The Art of the Surgical Docker Rebuild: How Three File Copies Fixed a Production Deadlock

In the high-stakes world of decentralized storage proving systems, a production bug can cascade into silent failures that corrupt proofs, waste GPU cycles, and erode trust in the network. When the assistant encountered two critical production bugs in the ProofShare system of the Curio project—a deadlock caused by HTTP 429 retries and a job ID collision that mixed partition proofs—the fixes themselves were only half the battle. The other half was getting those fixes compiled into a binary that would actually run on the remote GPU worker. Message [msg 1948] captures a pivotal moment in that deployment struggle: a single docker cp command that elegantly sidesteps a build cache problem to inject three modified source files into a Docker container for recompilation.

The Message Itself

The message is deceptively simple—a single bash command executed via the assistant's tool interface:

docker cp /tmp/czk/tasks/proofshare/task_request.go curio-rebuild:/build/tasks/proofshare/task_request.go && docker cp /tmp/czk/tasks/proofshare/task_prove.go curio-rebuild:/build/tasks/proofshare/task_prove.go && docker cp /tmp/czk/lib/proofsvc/provictl.go curio-rebuild:/build/lib/proofsvc/provictl.go && echo "copied"

Three files, one container, one echoed confirmation. But behind this terse command lies a rich tapestry of debugging, architectural understanding, and operational pragmatism.

The Context: Why This Message Exists

To understand why this message was written, we must trace the chain of events that led to it. The assistant had just implemented a series of fixes for two production bugs in the ProofShare system (see [msg 1919] through [msg 1933]). The first bug was a deadlock in TaskRequestProofs: the CreateWorkAsk function would retry HTTP 429 responses indefinitely with exponential backoff, blocking the poll loop from discovering matched work and inserting it into the proofshare_queue. The fix made CreateWorkAsk return a sentinel ErrTooManyRequests error immediately on 429, allowing the poll loop to continue, with progress-based exponential backoff to avoid hammering the service.

The second bug was more insidious: a job ID collision in the cuzk proving engine. ProofShare challenges all targeted the same miner (1000) and sector (1), so concurrent tasks sent identical job_id values to the GPU proving engine. The engine's partition assembler keyed on job_id, causing partition results from different proofs to mix—confirmed by a "partition 0 already inserted" panic. The fix added the harmony task ID to the RequestId, making it unique per invocation.

These fixes were applied to three Go source files: task_request.go, task_prove.go, and provictl.go. The assistant verified they compiled cleanly with go build and go vet (see [msg 1930] and [msg 1931]). But then came the deployment challenge.

The Deployment Problem: Shared Libraries and Docker Builds

When the user asked to "build curio and send updated binary to the vast host" ([msg 1934]), the assistant initially tried a straightforward approach: build the binary locally and upload it via SCP. The local build succeeded, producing a 163MB binary ([msg 1939]), and the upload completed ([msg 1940]). But when the assistant tried to swap the binary on the remote host, it failed with a shared library error: curio: error while loading shared libraries: libconfig++.so.15: cannot open shared object file ([msg 1942]).

This failure reveals a critical architectural detail: the Curio binary is not a standalone Go executable. It links against CUDA and supraseal libraries that are only available in the specialized Docker build environment defined by Dockerfile.cuzk. The local machine lacks these libraries, so the locally-built binary cannot run on the GPU worker.

The user then clarified: "it is also a curio node, maybe you need to docker-build (Dockerfile.cuzk) and send the binary from docker build" ([msg 1944]). This instruction set the assistant on the correct path: build inside the Docker environment where all the required shared libraries are available.

The Cache Problem: When Docker Build Doesn't Rebuild

The assistant examined Dockerfile.cuzk ([msg 1945]) and attempted a Docker build targeting the builder stage: docker build -f Dockerfile.cuzk --target builder -t curio-builder:latest . ([msg 1946]). The build completed, but the output revealed a problem: the curio binary was timestamped Mar 13 08:45, predating the source changes made at 09:38. Docker's layer caching had kicked in—the COPY . . instruction detected no changes to the source tree (or more precisely, the layer hash was unchanged because the Docker build context was the same), so it reused the cached layer containing the old binary.

This is a classic Docker build cache gotcha. The COPY . . instruction copies the entire build context into the image. If the build context hasn't changed (or if Docker's cache detection doesn't pick up the modifications), the cached layer is reused. The assistant's reasoning notes this explicitly: "The builder stage used a cached curio binary from before my changes."

The Surgical Solution: docker cp

At this point, the assistant faced a choice. The brute-force solution would be to rebuild with --no-cache, which would invalidate all cached layers and force a full rebuild from scratch. But this would take significant time—the builder stage involves installing CUDA tools, compiling Rust components, and building Go code. A full rebuild could take 10-15 minutes or more.

The alternative, which the assistant chose, was far more elegant. Instead of rebuilding the entire Docker image, the assistant:

Created a container from the existing builder image: docker create --name curio-rebuild curio-builder:latest /bin/true ([msg 1947])
Copied only the three modified source files into the container using docker cp (the subject message, [msg 1948]) This approach leverages Docker's container-layer architecture. The builder image already contains all the build dependencies—Go toolchain, Rust compiler, CUDA libraries, supraseal headers—compiled into its layers. By creating a container from this image and copying only the modified files, the assistant preserves the cached build environment while ensuring the source changes are present for recompilation. The choice of docker cp over docker build --no-cache reflects a deep understanding of Docker's caching mechanics and a pragmatic trade-off: minimal intervention for maximum effect. The next step (which would follow in subsequent messages) would be to execute go build inside the container, producing a binary that incorporates the proofshare fixes while linking against the correct shared libraries.

Input Knowledge Required

To understand this message, one must know:

The file paths: The three modified files live in /tmp/czk/tasks/proofshare/ and /tmp/czk/lib/proofsvc/. These paths correspond to the Go package structure of the Curio project.
The Docker container name: curio-rebuild was created in the previous message ([msg 1947]) from the curio-builder:latest image. The container's filesystem mirrors the builder image, with the build context at /build/.
The Dockerfile structure: The builder stage copies source code to /build/ (as seen in Dockerfile.cuzk), so the container's internal paths mirror the source tree.
The build dependency chain: The Curio binary requires CUDA and supraseal libraries that are only available in the Docker build environment, which is why local builds produce non-functional binaries.
The nature of the fixes: The three files contain the proofshare deadlock fix, job ID collision fix, queue cleanup improvements, and dedup scoping—all critical production patches.

Output Knowledge Created

This message produces a concrete, verifiable outcome: three modified source files are now present inside the curio-rebuild container at their correct paths, overwriting the stale versions from the cached build. The echo "copied" provides immediate confirmation that all three docker cp commands succeeded.

This sets the stage for the subsequent rebuild: the assistant can now execute docker exec curio-rebuild go build -o curio ./cmd/curio (or similar) to produce an updated binary that incorporates the proofshare fixes and links against the correct shared libraries.

The Thinking Process

The assistant's reasoning, visible in the agent reasoning blocks preceding the message, reveals a systematic problem-solving approach:

Recognition of the cache problem: The assistant notes that "the builder stage used a cached curio binary from before my changes" and that "the curio binary is 163M Mar 13 08:45 which is from 08:45, but I built locally at 09:38."
Evaluation of alternatives: The assistant considers --no-cache but rejects it as too heavy: "Rather than rebuilding everything from scratch with --no-cache, I could spin up a container from the builder image, copy my modified source files into it, rebuild curio there, and extract the updated binary."
Execution of the chosen approach: The assistant creates the container first, then copies the files. The && echo "copied" pattern is a simple but effective verification mechanism—if any docker cp fails, the chain breaks and "copied" is not printed.

Assumptions and Potential Pitfalls

The assistant makes several assumptions in this message:

That the builder image's /build/ directory structure matches the source tree: This is a reasonable assumption given that Dockerfile.cuzk uses COPY . . to copy the entire source context. However, if the Dockerfile had a different build directory structure (e.g., copying to /app/ or using subdirectory filters), the paths would be wrong.
That the modified files are the only ones that changed: The assistant assumes that no other source files were modified and that the three copied files are sufficient to produce a correct binary. This is correct for the proofshare fixes, but if there were cross-package dependencies that also changed, they would need to be copied as well.
That the container's filesystem is writable: docker cp copies files into the container's writable layer, which is always writable for a running container. This is a safe assumption.
That the Go build system will detect the file changes and recompile: Go's build cache is sophisticated—it tracks file hashes and recompiles only when inputs change. The modified source files will trigger recompilation of the affected packages and their dependents. One subtle issue: the docker create command in the previous message used /bin/true as the command, meaning the container exits immediately. However, docker cp works on stopped containers—it copies files into the container's filesystem layer regardless of whether the container is running. So this is fine.

Broader Significance

This message exemplifies a pattern that recurs throughout software engineering: the gap between "the code compiles" and "the code runs in production." The assistant's fixes were verified to compile cleanly on the local machine, but the deployment environment had different constraints—shared libraries, CUDA dependencies, and a Docker build pipeline. Bridging this gap required understanding not just the code changes, but the build infrastructure, the Docker caching semantics, and the remote host's library dependencies.

The docker cp approach is a microcosm of a larger DevOps philosophy: minimize rebuild time by maximizing cache reuse. Instead of rebuilding the entire Docker image (which would invalidate all cached layers), the assistant surgically replaces only the files that changed. This is the same principle behind incremental compilation, hot module replacement in web development, and partial deployment strategies in distributed systems.

Conclusion

Message [msg 1948] is a masterclass in pragmatic deployment engineering. Faced with a Docker cache that refused to acknowledge source changes, the assistant didn't reach for the nuclear option of --no-cache. Instead, it leveraged Docker's container-layer architecture to inject modified files directly into a cached build environment. The three docker cp commands—copying task_request.go, task_prove.go, and provictl.go—represent the culmination of a debugging journey that began with a production deadlock, traced through Go source code, Docker build files, and remote SSH sessions, and arrived at a minimal, targeted intervention that would produce a correct, deployable binary.

The message is a reminder that in complex distributed systems, the most impactful operations are often the simplest ones—if you have the context to know where to apply them.