The Deployment Question: From Docker Entrypoint to vast.ai Startup Scripts

In the middle of an intense coding session spanning Docker builds, GPU race conditions, and Filecoin proving pipelines, the user asks a short but pivotal question:

"When running in an env with no entrypoint (vast ssh) what on-startup script do I pass?"

This single sentence, message index 646 in the conversation, marks a critical transition point in the project. It is the moment when the work shifts from building to deploying, from development to operations. The question is concise, but it carries the weight of everything that came before it and opens the door to everything that follows. To understand why this message was written, we must trace the threads of context that converge in it.

The Context: A Docker Image Built for Production

The conversation leading up to this message spans dozens of rounds across multiple segments, all centered on one goal: creating a production-ready Docker container for Filecoin proving using the Curio and cuzk stack. The assistant had just completed the Docker build and pushed it to Docker Hub under the tag theuser/curio-cuzk:latest (<msg id=632-633>). The image contained four binaries — curio (163MB), sptool (210MB), cuzk-daemon (27MB), and cuzk-bench (5.5MB) — along with an entrypoint.sh script and a benchmark.sh script.

The entrypoint script, visible in [msg 623], was designed to run automatically when the Docker container starts. It fetches Filecoin proving parameters (the ~100GB parameter set) on first boot and then presumably launches the proving services. This is the standard Docker pattern: define an ENTRYPOINT in the Dockerfile, and the container runs it automatically.

The benchmark script, created in [msg 638], was a more specialized tool: it downloads test data (c1.json), starts cuzk-daemon, runs a warmup proof to trigger PCE extraction, and then benchmarks N proofs with configurable concurrency.

Both scripts were designed for the Docker runtime model — start the container, and things happen automatically.

The Shift: vast.ai Changes the Rules

The user's question reveals a new deployment target: vast.ai. Vast.ai is a marketplace for renting GPU instances. Unlike running a Docker container locally or on a traditional cloud provider, vast.ai gives users SSH access to a running container. The container's entrypoint does not execute automatically in this model — the user logs in via SSH and runs commands manually, or configures an "on-startup" script that vast.ai will execute when the instance boots.

This is a fundamentally different execution model. The elegant entrypoint-based design that worked perfectly for docker run --gpus all no longer applies. The user needs to bridge the gap between the Docker-native workflow and the vast.ai workflow.

The question "what on-startup script do I pass?" is the user asking for that bridge. They know the container has an entrypoint script. They know vast.ai has a startup script mechanism. But these are not the same thing, and the user needs guidance on how to translate between the two environments.

Assumptions Embedded in the Question

The user's question makes several implicit assumptions that are worth examining:

First, the user assumes that a startup script is the right mechanism for vast.ai. This is correct — vast.ai does support a "on-start" or "startup" command that runs when the instance boots. The user has clearly read enough of the vast.ai documentation or UI to know this exists.

Second, the user assumes that the existing entrypoint.sh is not directly suitable as the vast.ai startup script. This is a sophisticated observation. The entrypoint script likely contains logic that assumes Docker context — environment variable handling, signal trapping, maybe even an assumption that it's PID 1. Running it as a startup script in a vast.ai SSH environment could cause issues.

Third, the user assumes that there is some script they should pass — that the answer isn't "just run the daemon directly" but rather something more structured. This shows they understand the complexity of the proving pipeline: it's not just one binary, but a coordinated system involving parameter fetching, daemon startup, and proof processing.

Fourth, and most subtly, the user assumes that the assistant knows the vast.ai platform well enough to answer. This is a trust signal — the user has been working with the assistant through dozens of rounds and has come to rely on its expertise across domains ranging from Rust compilation to CUDA linker flags to Docker build optimization. Now they're extending that trust to deployment platform knowledge.

What You Need to Know to Understand This Message

To fully grasp the user's question, several pieces of prior knowledge are required:

The Docker entrypoint pattern: Understanding that Docker containers have an ENTRYPOINT that runs automatically, and that this is the standard way to start services in a container.
The vast.ai platform model: Knowing that vast.ai provides SSH access to rented GPU instances, that the container's entrypoint does not run automatically in SSH mode, and that vast.ai offers a startup script mechanism.
The project's architecture: Understanding that the proving pipeline involves multiple components — parameter fetching (which downloads ~100GB of data), the cuzk-daemon (which handles proof generation), and the curio node (which coordinates proving work). A startup script needs to orchestrate these correctly.
The PCE extraction process: Knowing that the first proof triggers Pre-Compiled Constraint Evaluator extraction, which takes 2-5 minutes and generates a pce-porep-32g.bin file. A good startup script should handle this warmup.
The previous work on entrypoint.sh and benchmark.sh: These scripts represent the assistant's understanding of how to start the system, and the user is essentially asking for a variant adapted to the vast.ai environment.

What This Message Creates

Though it's a question, this message creates significant output knowledge:

It identifies a gap in the deployment workflow. The Docker image is built and pushed, but the deployment instructions are incomplete. The entrypoint script works for docker run but not for vast.ai SSH. This message surfaces that gap.

It signals the user's deployment readiness. The user isn't asking "how do I deploy on vast.ai?" — they're asking a much more specific question about the startup script. This suggests they've already set up the vast.ai instance, configured the image, and are now at the final configuration step. They're ready to go live.

It frames the next task for the assistant. The assistant now needs to either explain how to use the existing entrypoint script in the vast.ai context, or create a new script tailored for vast.ai's startup mechanism. The answer will likely involve either pointing to entrypoint.sh with caveats, or creating a vast-startup.sh that handles the SSH environment.

It reveals the user's operational maturity. Asking about startup scripts rather than "how do I start the daemon" shows the user is thinking about automation, reliability, and hands-off operation. They don't want to SSH in and manually start processes — they want the instance to boot and start proving automatically.

The Broader Significance

This message, for all its brevity, represents a classic moment in any infrastructure project: the transition from "it builds" to "it runs." The Docker build succeeded, the binaries compiled, the smoke tests passed — but the real test is whether the system can deploy and operate autonomously in a production-like environment.

The user's question about vast.ai startup scripts is really a question about operationalization. It's asking: "We have a working system in a controlled environment. How do we make it work in the uncontrolled, automated, real world of GPU cloud instances?"

This is the moment where the project stops being a collection of binaries and scripts and starts being a deployable system. The entrypoint script was the first step toward that. The vast.ai startup script will be the second. And the comprehensive vast.ai management system documented in vast-cuzk-plan.md (mentioned in the chunk summary) will be the third.

The question also highlights the importance of platform-specific knowledge in modern infrastructure work. Docker's abstraction layer — "build once, run anywhere" — is powerful but incomplete. Every deployment platform has its quirks: vast.ai's SSH model, AWS's ECS task definitions, Kubernetes's pod specs. Bridging the gap between the generic container and the specific platform is where a lot of real engineering work happens.

Conclusion

The user's question at message 646 is a turning point. It's short — just 12 words — but it encapsulates the entire journey from development to deployment. It shows a user who has successfully navigated a complex build process, understands their deployment platform, and is now focused on the final operational details that will make the system run autonomously.

The question itself is the output of all the prior work: the Docker build, the entrypoint script, the benchmark script, the push to Docker Hub. Without that foundation, the question wouldn't make sense. And the answer to the question — whatever the assistant provides — will be the input to the next phase of the project: actual production deployment on rented GPU hardware.

In the flow of the conversation, this message is a bridge. It connects the development work of the past several segments to the operational work of the future. It's the moment when the project goes from "we have a Docker image" to "we have a running system." And it's a reminder that in infrastructure engineering, the last mile — the deployment details — is often where the most important questions live.