The Missing Label: Diagnosing a Silent Instance in a Distributed Proving Fleet

Introduction

In distributed systems, the gap between "an instance is running" and "the system knows about the instance" can be surprisingly wide. This article examines a single diagnostic message from an opencode coding session — message index 876 — where an AI assistant investigates why a newly launched GPU proving instance (ID 32709851) on the Vast.ai marketplace has started its work but remains invisible to the fleet management dashboard. The message is a deceptively simple bash command piped through a Python parser, but it represents a critical moment of discovery: the moment when an assumption about platform behavior collides with reality.

Context: The Vast Manager System

To understand this message, one must first understand the architecture it serves. The assistant has been building a comprehensive fleet management system called "vast-manager" for a Filecoin proving operation. The system consists of a Go backend running on a controller host (10.1.2.104) that tracks GPU instances rented from Vast.ai — a marketplace for cloud GPU compute. Each instance runs a Docker container (theuser/curio-cuzk:latest) that performs cryptographic proofs for the Filecoin network, and the manager tracks their state through a lifecycle: registration → parameter fetching → benchmarking → running → (potentially) killed.

The registration mechanism relies on a clever bootstrap: instances are expected to have a VAST_CONTAINERLABEL environment variable automatically injected by the Vast.ai platform. The instance's entrypoint script uses this label to self-register with the manager by POSTing to the manager's API. If the label is missing, the instance never announces itself, and the manager never learns of its existence — a silent ghost in the fleet.

The Message Itself

The subject message ([msg 876]) contains a single bash command executed by the assistant:

ssh 10.1.2.104 'export PATH="$HOME/.local/bin:$PATH" && vastai show instances --raw' 2>&1 | python3 -c "
import json, sys
data = json.load(sys.stdin)
for i in data:
    label = i.get('label') or '(none)'
    extra = dict(i.get('extra_env', []))
    vcl = extra.get('VAST_CONTAINERLABEL', '(not set)')
    pavail = 'yes' if 'PAVAIL' in extra else 'no'
    print(f'ID={i[\"id\"]}  label={label!r}  host_id={i[\"host_id\"]}  machine_id={i[\"machine_id\"]}  gpu={i[\"num_gpus\"]}x {i[\"gpu_name\"]}  status={i[\"actual_status\"]}  VAST_CONTAINERLABEL={vcl}  PAVAIL={pavail}')
    if i['id'] == 32709851:
        print(f'  public_ip={i.get(\"public_ipaddr\")}')
        ports = i.get('ports', {})
        ssh_port = ports.get('22/tcp', [{}])[0].get('HostPort', '?') if '22/tcp' in ports else '?'
        print(f'  ssh: ssh -p {ssh_port} root@{i.get(\"public_ipaddr\",\"?\")}')
        print(f'  start_date={i.get(\"start_date\")}  onstart={i.get(\"onstart\")}')
        print(f'  image={i.get(\"image_uuid\")}')
        print(f'  extra_env={i.get(\"extra_env\")}')
"

The output reveals two instances running on the Vast platform:

ID=32705217  label='C.32705217'  ...  VAST_CONTAINERLABEL=(not set)  PAVAIL=yes
ID=32709851  label='(none)'  ...  VAST_CONTAINERLABEL=(not set)  PAVAIL=yes
  public_ip=72.49.200.123
  ssh: ssh -p 48690 root@72.49.200.123
  start_date=1773274451.134675  onstart=/usr/local/bin/entrypoint.sh
  image=theuser/curio-cuzk:latest
  extra_env=[['PAVAIL', 'portavail1:...']]

Why This Message Was Written

The message was written in direct response to the user's report in [msg 874]: "Instance 32709851 started but not in ui while already fetching params." The user had attached logs showing the instance booting up, connecting to the portavailc tunnel, and beginning to download ~100GB of Filecoin proving parameters — a process that takes significant time. Yet the dashboard showed only one instance (32705217), and the new instance was invisible.

The assistant's immediate reaction in [msg 875] was to form a preliminary hypothesis: "The instance is running the old entrypoint (tunneling port 1234, no port 1235), and it doesn't have VAST_CONTAINERLABEL set." But this was speculation based on observing that the instance was tunneling port 1234 (the old API port) rather than port 1235 (the new manager port). The assistant needed hard data to confirm or refute this hypothesis, which is precisely what message 876 provides.

The reasoning is methodical: rather than SSHing into the instance directly (which would only show one perspective), the assistant queries the Vast.ai API itself via vastai show instances --raw. This gives the platform's authoritative view of the instance's configuration, including its environment variables, image version, and startup command. By comparing this data against what the manager expects, the assistant can pinpoint exactly where the registration chain breaks.

How Decisions Were Made

Several design decisions are visible in how this diagnostic command is constructed:

Choosing the API over direct inspection. The assistant could have SSHed into the instance and checked environment variables directly. Instead, it queried the Vast API. This choice reveals an understanding that the registration problem might stem from the platform layer itself — perhaps the image was wrong, the environment variables weren't being injected, or the onstart command was different. The API provides a holistic view that direct SSH cannot match.

Structuring the Python parser for comparison. The script prints fields for all instances, not just the problematic one. This allows the assistant to compare the working instance (32705217) against the non-working one (32709851) side by side. Both instances show VAST_CONTAINERLABEL=(not set), which is a critical observation: even the working instance appears to lack the label in the API response. This suggests either the label is injected through a different mechanism (perhaps after container start), or the working instance registered through a different path (manual registration).

The conditional deep-dive for the target instance. The if i['id'] == 32709851: block prints additional fields — public IP, SSH port, start date, onstart command, image UUID, and full extra_env. This reveals that the assistant is preparing for the next step: if the API data confirms the hypothesis, the next action would likely be to SSH into the instance and attempt manual registration or debugging.

Assumptions Made

The message reveals several assumptions, some of which prove incorrect:

Assumption 1: VAST_CONTAINERLABEL is automatically injected by the platform. The entire registration architecture depends on this assumption. The entrypoint script reads $VAST_CONTAINERLABEL to construct the instance label and register with the manager. The output shows VAST_CONTAINERLABEL=(not set) for both instances, which is the first concrete evidence that this assumption may be flawed. The assistant had previously believed this was a platform-provided variable, but the data suggests otherwise — or at least that it isn't visible through the extra_env mechanism.

Assumption 2: The instance is running the "old" entrypoint. The user's logs showed port 1234 being tunneled, which was the old API port before the manager was moved to port 1235. However, the API data reveals that the instance is running image=theuser/curio-cuzk:latest — the most recent image. This contradicts the "old entrypoint" hypothesis. The latest image should contain the updated entrypoint that tunnels port 1235 and ships logs. The real issue may be that the entrypoint was updated after this image was built, or that the image wasn't rebuilt after the entrypoint changes.

Assumption 3: The manager's database is the authoritative source of truth. The assistant is implicitly validating the manager's view against the platform's view. The manager's dashboard showed only one instance; the platform shows two. This confirms that the registration mechanism is indeed broken, and the gap is at the instance-to-manager communication layer, not at the platform level.

Mistakes and Incorrect Assumptions

The most significant mistake revealed by this message is the assumption about VAST_CONTAINERLABEL. The assistant had designed the registration system around a platform feature that either doesn't exist, isn't enabled for this account, or isn't exposed through the extra_env mechanism. The working instance (32705217) had been registered manually earlier in the session (<msg id=862-871>), which is why it appears in the dashboard despite also lacking the label. The automatic registration path was never actually tested end-to-end.

A secondary issue is the assumption that the latest image tag would automatically include the most recent entrypoint changes. The Docker build and push process (described in segment 5 of the session) had been completed, but the image on Vast may have been pulled before the entrypoint was updated with log shipping and the new port. The onstart=/usr/local/bin/entrypoint.sh value confirms the entrypoint is being executed, but its contents may be stale.

Input Knowledge Required

To fully understand this message, one needs:

Knowledge of the Vast.ai platform. Vast.ai is a peer-to-peer GPU rental marketplace. Instances are Docker containers launched with configurable environment variables, images, and startup commands. The vastai show instances --raw command returns the full JSON representation of all rented instances, including their configuration, status, and metadata.

Knowledge of the manager's registration protocol. The manager expects instances to POST to its API with a label derived from VAST_CONTAINERLABEL. Without this label, the instance cannot register. The manager also expects log shipping from three sources (setup, cuzk, curio) via the /api/log-push endpoint.

Knowledge of the portavailc tunnel system. The instances connect to a central relay (portavailc) that tunnels ports from the controller host to the instances. The manager API was originally on port 1234, then moved to port 1235 to avoid conflict with a Lotus node. The instance was tunneling port 1234, suggesting it was built before the port change.

Knowledge of the Docker image pipeline. The image theuser/curio-cuzk:latest is a multi-stage Docker build containing CUDA tooling, the CuZK proving engine, and the Curio Filecoin node. The entrypoint script orchestrates parameter fetching, benchmarking, and the proving daemon.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

Confirmed: VAST_CONTAINERLABEL is absent from both instances. The most critical finding is that neither instance has the label set in extra_env. This definitively rules out a per-instance configuration error and points to either a platform-level issue or a misunderstanding of how the label is supposed to be provided.

Confirmed: The instance is running the latest image. The image UUID confirms theuser/curio-cuzk:latest, which means the entrypoint should be the most recent version. However, "latest" is a floating tag — it depends on when the image was pulled by the Vast platform, which may predate the entrypoint changes.

Confirmed: The instance has 2x RTX 3090 GPUs. This is a different GPU configuration from the existing instance (1x RTX 4090). The manager's fleet summary will need to handle heterogeneous GPU types for accurate pricing and performance metrics.

New SSH access path. The assistant now has the SSH command (ssh -p 48690 root@72.49.200.123) and can directly access the instance for further debugging. This is the next logical step after the API query.

The registration gap is real and reproducible. With two instances, one registered and one not, the assistant now has a controlled experiment: the working instance was registered manually, the non-working one was not. This strongly suggests the automatic registration path is broken and needs either a fix to the entrypoint or a different registration strategy.

The Thinking Process

The assistant's thinking, visible in the structure of the command and the choice of fields to display, follows a clear diagnostic pattern:

  1. Establish the baseline. Print fields for all instances to see the full picture. The working instance (32705217) serves as a control — it is registered and running, so its configuration should show what a "successful" setup looks like.
  2. Focus on the registration mechanism. The VAST_CONTAINERLABEL field is the key variable. By extracting it from extra_env and printing it prominently, the assistant is testing the core hypothesis: does the label exist?
  3. Gather forensic evidence. For the target instance, the assistant collects everything needed for the next step: SSH access (public IP + port), image version (to check if it's stale), start date (to understand timing), and the full extra_env (to see all environment variables, not just the filtered ones).
  4. Prepare for intervention. The SSH command is printed so it can be copied and used directly. The assistant is setting up for the next action: SSH into the instance, verify the entrypoint contents, check environment variables directly, and potentially register the instance manually. The Python script also reveals an attention to edge cases: it handles missing labels with &#39;(none)&#39;, missing environment variables with &#39;(not set)&#39;, and missing ports with &#39;?&#39;. These fallbacks ensure the diagnostic output is complete even when data is missing — which turns out to be essential, since the missing label is precisely the finding.

Conclusion

Message 876 is a textbook example of remote diagnostic work in distributed systems. Faced with a silent failure — an instance that runs but doesn't appear in the management dashboard — the assistant systematically gathers data from the platform API, compares working and non-working instances, and isolates the root cause to a missing environment variable. The message is notable for what it reveals about assumptions in system design: the elegant automatic registration mechanism, built around a platform feature that was believed to be automatic, turns out to depend on a variable that the platform does not, in fact, inject. The diagnostic command is both a confirmation of the problem and a roadmap for the fix — whether that fix involves updating the entrypoint to handle the missing label, changing the registration strategy to use a different identifier, or manually registering instances as a stopgap. In the broader narrative of the coding session, this message marks the moment when theory meets reality, and the architecture must adapt to the platform as it actually is, not as it was assumed to be.