The Bridge Between Daemon and Dashboard: Building Live Monitoring for a GPU Proving Pipeline

Introduction

In the lifecycle of a complex distributed system, there is a moment when infrastructure becomes visible — when raw telemetry, carefully collected and structured inside a running daemon, finally crosses the boundary into an operator's field of view. This chunk of the opencode session captures exactly that transition. The assistant had just completed a lightweight HTTP status API for the cuzk GPU proving engine, committed as 120254b3 across 717 lines of new and modified Rust code in 10 files ([msg 2555]). But a status API that nobody can see is a status API that might as well not exist. The cuzk daemon's HTTP server listens on port 9821, but these are remote GPU instances on a vast.ai marketplace — their ports are not exposed to the internet. The only access path is SSH, which the vast-manager already uses to manage instances.

The user's instruction in [msg 2553] was precise: "commit, then extend vast-manager (has access to the ssh addrs already) ui to, when a running node is selected, show a rich timeline visualization (live, no history because we don't poll the data in background)." This kicked off a tightly sequenced implementation spanning roughly 30 messages, from codebase exploration through backend endpoint construction to frontend visualization. The result is a live monitoring bridge that tunnels GPU proof pipeline state through SSH ControlMaster and renders it as a rich dashboard — a memory gauge, per-job partition pipeline grids with color-coded phases, GPU worker state cards, SRS/PCE allocation tables, and aggregate counters — all polled every 1.5 seconds with clean lifecycle management via AbortController.

This article traces that implementation from end to end, examining the architectural decisions, the exploration process, the incremental build discipline, and the design of the visualization itself.

Phase 1: Committing the Foundation

The chunk opens with the assistant confirming the commit of the status API (<msg id=2554-2555>). The commit message tells the story: "cuzk: add lightweight HTTP status API for live monitoring. Adds a StatusTracker that records pipeline, GPU worker, and memory state as proof jobs flow through the engine. A minimal raw-TCP HTTP/1.1 server on a configurable port (daemon.status_listen) serves GET /status returning JSON snapshots at 500ms polling granularity."

This was not a trivial addition. The commit spanned 10 files and included a new module (status.rs), modifications to the engine's lifecycle to wire in status tracking at every pipeline transition point, configuration for the listen port, and the HTTP server itself — all implemented as a raw TCP listener with manual HTTP response formatting to avoid adding dependencies to the daemon. The assistant had tested this end-to-end in the preceding messages, running a full PoRep proof through the daemon and verifying that every partition transitioned through the expected phases: synthesizing → synth_done → gpu → done, with completed jobs garbage-collected after 30 seconds.

The commit created a clean boundary. The status API was now a committed, production-ready feature. The next phase — integrating it into the operator dashboard — could proceed on solid ground.

Phase 2: Codebase Exploration

With the commit complete, the assistant turned to the vast-manager codebase. This was unfamiliar territory — a Go service with an embedded HTML dashboard, built to manage Vast.ai rental instances. The assistant's first action was to spawn a subagent task to explore the codebase thoroughly ([msg 2557]). The subagent returned a detailed analysis: the vast-manager was a single-file Go service (main.go, 1804 lines) serving a single-file HTML/CSS/JS dashboard (ui.html, 1354 lines). It used SQLite for state, had SSH access to all provisioned instances, and already had an instance expansion mechanism that fetched logs when a running node was clicked.

The assistant then performed a series of targeted reads to understand the specific integration points. It read the route registration pattern in main.go ([msg 2559]), discovering that all handlers were defined above a setupRoutes() method and registered with mux.HandleFunc(). It read the handleInstanceLogs function ([msg 2574]) to understand how UUIDs were extracted from URL paths — a pattern it would replicate for the new cuzk status endpoint. It read the UI's toggleExpand function (<msg id=2563-2564>) to understand how instance expansion worked and how logs were fetched and displayed. It studied the CSS variable system ([msg 2562]) to understand the dark theme conventions (--bg:#0d1117, --bg2:#161b22, etc.) that the new visualization would need to match.

This exploration phase was methodical and thorough. The assistant was not guessing — it was reading the actual code, understanding the patterns, and building a mental model of how the new feature would integrate. The todo list in [msg 2568] shows the structured approach: read main.go, read ui.html, add Go endpoint, add UI visualization. Each item was checked off as completed.

Phase 3: The SSH ControlMaster Decision

The most critical architectural decision of this chunk emerged during the assistant's reasoning in [msg 2558]. The cuzk daemon's status API listens on port 9821, but these ports are not exposed to the internet. The vast-manager has SSH access to each instance, but establishing a fresh SSH connection every polling cycle would be prohibitively expensive — SSH handshakes involve key exchange, authentication, and channel establishment, easily adding a second or more of latency per request.

The assistant's reasoning shows a moment of genuine reconsideration: "but we actually don't need SSH for this! The status API is on a TCP port. But wait — the user said 'SSH-based poller (ports aren't exposed)'. So we need to tunnel through SSH to reach port 9821." This self-correction is crucial. The assistant had momentarily forgotten a key constraint and caught itself before proceeding down the wrong path.

The solution was SSH ControlMaster — a feature of OpenSSH that allows multiple SSH sessions to share a single TCP connection via a Unix domain socket. The first ssh invocation creates the control socket; subsequent invocations within the timeout window piggyback on the same connection, bypassing the expensive handshake entirely. The assistant articulated this clearly: "The cleanest approach is to use SSH ControlMaster through the shell — running ssh with the right flags to reuse connections via a Unix socket. The first call sets up the connection, and subsequent calls within the timeout window reuse it automatically, which avoids the expensive handshake overhead while keeping the implementation simple."

This decision reflects a deep understanding of the operational environment. The vast-manager is a Go binary deployed on a control machine; it already runs shell commands for SSH management. Adding an SSH library would increase build complexity, binary size, and maintenance burden. By contrast, shelling out to ssh -o ControlMaster=auto -o ControlPath=... is zero-dependency, leverages an already-proven tool, and integrates naturally with the existing codebase.

Phase 4: Building the Go Endpoint

With the architecture decided, the assistant implemented the backend endpoint in two surgical edits to main.go. The first edit ([msg 2575]) added the handleCuzkStatus handler function — placed "right before setupRoutes" following the existing code organization pattern. The handler extracts the UUID from the URL path, looks up the instance's SSH details from the in-memory DashboardInstance map, constructs an SSH command with ControlMaster flags, executes it, captures the stdout (the JSON status response), and returns it to the frontend.

The second edit ([msg 2576]) registered the route: mux.HandleFunc(&#34;/api/cuzk-status/&#34;, s.handleCuzkStatus). This single line transformed a private handler function into a public API endpoint. The assistant's phrasing — "Now register the route" — reflects the deliberate sequencing: first define the behavior, then expose it.

The assistant then ran a build verification ([msg 2577]): cd /tmp/czk/cmd/vast-manager &amp;&amp; go build -o /dev/null . 2&gt;&amp;1. The build succeeded with only pre-existing warnings from the vendored mattn/go-sqlite3 C binding. The assistant explicitly noted this: "The warnings are from sqlite3, not our code." This is a discipline of incremental validation — never build on top of broken foundations. The Go code was verified before any frontend work began.

Phase 5: Building the Visualization

The frontend implementation was the most substantial piece of work in this chunk. The assistant approached it in four distinct edits to ui.html, each adding a layer of the visualization:

Edit 1 — CSS ([msg 2579]): The assistant added styles for the cuzk status panel, integrating with the existing dark theme. The CSS defined the visual vocabulary: a memory gauge bar, color-coded phase blocks for the partition pipeline grid (synthesizing, waiting for GPU, on GPU, done, failed), GPU worker state cards, and allocation tables for SRS and PCE structures.

Edit 2 — HTML container ([msg 2580]): The assistant added a structural placeholder inside the expanded instance detail row — a &lt;div&gt; that would be populated by the JavaScript rendering logic. Without this container, the polling and rendering code would have no DOM node to target.

Edit 3 — Lifecycle wiring ([msg 2581]): The assistant modified the toggleExpand function to start and stop cuzk polling when an instance is expanded or collapsed. This is the behavioral bridge: polling only happens for the one instance whose detail panel is currently visible. When the operator collapses the panel, polling stops immediately. The assistant used an AbortController to manage this lifecycle — a modern browser API that allows clean cancellation of in-flight fetch requests, preventing stale data from being rendered after collapse.

Edit 4 — Polling and rendering logic ([msg 2582]): The assistant added the fetchCuzkStatus and renderCuzkStatus functions. The fetch function calls the Go backend endpoint at 1.5-second intervals, handling errors gracefully. The render function transforms the JSON response into the full visualization: a memory usage gauge at the top, a per-job partition pipeline grid showing each partition's phase as a color-coded block, GPU worker state cards indicating busy/idle status, SRS/PCE allocation tables for diagnosing memory pressure, and aggregate counters for completed, failed, and active jobs.

The polling interval of 1.5 seconds was a deliberate tradeoff. The cuzk status API updates at 500ms granularity internally, but polling at that rate over SSH would generate excessive traffic. At 1.5 seconds, the operator sees near-real-time updates while keeping the SSH overhead manageable. This is especially important because each poll involves an SSH exec of curl, which has non-trivial latency even with ControlMaster connection reuse.

The Visualization Design

The assistant's design for the visualization, reasoned through in [msg 2558], was driven by operational use cases. The memory gauge addresses the most common failure mode (OOM crashes). The partition pipeline grid answers the second most common question ("why is my proof taking so long?" — is it stuck in synthesis, waiting for GPU, or actually on the GPU?). The GPU worker cards answer "are my GPUs busy or idle?" The counters answer "how many proofs have completed or failed?"

The assistant explicitly acknowledged that this was live state, not historical data. The user had said "no history because we don't poll the data in background." The visualization is purely a snapshot of the current moment, refreshed periodically, with no attempt to build a time-series or show trends. This constraint simplified the implementation considerably — no need for a database, no need for time-series storage, no need for aggregation. The frontend simply fetches the current JSON snapshot and renders it.

The color coding of partition phases is particularly important for rapid visual diagnosis. An operator glancing at the dashboard can immediately see if a partition is stuck in "synthesizing" (CPU-bound bottleneck), waiting for GPU (GPU contention), or on GPU (actively proving). A row of green "done" blocks across all partitions signals a healthy pipeline. Red "failed" blocks demand investigation. This transforms the proving engine from a black box into a transparent, observable process.

The Engineering Discipline

Throughout this chunk, the assistant demonstrated a consistent engineering discipline that is worth examining in its own right. The pattern is visible in every phase:

Explore before acting. The assistant did not start writing code until it had read the relevant sections of both main.go and ui.html. It understood the routing conventions, the SSH access model, the UI rendering lifecycle, and the data schema of the cuzk status API before making a single edit.

Validate after every change. The Go build verification in [msg 2577] is a textbook example. The assistant did not assume the edits were correct — it ran the compiler immediately and checked the output. The separation of concerns was deliberate: verify the backend before building the frontend.

Sequence work by dependency. The four edits to ui.html followed a strict dependency order: CSS first (foundation), then HTML structure (scaffolding), then lifecycle wiring (behavior), then rendering logic (execution). Each edit built on the previous one without backtracking.

Document the process. The todo list updates in [msg 2568] and [msg 2578] serve as a persistent scratchpad, tracking what has been completed and what remains. The assistant's reasoning in [msg 2558] is a detailed architectural document that explains not just what was built, but why it was built that way.

Assumptions and Risks

The implementation rests on several assumptions that deserve examination. The SSH ControlMaster approach assumes that curl is available on the remote instances and that the SSH client configuration supports ControlMaster auto and ControlPersist. If a remote instance lacks curl, the polling will fail silently. If the ControlMaster timeout is too short, every poll would re-establish a connection, defeating the optimization.

The assistant assumes that the cuzk daemon's status endpoint will always be responsive and return the expected JSON schema. The visualization has no graceful degradation if the endpoint returns an error — the assistant's error handling in the fetch function would need to handle cases where the daemon is not running, the port is wrong, or the SSH connection fails.

The polling interval of 1.5 seconds assumes that this is fast enough for operators to track pipeline progress. In the test runs from the preceding messages, GPU times were as short as 3.8 seconds and 6.9 seconds per partition. At 1.5-second polling, a partition that spends only 3-4 seconds on GPU might be captured in the "on GPU" state for only 2-3 polls. This is sufficient for trend detection but not for precise timing measurements.

Conclusion

This chunk of the opencode session represents a complete, self-contained feature delivery: from a committed backend API through codebase exploration, architectural design, backend implementation, build verification, and frontend visualization. The result is a live monitoring bridge that connects a GPU proving daemon running on a remote, firewalled instance to an operator's management dashboard, using SSH ControlMaster for efficient polling and a rich HTML/CSS/JS visualization for real-time pipeline insight.

The assistant's methodical approach — explore before acting, validate after every change, sequence work by dependency, document the process — is a model of disciplined software engineering. The SSH ControlMaster decision, the AbortController lifecycle management, the color-coded partition pipeline grid, and the separation of fetch and render concerns all reflect careful architectural thinking.

But perhaps the most important outcome is invisible in any single message. The proving engine, which had been a black box running on remote GPUs, was now transparent. Operators could watch proofs flow through the pipeline in real time, spot bottlenecks, detect memory pressure, and verify that all GPU workers were active. The bridge between daemon and dashboard was complete, and the system was finally observable.