The Build That Nearly Wasn't: Docker Cache and the Perils of Patching Distributed Proving Systems

In the high-stakes world of Filecoin proof generation, where GPU workers churn through cryptographic computations and distributed systems must remain continuously operational, a single build command can represent the culmination of hours of debugging. Message [msg 1937] captures one such moment: the assistant runs cd /tmp/czk && make curio 2>&1 | tail -30 to produce an updated Curio binary. On its surface, this is a routine invocation of a Makefile target. But in the context of the session, this build attempt is the critical bridge between a set of carefully designed bug fixes and their deployment to a production GPU worker — and it would soon reveal a subtle but consequential failure mode.

The Message in Full

The assistant's message is a straightforward bash invocation:

[assistant] [bash] cd /tmp/czk && make curio 2>&1 | tail -30
Configuring go to allow (-Wl,--whole-archive|-Wl,--no-as-needed|-Wl,--no-whole-archive|-Wl,--allow-multiple-definition|--whole-archive|--no-as-needed|--no-whole-archive|--allow-multiple-definition)
warning: go env -w CGO_LDFLAGS_ALLOW=... does not override conflicting OS environment variable
rm -f curio
GOAMD64=v3 CGO_LDFLAGS_ALLOW='.*' go build  \
-tags "cunative" \
-o curio -ldflags " -s -w \
-X github.com/filecoin-project/curio/build.IsOpencl= \
-X github.com/filecoin-project/curio/build.Curr...

The output is truncated by tail -30, showing only the tail end of the build process. The visible lines reveal the build configuration: GOAMD64=v3 for AMD64 microarchitecture optimizations, -tags "cunative" for CUDA native compilation, and linker flags that strip debug symbols (-s -w) and inject version metadata via -X flags. The warning about CGO_LDFLAGS_ALLOW not overriding the OS environment variable is a minor diagnostic note, not a build failure.

The Context: Why This Build Was Necessary

To understand the significance of this message, one must trace back through the preceding conversation. The assistant had just implemented a series of fixes for the ProofShare system — a distributed proof marketplace where Curio nodes request proof generation work from a remote service and coordinate its execution across GPU workers. Two critical production bugs had been identified and patched:

Bug 1: The HTTP 429 Deadlock. The TaskRequestProofs task contained a Do() loop that polled a remote service for proof requests, created "work asks" to signal availability, and inserted matched work into a local proofshare_queue table for execution. The CreateWorkAsk function in provictl.go had an infinite retry loop on HTTP 429 (Too Many Requests) responses, with exponential backoff that could escalate to a 5-minute interval — but crucially, no overall timeout. If the service rate-limited the node, CreateWorkAsk would block forever, preventing the poll loop from ever discovering work that had already been matched to existing asks. This created a permanent deadlock: asks could never be created, matched work was never discovered, PSProve tasks starved, slots were never freed, and the 429 condition never cleared. The fix made CreateWorkAsk return a sentinel ErrTooManyRequests error immediately on 429, allowing the poll loop to continue, and added progress-based exponential backoff to the outer loop.

Bug 2: The Job ID Collision. Even after deploying a container-built cuzk binary that passed benchmarks, all ten PoRep partitions were still producing invalid proofs. The root cause was a job ID collision: ProofShare challenges all targeted the same miner=1000, sector=1 (a bench sector), so concurrent tasks sent identical job_id values to the cuzk 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.

Additional fixes included scoping the dedup SELECT to only non-submitted rows, changing orphan cleanup from DELETE to UPDATE to preserve fetched work, and adding a routine to purge completed rows older than two days. All of these changes lived in three Go source files: lib/proofsvc/provictl.go, tasks/proofshare/task_request.go, and tasks/proofshare/task_prove.go.

The Assumption That Nearly Failed

The build in [msg 1937] was executed on the local development machine, not inside the Docker build environment defined by Dockerfile.cuzk. This assumption — that a native go build would produce a binary compatible with the remote GPU worker — was incorrect, and the consequences would surface in the very next messages.

When the assistant attempted to upload and swap the binary on the vast host at 141.195.21.72 ([msg 1942]), the remote machine reported:

curio: error while loading shared libraries: libconfig++.so.15: cannot open shared object file: No such file or directory

The locally built binary was dynamically linked against shared libraries from the host's build environment — libraries that were absent on the target machine. The remote host, a CUDA GPU worker provisioned through vast.ai, had its runtime environment defined by the Docker image built from Dockerfile.cuzk, which included the supraseal dependencies (libconfig++, CUDA runtime libraries, etc.) in specific paths. A binary built outside that environment would reference libraries at paths that didn't exist on the target.

The user immediately identified the issue ([msg 1944]): "it is also a curio node, maybe you need to docker-build (Dockerfile.cuzk) and send the binary from docker build." This prompted the assistant to pivot to a Docker-based build strategy, using the builder stage of Dockerfile.cuzk as the compilation environment.

Input Knowledge Required

To understand this message, a reader needs knowledge of several domains:

  1. The Go build system: Understanding that go build with -tags "cunative" enables conditional compilation for CUDA support, and that -ldflags with -X injects version strings at compile time.
  2. The Curio project structure: Knowing that make curio invokes a build target that produces a single binary from ./cmd/curio, and that this binary is the main entry point for the Curio node.
  3. The ProofShare architecture: Understanding that the Curio binary contains the TaskRequestProofs and TaskProvideSnark tasks that were just modified, and that these tasks coordinate with a remote proof service and a local cuzk GPU proving daemon.
  4. The deployment environment: Knowing that the target host is a vast.ai GPU instance running a Docker-based runtime, and that binaries must be linked against the shared libraries present in that runtime environment.
  5. The concept of dynamic linking: Understanding that a Go binary can be either statically linked (self-contained) or dynamically linked against system libraries, and that the latter creates dependencies on specific library versions and paths.

Output Knowledge Created

This message produced a locally compiled Curio binary at /tmp/czk/curio. However, the true output knowledge created by this message is more subtle: it demonstrated that the locally built binary was not sufficient for deployment. The failure that followed ([msg 1942]) taught the critical lesson that in heterogeneous deployment environments — especially those involving CUDA GPU workers with specialized runtime dependencies — binaries must be built in the same environment (or an equivalent one) where they will execute.

The subsequent Docker-based build ([msg 1949]) succeeded where the local build failed, producing a binary at /tmp/curio-psfix that was correctly linked against the shared libraries available in the Docker runtime. This binary was successfully deployed ([msg 1953]), and the version string confirmed it contained the fixes: curio version 1.27.3-rc2+mainnet+git_96f6c783_2026-03-11T16:08:33+01:00_psfix.

The Thinking Process

The assistant's reasoning in [msg 1935] reveals the straightforward thought process behind this build: "The user wants me to build the Curio binary and send it to the vast host. From the context, the remote machine is at ssh -p 40362 root@141.195.21.72. Let me figure out how to build Curio - this is a Go project, so I need to find the main entry point and build it."

This reasoning is logical but incomplete. The assistant correctly identifies that Curio is a Go project and locates the entry point in ./cmd/curio/. It correctly invokes make curio which is the standard build target. However, it does not consider the runtime environment of the target host — a critical oversight in a production deployment context where the build environment and runtime environment may differ significantly.

The todo list in [msg 1935] shows the assistant's planned workflow: build → upload → restart. This is a reasonable sequence, but it assumes that a locally built binary will function correctly on the remote host. The assumption is natural — Go binaries are often statically linked and portable — but the Curio project's use of CGO (C Go bindings) for CUDA and supraseal integration introduces dynamic linking dependencies that break this portability.

Broader Implications

This message, and the events that followed, illustrate a fundamental tension in distributed systems development: the gap between development environments and production environments. In an ideal world, all binaries would be built in hermetic, reproducible build environments (like Docker multi-stage builds) that exactly mirror the production runtime. In practice, developers often build locally for speed and convenience, and this shortcut can introduce subtle failures.

The lesson extends beyond this specific build. The same principle applies to any system with native dependencies — CUDA libraries, hardware-specific drivers, protocol buffers, or system-level shared objects. A binary that compiles and runs perfectly on a developer's workstation may fail catastrophically in production due to missing libraries, different library versions, or different search paths.

For the Curio/cuzk system specifically, the Docker-based build workflow established in the subsequent messages became the standard deployment pattern. The Dockerfile.cuzk multi-stage build — with a builder stage containing all development dependencies (CUDA 13 devel, Go 1.24, Rust 1.86, gcc-13) and a runtime stage containing only the necessary shared libraries — ensures that binaries are linked against the exact libraries available in production. This is the correct approach for a system that bridges Go application logic, Rust GPU proving engines, and CUDA-accelerated cryptographic operations.

Conclusion

Message [msg 1937] appears, at first glance, to be a routine build command — the kind of message that fills the gaps between the interesting work of debugging and the satisfying moment of deployment. But it is precisely this kind of message that reveals the most about the engineering process. It shows the moment when theory meets practice, when carefully crafted source code must survive the transition from text editor to running binary. And when the locally built binary failed on the target host, it forced a deeper understanding of the deployment pipeline — an understanding that would prove essential for all future fixes and deployments in this session.

The build in [msg 1937] didn't produce a deployable binary, but it produced something more valuable: the knowledge that the build environment matters, that assumptions about portability must be verified, and that in distributed systems with heterogeneous runtime environments, the only safe binary is one built in the environment where it will run.