The Nuclear Option: Killing 11 GPU Instances to Fix a Port Forwarding Bug

Introduction

In distributed systems operations, there comes a moment when careful surgical fixes give way to a more drastic approach: tear it all down and start fresh. Message [msg 1427] captures exactly such a moment. In this single command, the assistant terminates 11 active GPU proving instances across the Vast.ai marketplace, wiping out an entire deployment in one fell swoop. The message is deceptively simple—a single bash loop wrapped in an SSH command—but it represents the culmination of a debugging chain that began with a crashed Curio daemon and ended with a fundamental infrastructure fix that could only be applied by rebuilding from scratch.

This article examines that message in depth: why it was necessary, how the decision was reached, the assumptions that underpinned it, and the knowledge it both required and produced. It is a study in operational pragmatism—the recognition that sometimes the fastest path to a fix is to burn down the current deployment and let the system rebuild itself.

The Message

The assistant executed the following command:

ssh 10.1.2.104 'for vid in 32730605 32730587 32730597 32730374 32730338 32730345 32730377 32729803 32729775 32729742 32730578; do echo "Killing $vid..."; curl -s -X POST http://localhost:1235/api/kill -H "Content-Type: application/json" -d "{\"vast_id\": $vid}"; echo; done'

The output was uniform for all 11 instances:

Killing 32730605...
{"ok":true}

Every instance acknowledged the kill command successfully. The operation was clean, complete, and irreversible.

Why This Message Was Written: The Root Cause

The chain of events leading to this message began with a seemingly ordinary operational failure. A user reported that Curio—the Filecoin proving daemon—was failing to start on a deployed instance ([msg 1408]). The error log showed a connection refused error when trying to reach the Lotus API at ws://127.0.0.1:1234/rpc/v1. This was puzzling because the system relied on portavailc, a secure TCP tunnel client, to forward ports from the controller host to the worker instances. Port 1234 was in the controller's portavaild configuration, so why wasn't it reachable?

The assistant traced the issue to the entrypoint script (entrypoint.sh) that runs on each worker instance at startup ([msg 1413]). The portavailc command was being invoked with only three forwarded ports: -L 1235 -L 5433 -L 9042. Port 1234—the critical port for the Lotus API—was missing from the list. This was a simple but devastating omission: the tunnel simply wasn't carrying the traffic Curio needed to start.

The fix itself was trivial: add -L 1234 to the portavailc invocation in the entrypoint script. The assistant applied this edit and rebuilt the Docker image (<msg id=1424-1425>). But applying the fix to the running instances was the real challenge.

The Decision Tree: Why In-Place Fixes Failed

Before resorting to mass termination, the assistant explored less disruptive options. The first attempt was to fix the running instances in-place by SSHing into each one and restarting portavailc with the corrected port list (<msg id=1417-1418>). This worked for one instance (the RTX PRO 4000 at 141.0.85.200:41071), where the assistant manually killed the old portavailc process and launched a new one with the -L 1234 flag.

Encouraged by this success, the assistant attempted to scale the fix to all other active instances (<msg id=1420-1421>). This failed spectacularly. The SSH connections succeeded in the sense that they reached the Vast.ai instances, but the commands were never executed. The instances returned a "Welcome to vast.ai" banner and then immediately rejected the SSH key with "Permission denied (publickey)" (<msg id=1422-1423>). Even when the assistant tried routing the SSH through the controller host (which presumably had the correct key), the result was the same ([msg 1423]).

The root cause of this SSH failure is worth examining. Vast.ai instances use a key-based authentication system where the user's public key must be uploaded to the Vast.ai platform and injected into instances at creation time. The controller host's SSH key was apparently not configured in this way, or the instances were created before the key was added. Either way, the instances were effectively sealed—no external SSH access was possible except through the Vast.ai web console or API.

This left the assistant with only one viable path: rebuild the Docker image with the fix, push it to the registry, terminate all existing instances, and let the deployment pipeline create new instances from the corrected image. The kill command in [msg 1427] is the execution of that decision.## Assumptions Underpinning the Decision

The assistant made several assumptions in choosing the mass-kill approach, most of which were validated by the context but deserve scrutiny.

Assumption 1: The instances were disposable. The assistant assumed that terminating all 11 instances would cause no permanent data loss. This was reasonable given the architecture: each instance was a worker node running a standardized Docker container, with all persistent state (proving parameters, configuration) either stored on the controller or fetched at startup. The param_done state on many instances indicated they had already downloaded the Filecoin proving parameters (~100+ GB), but those would need to be re-downloaded on the new instances. The assistant implicitly accepted this cost.

Assumption 2: The vast-manager would handle cleanup gracefully. The kill endpoint (/api/kill) was a custom API on the vast-manager service running on the controller. The assistant assumed that calling this endpoint would properly mark instances as killed, trigger any necessary cleanup on Vast.ai's side, and allow the deployment pipeline to create replacements. The uniform {&#34;ok&#34;:true} responses suggested this assumption held.

Assumption 3: No instance was in a critical state. The assistant assumed that none of these instances were mid-proof or performing work that would be lost. This was a reasonable inference from the states shown: most were params_done (finished parameter download, awaiting benchmark) or registered (just appeared, not yet started). None were in a running or proving state that would indicate active work.

Assumption 4: The fix was complete and correct. By adding -L 1234 to the entrypoint, the assistant assumed that port 1234 forwarding was the only issue preventing Curio from starting. This was a narrow assumption—if there were additional configuration problems (e.g., the Lotus API wasn't actually listening on 1234, or the Yugabyte connection was also broken), the new instances would fail for different reasons.

Mistakes and Incorrect Assumptions

The most notable mistake was the failed SSH-based fix attempt (<msg id=1420-1423>). The assistant initially tried to fix instances by iterating over SSH commands in a bash loop, but a subtle bug—a leading space in the connection string—caused ssh to interpret the arguments incorrectly, producing "Bad port" errors. The assistant corrected this by switching to a pipeline-based approach that parsed SSH commands from the dashboard API, but by then the underlying SSH key issue had already doomed the approach.

This sequence reveals a pattern of escalating attempts: first a manual fix on one instance (success), then an automated loop over all instances (failed due to string parsing), then a corrected pipeline (failed due to SSH key configuration). The assistant could have saved time by first testing SSH connectivity to a representative instance before writing the loop, but the urgency of the situation—multiple instances potentially stuck in a crash loop—justified the rapid iteration.

A more subtle issue is whether killing all instances was truly necessary. The assistant had successfully fixed one instance manually. Could the same approach have been applied to all instances via the Vast.ai web console or API directly, bypassing SSH? The Vast.ai platform provides a "restart" button that re-runs the instance's --onstart-cmd, which could have been used to restart the container with the fixed entrypoint. However, this would have required the new Docker image to already be deployed, which it was not at the time of the kill command. The assistant's approach—kill first, rebuild second—was the fastest path given the constraints.

Input Knowledge Required

To understand this message fully, one needs knowledge in several domains:

  1. Vast.ai instance lifecycle: Instances are rented GPU machines that run a user-provided Docker image. They are created, run for a duration, and can be destroyed at any time. The vast_id is Vast.ai's internal identifier for each instance.
  2. The portavail tunneling system: portavailc (client) and portavaild (daemon) form a secure TCP tunnel that forwards ports from a controller host to worker instances. This allows services running on the controller (Lotus API on port 1234, Yugabyte on 5433, etc.) to be accessed from worker instances as if they were local.
  3. The Curio proving stack: Curio is the Filecoin proving daemon that coordinates proof generation. It connects to a Lotus chain node via WebSocket on port 1234. Without this connection, Curio exits immediately with an error.
  4. The vast-manager API: The custom management service running on the controller provides endpoints like /api/kill to terminate instances. The assistant was intimately familiar with this API, having built it in previous segments.
  5. Bash scripting and SSH: The command uses a for loop over a space-separated list of IDs, with curl to POST to the API. The output is a simple JSON response per instance.

Output Knowledge Created

This message produced several forms of output knowledge:

  1. Operational state change: The 11 instances transitioned from active (registered/params_done) to killed. This was recorded in the vast-manager's SQLite database, with metadata persisted for historical tracking (as established in <msg id=1405-1407>).
  2. Confirmation of API correctness: The uniform {&#34;ok&#34;:true} responses validated that the /api/kill endpoint worked correctly for all instances, including those in different states (registered vs. params_done).
  3. A clean slate for redeployment: With all instances terminated, the deployment pipeline could create new instances from the fixed Docker image without any stale state or configuration drift.
  4. A lesson in SSH key management: The failed SSH attempts revealed that the controller host lacked direct SSH access to worker instances, which is important operational knowledge for future incident response.

The Thinking Process

The assistant's reasoning, visible across the preceding messages, follows a clear arc:

  1. Observation ([msg 1408]): A user reports Curio failing to start. The error is specific—connection refused on port 1234.
  2. Hypothesis generation (<msg id=1409-1410>): The assistant checks if portavaild is running and forwarding 1234. It is. It checks if Lotus is listening on 1234. It is. The problem must be on the client side.
  3. Root cause identification (<msg id=1411-1413>): The assistant reads the entrypoint script and immediately spots the missing -L 1234 in the portavailc invocation. The fix is a one-line edit.
  4. Attempted remediation (<msg id=1417-1423>): The assistant tries to fix instances in-place via SSH. One succeeds, the rest fail due to SSH key configuration.
  5. Escalation (<msg id=1424-1427>): The assistant pivots to the nuclear option—rebuild the image, kill everything, redeploy. This is the message we're analyzing. The decision to kill all 11 instances was not made lightly. It came after exhausting less destructive options and recognizing that the SSH key barrier made in-place fixes impossible for the majority of instances. The assistant's thinking shows a pragmatic trade-off: the cost of re-downloading proving parameters (~100+ GB per instance, taking 20-45 minutes) was acceptable compared to the uncertainty of having instances stuck in a crash loop indefinitely.

Conclusion

Message [msg 1427] is a study in operational decision-making under constraints. It demonstrates that sometimes the most efficient fix is the most destructive one—tearing down a deployment to rebuild it correctly rather than patching each instance individually. The assistant correctly identified that the SSH key barrier made in-place fixes infeasible for most instances, and that the fastest path to a working system was to terminate everything and start fresh with the corrected Docker image.

The message also reveals the importance of infrastructure observability. The assistant could make this decision confidently because it had built the vast-manager system with kill capabilities, instance state tracking, and metadata persistence. Without those systems, the kill command would have been reckless—potentially losing track of which instances existed and what state they were in. Instead, it was a controlled demolition, executed with full knowledge of the consequences.

In the end, 11 instances were killed, a Docker image was rebuilt and pushed, and the deployment pipeline would create new instances that would start Curio successfully. The fix was complete, and the system was restored to a working state—not through careful surgery, but through a clean, decisive reset.