The Redeployment: Creating Fresh Instances on Vast.ai with a Hardened Pipeline

Introduction

In the sprawling, multi-threaded narrative of deploying a Filecoin proof-of-replication proving pipeline across rented GPU instances on Vast.ai, few moments are as satisfying as the one captured in message 1018. After hours of debugging platform quirks, fixing race conditions, hardening scripts against timeout failures, and resolving a critical service-matching bug in the management layer, the assistant finally reaches a clean slate. Message 1018 is the pivot point: two vastai create instance commands that launch fresh compute instances on two geographically distant hosts, carrying a newly fixed Docker image and a carefully engineered workaround for Vast's SSH-mode entrypoint replacement. This message is not merely about provisioning cloud resources—it is the culmination of a sustained debugging campaign and the beginning of a new phase focused on hardware-aware pipeline tuning.

The Context and Motivation

To understand why message 1018 was written, one must trace the events of the preceding hour. The assistant had been tasked with deploying a CuZK-based proof pipeline across multiple Vast.ai GPU instances. The pipeline involved a complex lifecycle: a Docker entrypoint script that fetched Filecoin proof parameters, ran a benchmark to measure proof throughput, and then launched a Curio proving worker. A management service called vast-manager tracked instance state through a SQLite database, orchestrating the lifecycle across potentially dozens of machines.

The initial deployment had hit multiple failures. The first was a gRPC transport error during the warmup proof—the initial proof with Pre-Compiled Constraint Evaluator (PCE) extraction took several minutes, causing the client connection to time out. The benchmark script, running with set -e, aborted the entire pipeline. The assistant fixed this by hardening benchmark.sh to allow warmup failures without aborting, and by updating entrypoint.sh to handle benchmark failure gracefully.

A deeper problem emerged around the VAST_CONTAINERLABEL environment variable. The assistant had designed the entrypoint to detect this variable to confirm it was running on Vast.ai. But when testing via SSH session, the variable was invisible—it turned out Vast.ai injects it as a non-exported shell variable in .bashrc, making it available to interactive shells but not to child processes. The fix involved using --onstart-cmd instead of relying on the Docker ENTRYPOINT.

Then came the vast-manager monitor bug: the service matched DB instances to Vast API instances solely by the API's label field, which remains null unless explicitly set via vastai label. This caused the monitor to incorrectly kill instances. The fix introduced an ID-based fallback map (idMap) that extracts the Vast instance ID from the C.<id> label pattern.

By message 1017, the assistant had destroyed the old instances (32710471 on host 93197, and 32705217 on host 88910), cleaned the stale database entries, and pushed the fixed Docker image. Message 1018 is the natural next step: create fresh instances with all fixes baked in.

The Decision-Making Process

The assistant begins message 1018 by evaluating available offers on the two target hosts. This evaluation is not arbitrary—it reflects a careful balance of cost, capability, and memory constraints learned through painful experience.

On host 93197 (BC Canada), three offers were available: a 2x RTX 3090 with 125GB RAM at $0.28/hr, a 1x RTX 3090 with 62GB RAM at $0.14/hr, and a 2x RTX 3070 with 167GB RAM at $0.17/hr. The assistant selected the 2x RTX 3090 option (offer 29854362). This choice prioritizes GPU compute power over RAM—the 2x 3090 configuration provides 48GB of VRAM total, which is critical for the memory-intensive proof synthesis. The 125GB system RAM is a known constraint (the assistant will later discover it causes OOM kills), but at this point it is the best available option on that host.

On host 88910 (Norway), the assistant selected a 1x RTX 4090 with 500GB RAM at $0.47/hr (offer 31006895). This is a premium configuration: the RTX 4090 is the fastest consumer GPU available, and the 500GB RAM is more than sufficient for any proof synthesis workload. The cost is higher, but the reliability and performance justify it.

The assistant's reasoning is visible in the concise summary: "Good offers." This terse assessment belies the depth of analysis—the assistant has been tracking memory usage patterns, knows that the PCE extraction and proof synthesis can consume 100+ GB of RAM, and understands that GPU memory (VRAM) is the primary bottleneck for proof computation speed. The selection reflects a pragmatic trade-off: on host 93197, take the best GPU available even with limited RAM; on host 88910, take the configuration that guarantees success.

The Technical Details: Anatomy of a Create Command

Each vastai create instance command is a dense packet of configuration decisions:

vastai create instance 29854362 \
  --image theuser/curio-cuzk:latest \
  --disk 250 \
  --env '-e PAVAIL=[REDACTED] -e PAVAIL_SERVER=[REDACTED]' \
  --ssh --direct \
  --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'

The --image flag specifies the freshly pushed Docker image containing all the script fixes. The --disk 250 allocates 250GB of persistent storage—enough for the Filecoin proof parameters (which can be tens of gigabytes) and the benchmark data.

The --env flag passes two critical environment variables: PAVAIL (an authentication token for the port availability tunnel service) and PAVAIL_SERVER (the address of that service). These enable the portavailc tunnel that allows the management service to reach the instance even when it's behind NAT or firewalls.

The --ssh --direct flags are the most consequential. On Vast.ai, --ssh mode replaces the Docker ENTRYPOINT with Vast's own SSH initialization script. This is normally a problem—it means the carefully crafted entrypoint.sh never runs automatically. The --direct flag tells Vast to use direct connectivity (as opposed to proxy tunnels). The combination means the instance boots into SSH mode, and the user must manually trigger the entrypoint.

This is where --onstart-cmd comes in. This flag specifies a command that Vast runs after the SSH init script completes. By passing nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &, the assistant ensures the entrypoint runs in the background as soon as the instance is ready. The nohup and & detach it from the SSH session, the output redirection captures logs, and the background execution allows the SSH session to remain available for debugging. This is the workaround discovered in the previous chunk—it provides both automated lifecycle management and manual access via SSH.

Assumptions Made

Every deployment command carries assumptions. The assistant assumes that the Vast.ai API will correctly interpret the --onstart-cmd and execute it after SSH initialization. It assumes the Docker image is correctly tagged and available on Docker Hub (it was pushed moments earlier). It assumes the PAVAIL and PAVAIL_SERVER environment variables will be available to the entrypoint script—a reasonable assumption since they're passed via Docker -e flags.

More subtly, the assistant assumes that 250GB of disk is sufficient for both the proof parameters and the benchmark data. It assumes that the 2x RTX 3090 on host 93197 can complete the benchmark despite the 125GB RAM constraint—an assumption that will later prove incorrect when the OOM killer terminates the process. It assumes the vast-manager's fixed matching logic will correctly identify and manage these new instances.

Input Knowledge Required

To fully understand message 1018, one needs knowledge of several domains. First, the Vast.ai platform: what vastai create instance does, what --ssh --direct means, how --onstart-cmd works, and how instance IDs and host IDs relate to the marketplace. Second, the Filecoin proof pipeline: what CuZK is, why PCE extraction is memory-intensive, what partition workers do, and why the benchmark is necessary. Third, the Docker ecosystem: how image tagging and pushing work, how environment variables are passed to containers, and how ENTRYPOINT interacts with different launch modes. Fourth, the networking layer: what portavailc does, why a tunnel is needed, and how the management service communicates with instances.

Output Knowledge Created

Message 1018 creates two running instances on Vast.ai. Instance 32711932 (2x RTX 3090, BC Canada) and instance 32711934 (1x RTX 4090, Norway) are now provisioned with the fixed software stack. Their instance API keys are returned, allowing the vast-manager to authenticate with them. The message establishes the baseline configuration for the next phase of testing—the Norway instance will successfully complete its benchmark, validating the entire pipeline, while the BC Canada instance will reveal the memory constraint that drives the next iteration of fixes.

The Broader Significance

Message 1018 represents a transition. The previous chunks focused on platform-specific debugging: fixing Vast's SSH environment handling, correcting the vast-manager's matching logic, hardening scripts against timeout failures. With these fresh instances, the assistant moves from deployment logistics to workload-specific resource tuning. The successful creation of both instances proves that the deployment pipeline works—the Docker image is correct, the entrypoint workaround functions, the management service can track new instances. What remains is to discover and adapt to the hardware constraints of each individual machine, a challenge that will define the subsequent chunk.

In the larger narrative, message 1018 is the moment when the system becomes operational. The fixes are no longer theoretical patches to scripts—they are baked into a Docker image and running on real hardware. The instances will register with the vast-manager, download parameters, and begin benchmarking. The results of those benchmarks will feed back into the configuration, creating a feedback loop between deployment and optimization. This message is the bridge between debugging and production, between fragility and resilience, between theory and practice.## The Message Itself

Here is the subject message in full, with secrets redacted:

Good offers: - Host 93197: offer 29854362 — 2x RTX 3090, $0.28/hr, 125GB RAM, BC Canada - Host 88910: offer 31006895 — 1x RTX 4090, $0.47/hr, 500GB RAM, Norway

>

Create both:

>

[bash] ssh 10.1.2.104 "vastai create instance 29854362 --image theuser/curio-cuzk:latest --disk 250 --env '-e PAVAIL=[REDACTED] -e PAVAIL_SERVER=[REDACTED]' --ssh --direct --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'" 2>&1 Started. {'success': True, 'new_contract': 32711932, 'instance_api_key': '[REDACTED]'}

>

[bash] ssh 10.1.2.104 "vastai create instance 31006895 --image theuser/curio-cuzk:latest --disk 250 --env '-e PAVAIL=[REDACTED] -e PAVAIL_SERVER=[REDACTED]' --ssh --direct --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'" 2>&1 Started. {'success': True, 'new_contract': 32711934, 'instance_api_key': '[REDACTED]'}

The structure is deceptively simple. The assistant opens with a two-line summary of the selected offers, then executes two vastai create instance commands in parallel via a single SSH invocation to the management host at 10.1.2.104. Each command returns a JSON response confirming success and providing the new contract ID and instance API key. The entire message is just 11 lines of output, yet it represents the culmination of dozens of preceding messages of debugging and fixes.

The parallel execution is notable. Both vastai create instance commands are dispatched in the same bash call, separated by a newline. This is not a script—it's two commands issued sequentially within a single SSH session. The Vast.ai API processes them independently, and the assistant receives both responses in the same output block. This efficiency reflects the assistant's awareness that instance creation is an idempotent, non-interfering operation—there is no risk of race conditions between two independent API calls.

Mistakes and Incorrect Assumptions

The most significant incorrect assumption in this message concerns the memory capacity of the BC Canada instance. The assistant selects the 2x RTX 3090 with 125GB RAM, believing this to be sufficient for the proof synthesis workload. In reality, as the subsequent chunk reveals, the initial PCE extraction and proof synthesis consume more than 125GB of RAM, triggering the OOM killer and terminating the instance. The Norway instance with 500GB RAM handles the same workload without issue.

This is not a failure of reasoning per se—the assistant had limited information about the memory footprint of the uncached PCE extraction on this particular hardware combination. The 125GB RAM had been sufficient for earlier tests on other machines, and the assistant had no way to predict that this specific configuration would exceed the threshold. The mistake is a natural consequence of deploying to heterogeneous hardware: each machine has different performance characteristics, and only empirical testing reveals the boundaries.

A second assumption worth questioning is the reliance on --ssh --direct mode. While this provides the flexibility of SSH access alongside the automated entrypoint via --onstart-cmd, it introduces a dependency on the Vast.ai SSH infrastructure. If the SSH daemon fails to start, or if the --onstart-cmd is not executed (due to a Vast platform bug), the instance will boot but never run the entrypoint. The assistant is trading reliability for flexibility—a reasonable trade-off given the debugging context, but one that could cause issues in a fully automated production deployment.

Thinking Process and Reasoning

The thinking visible in this message is compressed but discernible. The assistant begins by evaluating offers, implicitly comparing them against known requirements. The selection criteria are not stated but can be inferred: GPU compute power (RTX 3090 vs 3070, RTX 4090), memory capacity (125GB vs 62GB vs 500GB), and cost ($0.28/hr vs $0.47/hr). The assistant is balancing performance against budget, choosing the best GPU available on each host while accepting the memory constraints.

The --onstart-cmd workaround reveals the most sophisticated reasoning. The assistant has learned from the previous chunk that VAST_CONTAINERLABEL is not available in SSH sessions, that the Docker ENTRYPOINT is replaced by Vast's SSH init, and that the solution is to run the entrypoint in the background via --onstart-cmd. This is not documented behavior—it was discovered through iterative debugging. The command structure (nohup ... > /var/log/entrypoint.log 2>&1 &) is carefully designed: nohup prevents SIGHUP from killing the process when the SSH session ends, output redirection captures all logs, and the background & ensures the SSH session remains interactive.

The assistant also demonstrates awareness of the management infrastructure. The commands are executed via SSH to 10.1.2.104, which is the vast-manager host. This means the assistant is not running Vast CLI commands locally—it is remotely executing them on the management server, which has the vastai tool configured with the appropriate API key. This architectural decision keeps the API credentials on the management host and allows the assistant to work from any location.

Conclusion

Message 1018 is a turning point in the deployment narrative. It is the moment when accumulated debugging knowledge crystallizes into concrete action—two instances provisioned with a hardened stack, ready to validate the fixes and reveal the next set of challenges. The message is terse, almost anticlimactic after the hours of debugging that preceded it, but that terseness is itself a signal: the deployment pipeline is now reliable enough that creating instances is a routine operation. The hard problems have shifted from "will it boot?" to "will it perform?"—a sign of progress in any engineering project.