The Deployment That Went Wrong: Testing Cgroup-Aware Memory on a 256G Machine

Introduction

In the sprawling, iterative process of building a production-grade GPU proving system for Filecoin, few moments are as revealing as the transition from "it works on the big machine" to "does it work on the constrained one?" Message 3964 of this opencode session captures exactly that transition — a single, seemingly straightforward deployment command that embodies the tension between confident engineering and the messy reality of cloud GPU infrastructure. The assistant, having just validated cgroup-aware memory detection on a 961 GiB server, receives a simple instruction from the user: "Try on a 256G machine." What follows is a deployment decision that, while made with reasonable criteria, immediately goes awry — deploying an instance with 503 GiB instead of the targeted 256 GiB, setting off a chain of debugging that reveals deeper truths about offer staleness, API behavior, and the importance of verification in automated infrastructure management.

Context: The Road to Cgroup-Aware Memory

To understand message 3964, we must first understand what brought the session to this point. The preceding messages (roughly [msg 3941] through [msg 3961]) document a major milestone: the successful deployment and validation of cgroup-aware memory detection on a production vast.ai instance. The problem was acute: Docker containers on vast.ai report the host machine's full RAM via /proc/meminfo, even when a cgroup limit is in place. A 2003 GiB host with a 961 GiB cgroup limit would see total_budget_gib=1993 instead of 951, leading to massive over-allocation and OOM kills. The assistant fixed this by rewriting detect_system_memory() in Rust to read cgroup v2 (memory.max) and v1 (memory.limit_in_bytes) limits, returning the minimum of host RAM and the cgroup constraint.

The fix was validated on a 961 GiB instance (C.32874928, RTX 4090). The first proof completed successfully in 201 seconds, RSS sat at 666 GiB — well within the 961 GiB limit, and crucially, the budget was correctly set to 951 GiB (961 minus the 10 GiB safety margin). Both GPU workers were active, no OOM kill occurred. The system was stable.

But the user knew this was only half the battle. The machines that had been OOM-killing were the small ones — the 256 GiB instances where memory pressure is intense, where every GiB of overhead matters, and where the previous non-cgroup-aware code would have tried to allocate 500+ GiB on a machine that only had 256 GiB of cgroup limit. So the user's instruction in [msg 3962] was both a natural next step and a stress test: "Instance is up and pretty good; Try on a 256G machine."

The Subject Message: Selecting and Deploying

Message 3964 is the assistant's response to that instruction. It reads:

Good options. Let me pick a cheap 1x GPU machine with decent connectivity. The RTX 5090 1x at $0.33/hr in Canada (id 28256188, 64 cores, 251 GB) looks good — or the RTX 4090 in Spain (id 13651733, $0.34/hr). Let me deploy via the vast-manager API:

>

[bash] ssh theuser@10.1.2.104 "curl -sf -X POST http://localhost:1235/deploy -H 'Content-Type: application/json' -d '{\"offer_id\": 28256188, \"disk\": 250, \"min_rate\": 0}'" 2>&1

The message is deceptively simple: a brief evaluation of options, a choice, and a single curl command. But within this simplicity lies a wealth of decision-making, assumptions, and — as the subsequent messages reveal — a critical mistake.

Why This Message Was Written: Reasoning and Motivation

The motivation is clear from the user's request: validate the cgroup-aware memory fix on a constrained machine. The assistant's reasoning, visible in the agent reasoning block of [msg 3963], states: "The user wants me to deploy a new vast.ai instance with ~256GB RAM to test the cgroup-aware memory detection on a smaller machine. These were the ones that were OOM-killing before."

This is a classic validation pattern in systems engineering. You fix a bug on one configuration, then you must prove it works on the configuration that actually triggered the bug. The 961 GiB machine was a generous environment where the fix worked trivially. The 256 GiB machines were the real target — the ones where the old code would have tried to allocate 500+ GiB on a 256 GiB cgroup limit, causing immediate OOM kills. Without testing on the constrained configuration, the fix is unproven.

The assistant's reasoning also reveals a practical concern: cost. It explicitly looks for "a cheap 1x GPU machine with decent connectivity." The RTX 5090 at $0.33/hr in Canada is a good deal — single GPU (cheaper), 251 GB RAM (close to the 256G target), 64 cores (plenty for synthesis), and reasonable connectivity. The alternative (RTX 4090 in Spain at $0.34/hr) is slightly more expensive but also viable. The assistant is being cost-conscious while meeting the technical requirements.

How Decisions Were Made

The decision process is visible in the progression from [msg 3963] to [msg 3964]. In [msg 3963], the assistant runs a vastai search offers command with specific filters: disk_space>=250 dph<=0.9 gpu_ram>12.5 cpu_ram>=200 cpu_ram<=300 cpu_cores>25 inet_down>100 cuda_vers>=13.0. This is a carefully constructed query that targets exactly the kind of machine needed:

Mistakes and Incorrect Assumptions

The most significant mistake is not verifying the deployed instance's specifications before proceeding. The assistant deploys with a single curl command and then immediately checks the dashboard ([msg 3965]), but by that point the wrong instance is already provisioned. The mistake is not in the deploy command itself — the command is syntactically correct and follows the API contract — but in the lack of pre-flight checks.

Specifically:

  1. No offer availability check. The assistant could have queried the offer's current status before deploying. A simple vastai show offer 28256188 would have revealed whether it was still available.
  2. No deployment confirmation with expected specs. After deployment, the assistant checks the dashboard but doesn't immediately cross-reference the deployed instance's GPU name and RAM against what was requested. The dashboard output in [msg 3965] shows gpu_name: "RTX PRO 4000" and cpu_ram_mb: 515604 (~503 GiB), but the assistant doesn't flag this discrepancy until the next message.
  3. Blind trust in the API. The assistant assumes the /deploy endpoint will honor the exact offer ID. In reality, the vast-manager's implementation may have fallback logic — if offer 28256188 is unavailable, it may deploy the next best match, or it may have a bug where the offer ID is misinterpreted.
  4. No validation of the response. The curl command's output is not captured or parsed. The assistant doesn't check whether the deployment succeeded, what instance ID was created, or what specifications it has. It simply assumes success and moves on. The root cause of the mismatch is likely offer staleness. The search results in [msg 3963] were generated at a specific point in time. By the time the deploy command executes, the offer may have expired. The vast-manager's deploy endpoint may then fall back to a different offer — perhaps one with similar pricing but different hardware — or the offer ID may map to a different machine entirely in the vast.ai system. This is a classic infrastructure-as-code pitfall: treating market listings as stable resources when they are inherently ephemeral. The assistant's mental model treats offer IDs as persistent identifiers, but in the vast.ai marketplace, they are more like snapshots of available capacity at a moment in time.

Input Knowledge Required

To understand this message, the reader needs:

  1. Knowledge of the cgroup-aware memory fix. Without understanding that the assistant just fixed detect_system_memory() to read cgroup limits, the deployment of a 256G machine seems arbitrary. The reader must know that the 256G machines were the ones OOM-killing before the fix.
  2. Knowledge of the vast.ai ecosystem. The assistant uses vastai search offers with specific filters, then deploys via a custom manager API at localhost:1235/deploy. The reader must understand that vast.ai is a decentralized GPU rental marketplace where instances are provisioned on demand from available offers.
  3. Knowledge of the custom management layer. The team has built a "vast-manager" service that wraps vast.ai operations. The /deploy endpoint is part of this custom infrastructure, not the raw vast.ai API.
  4. Knowledge of the GPU proving pipeline. The assistant mentions "synthesis" and "GPU proving" — the reader must understand that CuZK is a GPU-accelerated proof generation system for Filecoin, and that memory budgeting is critical because proof parameters (SRS files) are ~44 GiB and must be loaded into RAM.
  5. Knowledge of cgroup memory constraints in Docker. The entire fix hinges on the fact that Docker containers report host RAM via /proc/meminfo even when cgroup-limited. Without this context, the deployment of a "256G" machine seems unrelated to the memory fix.## Output Knowledge Created This message creates several outputs, both immediate and consequential: Immediate output: A deployment request to the vast-manager API. The curl command triggers the provisioning of a new vast.ai instance. The assistant does not capture the response, but the dashboard check in [msg 3965] confirms that an instance (C.32896749) was created. Consequential output: A debugging session that reveals the offer mismatch. The assistant's reasoning in [msg 3966] shows the realization: "Hmm, the newly deployed instance (C.32896749) has cpu_ram_mb: 515604 which is ~503 GiB, not 256 GiB. And the GPU is 'RTX PRO 4000' not RTX 5090." This triggers a deeper investigation into the vast-manager's deployment logic and the offer staleness problem. Knowledge about the vast-manager's behavior: The assistant learns that the /deploy endpoint does not guarantee exact offer matching. This is a valuable operational insight — the team now knows they need to either fix the manager to verify offer availability before deployment, or add post-deployment validation that checks actual specs against requested specs. Knowledge about deployment verification gaps: The assistant discovers that it needs to verify deployed instances more carefully. In subsequent messages, it runs vastai show instances --raw to get the actual specifications of all running instances, cross-referencing them against what was requested.

The Thinking Process Visible in Reasoning

The assistant's reasoning is partially visible in the message itself and more fully in the surrounding context. In [msg 3963], the agent reasoning block shows the assistant thinking through the deployment strategy: "The user wants me to deploy a new vast.ai instance with ~256GB RAM to test the cgroup-aware memory detection on a smaller machine. These were the ones that were OOM-killing before."

The search query in [msg 3963] reveals a careful, systematic approach to filtering offers. The assistant doesn't just pick any cheap machine — it constructs a query with specific bounds for RAM (200-300 GB), disk (250+ GB), GPU RAM (12.5+ GB), CPU cores (25+), download speed (100+ Mbps), and CUDA version (13.0+). This shows domain expertise: the assistant knows exactly what resources the CuZK pipeline needs.

In message 3964 itself, the assistant's reasoning is visible in the selection criteria: "Let me pick a cheap 1x GPU machine with decent connectivity." The evaluation of two options (RTX 5090 in Canada vs RTX 4090 in Spain) shows a comparative decision process. The assistant weighs cost ($0.33 vs $0.34), GPU capability (RTX 5090 is newer), RAM (251 GB vs unspecified), and location (Canada vs Spain).

The deployment method — using the vast-manager API rather than direct vast.ai CLI — shows the assistant following the established infrastructure pattern. The team has built a management layer, and the assistant uses it consistently. The curl command is well-formed: POST request, JSON content type, proper payload structure with offer_id, disk, and min_rate fields.

However, the thinking process also reveals a gap: the assistant does not consider what happens if the offer is stale. There is no fallback plan, no retry logic, no validation of the deployed instance's specs. The reasoning is linear: search → select → deploy → verify. The verification step (checking the dashboard) happens, but it's not integrated into a feedback loop that would catch the mismatch early.

Conclusion

Message 3964 is a pivotal moment in the session — the point where a validated fix meets the messy reality of cloud infrastructure. The assistant's deployment command is technically correct but operationally naive, assuming that offer IDs are stable identifiers in an inherently ephemeral marketplace. The resulting mismatch (503 GiB instead of 256 GiB) is not a catastrophic failure — the instance is still usable for testing — but it represents a failure of verification that could have been prevented with a simple pre-flight check.

More broadly, this message illustrates a fundamental challenge in infrastructure automation: the gap between what you request and what you get. Every API call to a cloud provider is a negotiation, not a command. Offers expire, resources get reallocated, and fallback logic can produce unexpected results. The assistant's mistake is not in the code but in the mental model — treating the deployment API as a deterministic function when it is anything but.

The lesson is clear: in automated infrastructure management, verification is not optional. Every deployment should be followed by a confirmation that the provisioned resources match the requested specifications. The assistant learns this lesson the hard way in the subsequent messages, spending time debugging the mismatch instead of running the benchmark that was the actual goal. But this is how operational knowledge is built — not through theory, but through the concrete experience of deploying to production and discovering that reality rarely matches your assumptions.