From Container to Fleet: The Complete Engineering Journey of a CUDA Proving Stack

Introduction

In the span of a single opencode coding session, the assistant and user accomplished what would typically require a small team of engineers working for weeks: they built a production-grade Docker container for a GPU-accelerated zero-knowledge proving stack, pushed it to Docker Hub, created benchmarking and deployment scripts, integrated a port tunneling system for distributed communication, patched a spurious error in the proving daemon, and designed a comprehensive self-managing fleet orchestration system for vast.ai GPU instances. This article synthesizes the entire arc of that work, tracing the path from a broken Docker build to a fully specified distributed system architecture.

The chunk covered by this analysis spans messages 589 through 771, encompassing dozens of tool calls, multiple subagent research sessions, and a remarkable breadth of engineering work. At its heart is a single Docker image — theuser/curio-cuzk:latest — that bundles the Curio Filecoin proving node, the CuZK GPU proving engine, the sptool utility, and the cuzk-bench benchmarking tool, all compiled against CUDA 13.0.2 on Ubuntu 24.04. But the image is merely the foundation; the real story is in the operational infrastructure built around it.

Part I: The Docker Build — Overcoming Every Blocker

The journey to a working Docker image was a catalog of everything that can go wrong in a multi-stage CUDA Docker build. Each blocker required a different diagnostic approach, and each fix taught something about the system's architecture.

The Pip Conflict That Nearly Derailed Everything

The first major blocker emerged from a seemingly innocuous interaction between Python packaging systems. The supraseal build script — part of the SPDK (Storage Performance Development Kit) that provides high-performance storage operations for the proving pipeline — creates a Python virtual environment and runs pip install --upgrade pip inside it. On Ubuntu 24.04, the Debian-managed python3-pip package installs pip as a system-level package. When the venv's pip attempts to upgrade itself, it detects the system pip package in sys.path and tries to uninstall it — but the uninstallation fails because the Debian package manager owns the RECORD file, not pip. The result is a cryptic error: ERROR: Cannot uninstall pip 24.0, RECORD file not found. Hint: The package was installed by debian.

The assistant's diagnostic process for this issue was methodical. It traced the error through the build chain — build.sh → pkgdep.sh → pip install --upgrade pip — and identified the root cause as the conflict between the Debian-managed pip and the venv's pip. The fix was elegantly simple: remove python3-pip from the Dockerfile's apt-get install list entirely. The python3-venv package bootstraps pip via ensurepip, which uses the wheel file from python3-pip-whl (a dependency of python3-venv). This gives the venv its own pip without any system-level pip package to conflict with. The assistant verified this approach by checking package dependencies in a clean container, confirming that python3-venv does not depend on python3-pip and that removing python3-pip would not break venv creation ([msg 597], [msg 600]).

The Phantom Library: Tracing libcudart_static.a

The second blocker appeared during the Go link step. The Go linker could not find libcudart_static.a, producing the error cannot find -lcudart_static. This static library is required for CUDA runtime support in the Go binaries (curio and sptool are Go programs with CUDA bindings via cgo). The assistant traced the file to /usr/local/cuda-13.0/targets/x86_64-linux/lib/libcudart_static.a, which is accessible via the symlink chain /usr/local/cuda → /etc/alternatives/cuda → /usr/local/cuda-13.0 and thus available at /usr/local/cuda/lib64/libcudart_static.a ([msg 608]).

The root cause was that the Dockerfile's LIBRARY_PATH environment variable only included the CUDA stubs directory (where stub versions of CUDA libraries live for compile-time linking). The main lib64 directory was missing. The fix was to append /usr/local/cuda/lib64 to LIBRARY_PATH, giving the linker access to libcudart_static.a and any other static libraries needed during the Go build ([msg 611]).

The Missing Runtime Libraries

The third category of issues emerged during smoke testing. When the assistant ran ldd on the binaries inside the container, it found four unresolved dependencies: libconfig++.so.9, libaio.so.1t64, libfuse3.so.3, and libarchive.so.13 ([msg 616]). These are dynamically linked by the curio binary as dependencies of the supraseal/SPDK build. The runtime stage of the Dockerfile, which starts from the minimal nvidia/cuda:13.0.2-runtime-ubuntu24.04 image, did not include these libraries. The fix was to add libconfig++9v5, libaio1t64, libfuse3-3, and libarchive13t64 to the runtime stage's apt-get install command ([msg 618]).

The StorageMetaGC Bug: An Edge Case in Distributed Systems

Just as the Docker image was nearing completion, the user reported a spurious error: the StorageMetaGC task in Curio was logging ERROR-level messages every 21 minutes on snark-only clusters (instances with no local storage paths). The error read: sector_path_url_liveness update: transaction didn't commit. The assistant traced the issue to a subtle behavior in the database transaction layer: when BeginTransaction with OptionRetry() is called with no executed statements (because there are no storage paths to check), the transaction refuses to commit and returns committed=false ([msg 718]). The fix was a one-line guard clause: if there are no storage paths, return early with success before entering the transaction at all. This eliminated the spurious error entirely and was immediately built into the Docker image and pushed to Docker Hub ([msg 721]).

Part II: The Benchmark and Run Scripts

With the Docker build stable, the user requested a benchmarking script ([msg 634]). The assistant responded by spawning a subagent task to research the cuzk codebase ([msg 635]), discovering that cuzk-bench is a separate binary with a batch subcommand that can run multiple proofs and report timing. A second subagent task investigated how C1 output test data is obtained ([msg 636]), discovering a downloadable JSON file (~51 MB) from a public R2 bucket that serves as test data for PoRep proofs.

The resulting benchmark.sh script ([msg 638]) was designed with several thoughtful features:

Part III: The Portavailc Tunnel Integration

The Docker image was functionally complete for local proving, but a standalone proving node is only half the story. The user requested integration of portavailc — a Go-based port forwarding client that connects to a central daemon at 10.1.2.104:22222 and establishes tunnels for ports 1234, 5433, 9042, and 4701 ([msg 737]). This is the mechanism by which instances in the distributed fleet communicate with the controller host.

The assistant's integration was architecturally sound: it added the portavailc build in the builder stage of the multi-stage Dockerfile (where Go is already installed), then copied the compiled binary to the runtime stage with a COPY --from=builder directive (<msg id=740-741>). The entrypoint script was updated to start portavailc in the background when the $PAVAIL environment variable is set ([msg 743]), keeping the image flexible — the same image can run with or without port forwarding depending on the deployment context.

Part IV: The Vast.ai Management System Design

The capstone of the chunk is the design of a comprehensive vast.ai management system, documented in vast-cuzk-plan.md ([msg 763]). This is not a simple script or configuration — it is a complete distributed system architecture for managing a fleet of GPU proving instances.

The User's Vision

The user's specification in [msg 758] was remarkably dense, containing approximately 15 distinct requirements in a single paragraph. It described a feedback loop where a Docker container running on a vast.ai GPU instance would autonomously progress through stages: register with a management service, fetch proving parameters (with a 90-minute timeout), run a benchmark (with a 20-minute timeout), and only if the benchmark rate exceeded a configurable threshold (default 50 proofs per hour), proceed to start the cuzk daemon and curio service. The management service on the controller host would track every instance through its lifecycle, kill orphaned or underperforming instances, assign monotonically increasing runner IDs, and maintain a bad-host list to prevent re-deployment on problematic machines.

The Assistant's Architectural Response

The assistant's response was a masterclass in architectural decomposition. It identified five components:

  1. The Go-based Management Service (cmd/vast-manager/main.go): Running on the controller host (10.1.2.104), this service listens on port 1234 (forwarded by the portavailc tunnel) and uses SQLite for persistence. Its API surface is minimal: POST /register (accepts container label, returns UUID and runner ID), POST /param-done, POST /bench-done, GET /status, and POST /bad-host. The background goroutine runs every 60 seconds, executing vastai show instances --raw and applying a cascade of kill conditions: bad hosts die immediately, unregistered instances older than 15 minutes die, instances stuck in parameter fetching for more than 90 minutes die, instances stuck in benchmarking for more than 20 minutes die, and instances below the minimum rate threshold die ([msg 760]).
  2. The Expanded Entrypoint (docker/cuzk/entrypoint.sh): The entrypoint orchestrates the full lifecycle: start the portavailc tunnel, detect available RAM to set partition workers (10 if <400GB, 16 otherwise), register with the management service, fetch proving parameters, signal parameter completion, run the benchmark, report the rate, and — if the rate exceeds MIN_RATE — start the proving services. A supervisor loop monitors both PIDs and restarts them if they crash.
  3. The Monitor Script (docker/cuzk/monitor.sh): A simple utility that tails logs from both cuzk and curio with colored prefixes.
  4. Dockerfile Updates: Including monitor.sh and the new entrypoint in the image.
  5. Controller Host Setup: Building the Go binary, copying it to the controller host along with the vast.ai API key (without reading it, as the user specifically requested), installing the vast CLI, and creating a systemd unit.

Key Design Decisions

Several design decisions in this plan merit attention. The unification of runner ID and instance ID was a critical insight: the assistant initially treated them as separate entities, but after re-reading the user's answers, realized they are the same monotonically increasing counter that maps directly to the curio listen port via 127.0.0.1:$((2000 + runner_id)) ([msg 761]). The UUID-based registration scheme provides lightweight security: the management service generates a UUID at registration time, which serves as a bearer token for subsequent API calls, preventing other containers from impersonating registered instances. The RAM-based partition worker tuning (10 workers for <400GB, 16 otherwise) is a direct response to the OOM error the user encountered earlier, encoding operational experience into the deployment script.

The background monitor's cascade of kill conditions reveals a deep understanding of operational reality. The assistant doesn't assume that instances will behave correctly. Instead, it designs for failure: timeouts for every step, explicit kill actions, and a clear separation between "still working" and "stuck." The 15-minute grace period for registration acknowledges that Docker image pulls and cold starts take time. The 90-minute param fetch timeout acknowledges that downloading multi-gigabyte parameter files (44GB SRS, 25.7GB PCE) over the internet is inherently unreliable. The 20-minute benchmark timeout acknowledges that GPU proving can fail in ways that don't produce clean error messages.

The Thinking Process: From Build to Architecture

What makes this chunk remarkable is not just the breadth of work accomplished, but the quality of the thinking at every stage. The assistant's diagnostic process for each build blocker was methodical: trace the error through the build chain, identify the root cause, verify the fix, and confirm with a rebuild. The architectural design for the vast.ai management system was equally rigorous: decompose the problem into components, identify dependencies, design interfaces, anticipate failure modes, and present the plan for review before implementing.

The assistant's willingness to ask clarifying questions — about the management service language, the source of instance IDs, the semantics of the runner ID — demonstrates a crucial engineering discipline: resolve ambiguity before writing code. The user's answers shaped the entire architecture. Without understanding that the runner ID is implicitly derived from the listen port, the assistant might have designed an unnecessarily complex configuration system.

Conclusion

This chunk of the opencode session is a microcosm of the entire engineering lifecycle: from debugging a broken build, through creating operational scripts, integrating network infrastructure, fixing edge-case bugs discovered in production, and designing a complete distributed system architecture. The Docker image that emerged — theuser/curio-cuzk:latest — is not just a container; it is the foundation for a self-managing fleet of GPU proving instances that can provision themselves, benchmark their own performance, and gracefully integrate into a larger proving infrastructure.

The journey from "the build fails" to "here is the complete fleet management specification" spans approximately 180 messages and countless tool calls. It demonstrates that effective AI-assisted engineering is not about generating code in isolation — it is about maintaining coherent context across debugging cycles, asking the right questions at the right time, and building shared understanding through structured documentation. The vast-cuzk-plan.md file, written at the end of this chunk, is the crystallization of that understanding — a document that transforms a conversational design into an implementable specification for a self-managing GPU fleet.