A Surgical Deployment: Fixing the SSH Pile-Up That Broke the Vast-Manager UI

The Message

[assistant] [bash] scp /tmp/czk/vast-manager-agent theuser@10.1.2.104:/tmp/vast-manager-agent && ssh theuser@10.1.2.104 "
sudo systemctl stop vast-manager && sleep 1
sudo cp /tmp/vast-manager-agent /usr/local/bin/vast-manager
sudo systemctl start vast-manager && sleep 2
curl -sf -o /dev/null -w 'UI: %{http_code} %{time_total}s\n' http://127.0.0.1:1236/
curl -sf -o /dev/null -w 'Dashboard: %{http_code} %{time_total}s\n' http://127.0.0.1:1236/api/dashboard
journalctl -u vast-manager --since '3 sec ago' --no-pager | tail -5
" 2>&1
UI: 200 0.001379s
Dashboard: 200 0.012640s
Mar 17 11:40:50 vast-arb-host systemd[1]: Started vast-manager.service - Vast Worker Manager.
Mar 17 11:40:50 vast-arb-host vast-manager[583672]: 2026/03/17 11:40:50 Connected to Curio DB: host=127.0.0.1 port=5433 user=yugabyte dbname=yugabyte sslmode=disable options=-csearch_path=curio
Mar 17 11:40:50 vast-arb-host vast-manager[583672]: 2026/03/17 11:40:50 Web UI listening on 0.0.0.0:1236
Mar 17 11:40:50 vast-arb-host vast-manager[583672]: 2026/03/17 1...

On its surface, this message appears to be a routine deployment — copy a binary, restart a service, verify it works. But beneath this mundane shell script lies the culmination of a focused debugging session that uncovered a subtle concurrency failure in a production GPU-proving infrastructure. The message is the final act of a mini-drama: the deployment of a fix for an SSH process pile-up that had silently degraded the vast-manager's web UI to the point of apparent unresponsiveness.

The Crisis: A UI That Wouldn't Load

Moments before this message, the user had reported a critical problem: "Manager UI does not load, something crashed?" ([msg 4539]). The vast-manager — the central control plane for a fleet of GPU instances running CuZK proof generation — had become inaccessible. For an operator managing dozens of remote proving machines, a dead UI means blind operations: no visibility into instance health, no demand monitoring, no ability to intervene when things go wrong.

The assistant's initial investigation ([msg 4540]) revealed something puzzling: the service was running fine. Systemd reported it as "active (running)" with normal memory usage (50.6 MB) and CPU (0.3%). No crash, no OOM, no panic. Yet the user couldn't load the interface. This discrepancy between "service is healthy" and "UI is unresponsive" is precisely the kind of bug that frustrates operators — the system reports green while delivering brown.

The Debugging Trail: Uncovering the SSH Accumulation

The assistant's reasoning in [msg 4543] shows the critical insight: "There are a LOT of SSH processes piling up — all trying to curl the cuzk status on the same host." The UI, when an instance is expanded, polls /api/cuzk-status/{uuid} every 1.5 seconds. Each poll spawns an SSH process to the target machine, which runs a curl command against the local cuzk daemon's status endpoint. If the target host is slow, unreachable, or has high latency, these SSH processes don't complete quickly — they accumulate.

The assistant checked the SSH process count and found it had already dropped to 1 (the lingering ControlMaster socket), suggesting the pile-up was transient and had cleared by the time of investigation. But the evidence was clear: the handler in main.go had a ConnectTimeout=5 on the SSH connection and --max-time 3 on the inner curl, meaning each request could take up to 8 seconds to fail. With the UI polling every 1.5 seconds, requests would queue faster than they could drain, eventually exhausting file descriptors, goroutine pools, or simply making the HTTP server appear hung to the browser.

This is a classic failure mode in distributed systems: a component that works correctly under normal conditions becomes a bottleneck under degraded conditions, and the degradation is self-reinforcing. Slow connections cause more concurrent requests, which consume more resources, making everything slower.

The Fix: A Context Timeout

The assistant's fix, implemented in [msg 4547], was surgically precise: add a Go context timeout to the entire SSH command execution. The edit wrapped the exec.Command execution with a context that would cancel the entire operation after a hard deadline. This is a fundamentally different approach from the existing timeouts — instead of setting timeouts on individual sub-components (SSH connect timeout, curl max-time), it imposes a single, non-negotiable deadline on the entire operation. If the SSH connection, the remote curl, or the data transfer exceeds this limit, the entire process is killed, the file descriptor is released, and the handler returns an error to the UI.

The choice to use Go's context.Context mechanism rather than, say, reducing the poll interval or implementing connection pooling, reveals the assistant's design philosophy: prefer bounded operations over optimistic ones. The existing code had attempted to be safe with individual timeouts, but these created a combinatorial worst case (5s + 3s = 8s). A single context timeout eliminates that combinatorial explosion. It's a defensive programming pattern: "this operation must complete within X seconds, no matter what."

The Deployment: Verification as a First-Class Concern

Message 4551 executes this fix in production. The deployment script is structured as a pipeline with verification gates at every stage:

1. Transfer: scp the binary to the management host
2. Stop: systemctl stop with a 1-second grace period
3. Replace: copy the binary into /usr/local/bin/
4. Start: systemctl start with a 2-second settling period
5. Verify UI: curl the root endpoint, check HTTP 200 and response time
6. Verify API: curl the dashboard endpoint, check HTTP 200 and response time
7. Verify Logs: journalctl to confirm clean startup This is not a "deploy and hope" approach. Every step has a verification. The -sf flags on curl (silent, fail on error) ensure that any non-200 response will cause the script to abort. The -w format strings extract response times, giving immediate feedback on performance. The sleep commands between stop and start provide a safety margin for port release and process cleanup. The output confirms success: the UI responds in 1.3 milliseconds, the dashboard in 12.6 milliseconds. The journal shows a clean startup — database connection established, web UI listening on port 1236. The truncated final line suggests normal operation continuing without incident.

Assumptions and Decisions

This message rests on several key assumptions. First, that the SSH pile-up was indeed the root cause of the UI unresponsiveness — an assumption supported by the debugging evidence but not proven in a controlled test. The assistant implicitly assumes that fixing the timeout will prevent future occurrences, even though the immediate symptom (UI not loading) had already cleared by the time of investigation.

Second, the deployment assumes that a context timeout of approximately 3-5 seconds (the value used in the edit) is appropriate for all target hosts. This is a judgment call: too short, and legitimate slow connections from distant vast.ai instances would always fail; too long, and the pile-up risk returns. The assistant chose a value that matches the existing curl --max-time 3, effectively making the outer SSH timeout redundant with the inner one — but with the critical difference that the Go context can cancel the entire process tree, not just wait for the curl to exit.

Third, the message assumes that a rolling restart (stop, replace, start) is safe for a single-instance management service. There is no blue-green deployment, no load balancer, no graceful connection draining. The 1-second sleep between stop and start is the only concession to safety. For a service that manages GPU instances but is not itself processing proofs, this risk is acceptable — a few seconds of downtime during the restart is preferable to the complexity of zero-downtime deployment.

Input Knowledge Required

To fully understand this message, one needs familiarity with several domains. The SSH ControlMaster and ControlPersist options — the existing code used ControlPersist=120 to reuse SSH connections, but this only helps after the first connection is established. The Go os/exec package and its interaction with context.Context — the fix required importing the context package and wrapping command execution. The vast.ai instance lifecycle — understanding why cuzk-status polling is necessary and why target hosts might be slow. The systemd service management commands — systemctl stop/start and the --since flag on journalctl. And crucially, the architecture of the vast-manager itself — that it serves as an HTTP proxy to SSH into remote instances for health checks.

Output Knowledge Created

This message creates several pieces of knowledge. First, it confirms that the fix compiles and deploys successfully — the build in [msg 4550] succeeded, and the deployment in this message confirms runtime correctness. Second, it establishes a baseline for UI and API response times (1.3ms and 12.6ms respectively) that can be used for future performance monitoring. Third, it documents the startup sequence in the journal, providing a forensic record for future debugging. Fourth, it validates the hypothesis that the SSH pile-up was the root cause — if the UI remains stable after this deployment, the hypothesis is confirmed.

The Broader Pattern

This message exemplifies a pattern that recurs throughout the opencode session: the assistant builds features rapidly, encounters production failures, diagnoses them with system-level debugging, and deploys surgical fixes with rigorous verification. The SSH pile-up bug was not a logic error or a data race — it was a resource leak caused by unbounded concurrent operations. The fix was not to reduce concurrency or add a mutex, but to bound the lifetime of each operation. It's a lesson in defensive systems design: every operation that touches an external resource must have a hard deadline, because external resources are the one thing you cannot control.

The message also reveals the assistant's operational maturity. The deployment script includes verification steps that test not just that the service starts, but that it actually serves requests with acceptable latency. This is the difference between "deploying" and "delivering" — the former pushes code, the latter ensures the system works for its users.