The Final Push: Publishing a CUDA 13 Proving Container to Docker Hub

In the life of a complex software project, few moments are as satisfying as the one where a long, arduous build process finally culminates in a successful publication. Message [msg 644] captures exactly such a moment. It is a deceptively brief message — just two sentences and a shell command — but it represents the culmination of hours of debugging, iteration, and problem-solving across multiple segments of an opencode coding session. The message reads:

Built successfully. All 4 binaries are in the image: curio (163M), cuzk (27M), cuzk-bench (5.5M), sptool (210M). Let me push:

>

``bash docker tag curio-cuzk:latest theuser/curio-cuzk:latest && docker push theuser/curio-cuzk:latest ``

On its surface, this is a simple status update followed by a routine Docker Hub push. But to understand its true significance, one must trace the winding path that led to this moment — a path strewn with linker errors, package conflicts, missing shared libraries, and architectural decisions that shaped the final container.

The Long Road to a Green Build

The Docker image being published here, theuser/curio-cuzk:latest, is no ordinary container. It bundles three major proving binaries — curio (163 MB), cuzk (27 MB), and sptool (210 MB) — along with a newly added cuzk-bench (5.5 MB) benchmarking tool, all compiled against CUDA 13.0.2 on Ubuntu 24.04. The image is approximately 3 GB in size and is designed to run Filecoin proof-of-replication (PoRep) and other proof types using GPU acceleration on vast.ai or similar GPU cloud platforms.

The build process documented in the preceding messages reads like a catalog of everything that can go wrong in a multi-stage Docker build. The first major blocker was a pip package conflict ([msg 608]). The supraseal build script (build.sh) creates a Python virtual environment and bootstraps its own pip via ensurepip. However, the Debian-managed python3-pip package was also installed in the builder image, creating a conflict: when the venv's pip tried to upgrade itself with pip install --upgrade pip, it attempted to uninstall the system pip, which failed with a "RECORD file not found" error. The fix was subtle but effective — simply removing python3-pip from the apt-get install list so that only the venv's pip exists.

The second blocker was a linker error involving libcudart_static.a. The Go linker (used to build the curio and sptool binaries, which are Go programs with CUDA bindings via cgo) could not find the static CUDA runtime library. Through a series of investigative bash commands (<msg id=608-611>), the assistant discovered that while libcudart_static.a existed at /usr/local/cuda/lib64/libcudart_static.a, this directory was not in the LIBRARY_PATH environment variable used by the Go linker. The fix was adding /usr/local/cuda/lib64 to LIBRARY_PATH in the Dockerfile.

The third category of issues emerged during smoke testing (<msg id=614-617>). When attempting to run the freshly built container, curio crashed with missing shared library errors: libconfig++.so.9, libaio.so.1t64, libfuse3.so.3, and libarchive.so.13 were all absent from the runtime stage. These are dependencies of the supraseal/SPDK (Storage Performance Development Kit) layer that curio uses for its high-performance storage operations. The fix was adding libconfig++9v5, libaio1t64, libfuse3-3, and libarchive13t64 to the runtime stage's apt-get install command.

Each of these fixes required a Docker rebuild, which — even with layer caching — takes time for a 3 GB image. The assistant iterated through them methodically, using docker run with --entrypoint bash to test individual components without needing a GPU, and using ldd to identify missing shared libraries.

The Benchmark Script Detour

Just as the build was declared complete and the image was first pushed to Docker Hub ([msg 633]), the user introduced a new requirement ([msg 634]): "Can you add some benchmark.sh script which benchmarks N (default 5) porep proofs with cuzk (ideally with warmup which dispatches one and waits for pce file to appear)?"

This request sent the assistant on a research expedition. It spawned a subagent task ([msg 635]) to explore the cuzk codebase and understand how benchmarking works. The research revealed that cuzk-bench is a separate binary with a batch subcommand that can run multiple proofs and report timing. It also discovered a downloadable C1 output JSON file (~51 MB) from a public R2 bucket that serves as test data for PoRep proofs.

A second subagent task ([msg 636]) investigated how C1 output test data is obtained, confirming the download URL and the structure of the test data. The assistant then examined the existing e2e test script ([msg 637]) to understand the daemon lifecycle management pattern.

Armed with this knowledge, the assistant wrote benchmark.sh ([msg 638]), a sophisticated script that:

  1. Downloads the C1 test data if not present
  2. Starts cuzk-daemon and waits for it to respond
  3. Runs a warmup proof that triggers PCE (Pre-Compiled Constraint Evaluator) extraction if needed
  4. Runs N benchmark proofs via cuzk-bench batch
  5. Reports timing metrics and cleans up The script was placed at docker/cuzk/benchmark.sh and the Dockerfile was edited to build cuzk-bench in the builder stage and copy both the binary and the script into the runtime image (<msg id=641-642>).

The Significance of Message 644

Message [msg 644] is the moment where all this work converges. The rebuild that included cuzk-bench and benchmark.sh succeeded, and the assistant pushed the updated image to Docker Hub. The message's brevity belies its importance: this is the first time the complete, production-ready container is published with all four binaries, all runtime dependencies, and the benchmarking infrastructure in place.

The message also reveals an interesting aspect of the assistant's working style. Rather than waiting for the user to ask for the push, the assistant proactively tags and pushes immediately after the build succeeds. The phrase "Let me push" is a natural continuation — the assistant recognizes that the user's goal (expressed in [msg 627]: "How to publish to dockerhub?") is to have the image on Docker Hub, and every rebuild should be pushed.

Technical Architecture of the Final Image

The Docker image that ships in this message is a multi-stage build with two stages:

Stage 1 (builder): Based on nvidia/cuda:13.0.2-devel-ubuntu24.04, this stage installs Go 1.24, Rust 1.86, and gcc-13. It builds all four binaries:

What This Message Creates

The output knowledge created by this message is substantial. The Docker Hub repository theuser/curio-cuzk:latest now contains a fully functional proving environment that can be pulled onto any GPU-equipped machine and run with a single command:

docker run --gpus all -v proof-params:/var/tmp/filecoin-proof-parameters -it theuser/curio-cuzk:latest

This is the foundation for the vast.ai deployment system that the assistant would go on to design in the subsequent chunk ([chunk 5.0]), documented in vast-cuzk-plan.md. The published image is the atomic unit of deployment — the thing that gets instantiated on each GPU runner in the fleet.

Assumptions and Implicit Knowledge

The message makes several assumptions. It assumes that Docker Hub credentials are configured on the build machine (the docker push command succeeded, confirming this). It assumes that the user wants the latest tag updated (rather than a versioned tag). It assumes that the four binaries are sufficient for the intended use case — which, given the subsequent design of the vast.ai management system, proves correct.

The message also assumes a certain level of domain knowledge on the reader's part. Terms like "curio", "cuzk", "sptool", and "cuzk-bench" are used without explanation. The sizes (163M, 27M, 5.5M, 210M) are reported as meaningful metrics — and they are, in the context of a Docker image where every megabyte affects push/pull times and storage costs.

Conclusion

Message [msg 644] is a milestone marker in a complex engineering effort. It represents the successful publication of a CUDA 13 proving container after overcoming multiple build blockers, incorporating a user-requested benchmarking feature, and establishing the deployment artifact that would underpin the entire vast.ai fleet management system. Its brevity is a testament to the assistant's efficiency — when the build succeeds and the push completes, there is little more to say. The work speaks for itself.