The Phantom Instance: Debugging a Missing Worker in a Distributed Proving Fleet

Introduction

In distributed systems, the moment a new worker joins the fleet but fails to appear on the dashboard marks the boundary between theory and reality. The code is deployed, the services are running, and yet—somewhere in the cloud—a GPU instance boots up, begins its work, and remains invisible to the management layer. Message <msg id=874> captures exactly this moment: a user reports that instance 32709851 has started but is not showing up in the freshly deployed web UI dashboard. The message is a raw terminal log—an unfiltered capture of the instance's boot sequence—and it contains within it the full story of why the system's assumptions about its own infrastructure were wrong.

This article examines that single message in depth: what it reveals about the architecture of the vast-manager system, the assumptions baked into the deployment pipeline, the debugging process it triggered, and the broader lessons it offers about building reliable distributed proving infrastructure.

The Context: A Fresh Dashboard, A Missing Worker

To understand <msg id=874>, we must first understand what came before it. In the preceding messages (<msg id=873>), the assistant had just completed a major deployment: a comprehensive web UI dashboard for the vast-manager system, a service designed to orchestrate a fleet of GPU workers on the Vast.ai marketplace running Filecoin proof generation (via the Curio/CuZK proving engine). The dashboard was feature-rich—instance tables with GPU utilization, pricing per proof, SSH commands, log viewers, kill buttons, and auto-refresh. It had been tested against a single running instance (32705217) and everything worked perfectly.

The user then started a second instance (32709851) through Vast.ai. This message is the user reporting that the new instance is not appearing in the dashboard. The user pastes the full terminal output from the instance's boot sequence, captured presumably by watching the instance's logs through Vast.ai's console interface. This is not a structured error report or a bug description—it is raw evidence, presented without commentary, for the assistant to diagnose.

The Boot Log: Reading the Evidence

The user's message begins with a timestamp: "Instance 32709851 started but not in ui while already fetching params, Thu Mar 12 00:14:51 UTC 2026." Then comes the raw log. Let us walk through what this log reveals, line by line.

The first block shows apt-get update hitting Ubuntu repositories—the standard NVIDIA CUDA repository for Ubuntu 24.04, the standard Ubuntu security and updates repos. This is the instance's base image doing its startup routine. The warning about the deprecated trusted.gpg keyring is cosmetic and ubiquitous. Then we see package installation: openssh-server, tmux, wget, curl, less, locales, sudo, software-properties-common, git, rsync—all already installed. This is the Docker image's initialization script ensuring baseline tools are present.

Then a critical line: rm: cannot remove '/etc/forward_port2': No such file or directory. This is a cleanup command from the entrypoint script attempting to remove a file that doesn't exist. It is harmless but revealing—it tells us the entrypoint script is from an older version of the image.

Next: Starting portavailc tunnel to 45.33.141.226:22222 ... followed by portavailc started (PID=335). This is the port forwarding tunnel that connects the instance back to the controller host. The tunnel is listening on three ports: 9042 (for the proving engine's remote GPU communication), 5433 (for database connectivity), and critically, 1234—not 1235. The manager's API server runs on port 1235. The instance is tunneling to the wrong port.

Then: Proving parameters not found in /var/tmp/filecoin-proof-parameters — fetching 32GiB params... followed by the paramfetch process downloading verification keys and SRS files from a Cloudflare R2 bucket. The instance is in the "fetching" state—it has not yet begun proving.

The final lines show the paramfetch process making HTTP GET requests to download the cryptographic parameters needed for proof generation: the vk files for proof-of-spacetime, stacked-proof-of-replication, empty-sector-update, and the massive v28-fil-inner-product-v1.srs file (the Structured Reference String, a 44GB+ parameter file required for the CuZK proving system).

What the Log Reveals About the System Architecture

This single log dump is a window into the entire proving infrastructure. The system consists of:

  1. Vast.ai marketplace: Provides GPU instances on demand. Each instance is a Docker container running a custom image (theuser/curio-cuzk:latest).
  2. The controller host (10.1.2.104): Runs the vast-manager service, which maintains a SQLite database of registered instances, monitors their status via the Vast API, and provides the web UI dashboard.
  3. The portavailc tunnel: A reverse tunnel that connects the instance back to the controller, allowing the instance to reach services on the controller's private network (like the manager API on port 1235, the HarmonyDB on port 5433, and the proving engine's coordination port 9042).
  4. The entrypoint script: The bootstrap process that runs inside each container, handling tunnel setup, registration with the manager, parameter fetching, benchmarking, and ultimately launching the proving daemons.
  5. Paramfetch: The parameter downloader that fetches the multi-gigabyte cryptographic proving parameters from a CDN before proof generation can begin. The log shows that this instance is running an older version of the entrypoint script—one that tunnels port 1234 instead of 1235, and crucially, one that does not include the log-shipping and registration logic that was just added in the previous round of development.

Assumptions Exposed

The message exposes several assumptions that were baked into the system design:

Assumption 1: All instances run the latest image. The dashboard and manager were designed assuming that any instance started would be running the most recent Docker image with the updated entrypoint that includes log shipping and automatic registration. But Vast.ai instances can be started with older images that are cached on the host machine, or the user may have started the instance before the new image was pushed.

Assumption 2: The manager can discover instances via the Vast API. The monitor component of vast-manager periodically queries the Vast API for running instances and cross-references them against the database. But this requires instances to have a label matching the C.<id> pattern. The new instance had no label (label='(none)'), so the monitor would skip it.

Assumption 3: Port 1235 is the correct tunnel target. The manager API was moved from port 1234 to 1235 earlier in the development cycle because port 1234 was already in use by Lotus (the Filecoin node). The old entrypoint still tunnels port 1234. An instance running the old image cannot reach the manager at all.

Assumption 4: VAST_CONTAINERLABEL is available as an environment variable. The entrypoint script was designed to use $VAST_CONTAINERLABEL to determine the instance's Vast.ai ID for registration. The user initially described this as a label on the VM, but later clarified it is an environment variable injected by the Vast.ai platform. However, when the assistant SSH'd into the instance to verify, the variable was entirely absent from the environment—a deeper platform-level issue that remained unresolved.

The Thinking Process Visible in the Message

The message itself does not contain reasoning—it is raw data. But the act of presenting this data is itself a reasoning step. The user could have simply said "instance not showing up." Instead, they provided the full boot log. This choice reveals an understanding of what information would be useful for debugging: the entrypoint's output, the tunnel configuration, the paramfetch state, and the timing of events.

The log is timestamped at 00:14:51 UTC. The instance started, ran its boot sequence, and began fetching parameters. The user noticed it wasn't in the UI and captured the log while the instance was still in its early startup phase. This suggests the user was actively monitoring the dashboard, saw the new instance was missing, and immediately gathered forensic evidence.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

Output Knowledge Created

This message, combined with the assistant's subsequent investigation (<msg id=875> through <msg id=884>), produced several important pieces of knowledge:

  1. The old entrypoint does not register with the manager. Instances running the old image will never appear in the dashboard unless manually registered.
  2. Port mismatch is a blocking issue. Even if the instance could reach the manager, it would be tunneling to port 1234, not 1235. The manager API is unreachable.
  3. VAST_CONTAINERLABEL is not reliably injected. Despite the user's expectation that Vast.ai provides this variable, it was absent from the container's environment. This discovery required SSH access to the running instance and direct inspection of environment variables.
  4. Manual workaround is possible. The assistant demonstrated that an instance can be labeled via the Vast CLI (vastai label instance 32709851 "C.32709851") and then manually registered with the manager via a POST to /register. This provided a temporary fix while the root cause was investigated.
  5. The image needs to be rebuilt and pushed. The long-term fix requires rebuilding the Docker image with the updated entrypoint and ensuring new instances are started with the latest image.

Mistakes and Incorrect Assumptions

The most significant incorrect assumption in this message is the implicit belief that the system would work seamlessly for new instances. The dashboard had been tested against a single instance that was already running and had been manually registered. The auto-discovery path—where new instances automatically register and appear—had not been tested end-to-end.

The user's assumption that VAST_CONTAINERLABEL would be present was also incorrect, though understandably so. The Vast.ai documentation suggests this variable is injected, but in practice it was absent. This could be due to the specific image configuration, the way the instance was started, or a platform-level issue that required further investigation.

The assistant's assumption that the monitor would discover the instance via the Vast API was partially correct—the monitor can see running instances—but the instance had no label, so it was skipped. The monitor's logic explicitly filters out instances with empty labels to avoid interfering with non-managed workers.

The Broader Lesson: Observability Gaps in Distributed Systems

This message illustrates a fundamental challenge in distributed systems: the gap between "the system is deployed" and "the system is working." The dashboard was functional, the APIs responded, the data was enriched—but only for instances that had already been through the registration flow. A new instance, started fresh from the image, fell into a blind spot.

The blind spot existed because the system had two independent discovery mechanisms: the Vast API (which sees all instances but requires labels) and the registration API (which requires the instance to actively register). Neither mechanism worked for this instance: the Vast API saw it but skipped it due to the missing label, and the registration API was unreachable due to the port mismatch.

This is a classic "two-body problem" in system design: when two subsystems must coordinate (the manager discovering instances, and instances registering themselves), failures in either path leave the system in an inconsistent state. The instance exists in the cloud, it is running, it is consuming resources and generating cost—but it is invisible to the management layer.

Conclusion

Message <msg id=874> is, on its surface, a simple report: "Instance started but not in UI." But the raw boot log it contains is a rich artifact that reveals the architecture, assumptions, and failure modes of a complex distributed proving system. It shows how a freshly deployed dashboard, tested against a single instance, immediately encountered edge cases when a second instance was added. It exposed mismatches between old and new entrypoint versions, between expected and actual environment variables, and between the manager's discovery mechanisms and the reality of Vast.ai's platform behavior.

The debugging that followed this message—SSHing into the instance, inspecting environment variables, manually labeling and registering—was a necessary but temporary fix. The deeper work of rebuilding the Docker image, ensuring the entrypoint handles missing VAST_CONTAINERLABEL gracefully, and adding fallback registration paths would follow. But this message marks the moment when the system's assumptions met reality, and the debugging process began.