" "$CURIO_PID" to detect process exits. The hypothesis forming in the assistant's mind was that wait -n was failing to notice when cuzk died — a known reliability issue in bash where the wait builtin can miss already-reaped children.

The probe design reflects this hypothesis. By checking process lists, the assistant can confirm which PIDs are alive. By checking the entrypoint log, it can see whether the supervisor logged any restart attempts. By checking the daemon log, it can see when cuzk last produced output and whether there were any error messages before death. The combination of these three data sources for each node provides a complete picture of the failure mode.

The assistant also checks for entrypoint specifically, not just cuzk and curio. This is important because if the entrypoint script itself had crashed, the entire container would restart (Docker's default behavior). But the entrypoint is still running on all nodes, which means the supervisor loop is alive but stuck — it's not crashing, it's just not working.

Assumptions Made and Validated

Several assumptions underpin this diagnostic sweep. The first is that the SSH key on the assistant's machine would work where the vast-manager's key did not. This was a gamble based on the observation that the user had manually deployed instances and likely authorized their own key. It paid off.

The second assumption is that the process list and log files would be sufficient to diagnose the failure. The assistant does not check GPU state, CUDA errors, or kernel logs in this message — those come later. The assumption is that the presence or absence of the cuzk process, combined with the entrypoint's behavior, will reveal the root cause.

The third assumption is that the failure is consistent across nodes. By checking all six in parallel, the assistant implicitly assumes that the same root cause is affecting multiple machines. This assumption is validated: 4 of 6 show the identical pattern of cuzk dead, curio alive, supervisor stuck.

A subtle but important assumption is that the entrypoint log is reliable. The assistant later discovers that the entrypoint log has been polluted by proxy "CloseWrite" error messages, which may have obscured supervisor output. But at this stage, the absence of restart messages in the log is taken as evidence that no restart was attempted.

Mistakes and Blind Spots

The most significant blind spot in this message is the A40 node (32854594), which returns "Permission denied" from both direct and relay SSH attempts. This node remains a mystery throughout the diagnostic phase. The assistant never gains access to it, and its status is inferred only from the vast-manager database (which shows it as "running").

Another limitation is that the assistant does not yet check for OOM kills, core dumps, or GPU driver errors. The daemon logs end abruptly — "cuzk died mid-synthesis" is the working hypothesis — but the actual cause of death is not determined in this message. That investigation comes in subsequent messages, where the assistant checks dmesg for OOM events and examines the daemon's last log lines more carefully.

The assistant also assumes that the wait -n failure is the sole reason for the supervisor's inaction. Later analysis reveals a more complex picture: set -euo pipefail at the top of the entrypoint script means that if wait -n returns a non-zero exit code (as it would when cuzk dies from a signal), the entire script would exit — but the script is still running, which suggests wait -n never returned at all. The entrypoint process is stuck in the do_wait kernel state, waiting for a child that has already been reaped.

The Knowledge Produced

Message 4345 produces several critical pieces of knowledge that reshape the subsequent work:

1. The crash pattern is fleet-wide, not isolated. Four of six nodes show the same failure mode. This rules out hardware-specific issues (bad GPU, faulty RAM) and points to a systemic software problem.
2. The supervisor loop is broken, not missing. The entrypoint script has restart logic, but it's not executing. The wait -n mechanism is failing silently.
3. The crash is silent. No OOM, no panic message in the daemon logs, no core dump. cuzk simply stops producing output and vanishes from the process list.
4. The healthy node provides a control case. Node 32919934 (RTX 5090 new1) has cuzk running at 2722% CPU, actively processing snap-update work. This confirms that the software can work correctly — the failure is environmental or timing-related, not a universal bug.
5. The daemon logs show budget-related messages before death. The RTX PRO 4000 node's last log lines mention "budget fallback to unpinned," suggesting the pinned memory pool integration may be involved in the crash. This knowledge directly drives the next actions: the assistant will examine the entrypoint process state more carefully (finding it stuck in do_wait), diagnose the wait -n bug, and ultimately replace the supervisor loop with a reliable polling mechanism using kill -0. The parallel SSH sweep is the diagnostic turning point — before it, the assistant was guessing about the failure mode; after it, the evidence is clear and actionable.

The Architecture of a Diagnostic Message

What makes message 4345 exemplary as a diagnostic artifact is its structure. It begins with a summary of what was learned from the first probe (the test node), then dispatches five more probes in parallel. The results are presented raw — no filtering, no interpretation, just the command outputs as they came back from the machines. This fidelity to the source data is crucial in production debugging, where interpretation can introduce bias.

The message also demonstrates a key principle of distributed systems debugging: when you can't see into a machine through your monitoring tools, you must find another way in. The assistant spent several messages trying to use the vast-manager as a proxy, failing repeatedly. The breakthrough came from recognizing that the authentication boundary was not where it seemed — the user's machine had access that the management server did not.

Conclusion

Message 4345 is the moment when a confusing, multi-faceted production outage suddenly resolves into a clear pattern. Six SSH commands, executed in parallel, reveal that the cuzk GPU proving daemon has crashed on 4 of 6 nodes, and the supervisor loop designed to restart it is broken. The wait -n mechanism in bash — a seemingly simple tool for process supervision — has failed silently, leaving the fleet in a state of suspended animation where curio runs on but no proving work gets done.

This message is a testament to the value of direct observation in systems debugging. No amount of log analysis, database queries, or API calls could have replaced the simple act of SSHing into each machine and checking what processes were actually running. In a world of increasingly sophisticated monitoring and observability platforms, sometimes the most effective diagnostic tool is still a shell prompt and a clear hypothesis about what to look for.