The Reconnaissance SSH: Probing the Deployment Target for a Budget-Based Memory Manager

Introduction

In any production deployment, the gap between a theoretically correct implementation and a system that actually works under real-world constraints is bridged by reconnaissance. Message 2298 in this opencode session captures a pivotal moment of such reconnaissance: the assistant, having just committed a comprehensive unified budget-based memory manager for the cuzk GPU proving engine, pauses to inspect the remote deployment target before shipping the binary. This message, consisting of two SSH commands and their output, is deceptively simple. On its surface, it is a routine system check. But beneath that surface lies a dense layer of engineering reasoning, strategic decision-making, and the kind of contextual awareness that separates a fragile deployment from a robust one.

The message reads in full:

[assistant] Now let me check the remote machine and see how cuzk is built/deployed there: [bash] ssh -p 40612 -o StrictHostKeyChecking=no -o ConnectTimeout=10 root@141.0.85.211 'free -h; echo "==="; uname -a; echo "==="; nvidia-smi --query-gpu=index,name,memory.total --format=csv,noheader 2>/dev/null; echo "==="; nproc' total used free shared buff/cache available Mem: 755Gi 227Gi 515Gi 87Gi 105Gi 528Gi Swap: 8.0Gi 105Mi 7.9Gi === Linux 054692087abe 5.15.0-170-generic #180-Ubuntu SMP Fri Jan 9 16:10:31 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux === 0, NVIDIA GeForce RTX 5090, 32607 MiB === 64

>

[bash] ssh -p 40612 -o StrictHostKeyChecking=no -o ConnectTimeout=10 root@141.0.85.211 'systemctl status cuzk 2>/dev/null || true; echo "==="; ps aux | grep cuzk | grep -v grep; echo "==="; ls -la /usr/local/bin/cuzk 2>/dev/null || ls -la /usr/bin/cuzk 2>/dev/null || which cuzk-daemon 2>/dev/null || find / -name "cuzk-daemon" -type f 2>/dev/null | head -5' === === -rwxr-xr-x 1 root root 27901424 Mar 13 11:07 /usr/local/bin/cuzk -rwxr-xr-x 1 root root 5765768 Mar 13 11:07 /usr/local/bin/cuzk-bench

This is the entirety of the subject message. To the uninitiated, it looks like a sysadmin running a few checks. To the engineer who has just rewritten the memory management subsystem of a GPU proving engine, every byte of this output is a critical data point that will shape the next several hours of work.

Why This Message Was Written: The Strategic Context

The assistant had just completed a major architectural overhaul of cuzk's memory management. The old system used a static partition_workers semaphore — a crude concurrency limiter that assumed all memory consumers were equal and that a fixed number of concurrent partitions would always fit within available RAM. This was fragile. It did not account for the varying memory footprints of different proof types (PoRep, WindowPoSt, WinningPoSt, SnapDeals), nor did it track the SRS (Structured Reference String) pinned GPU memory, the PCE (Pre-Compiled Constraint Evaluator) heap allocations, or the synthesis working sets. The new system replaced all of this with a unified MemoryBudget that tracks every major consumer under a single byte-level budget, auto-detected from system RAM.

The user's request was clear: "Commit, test on ssh -p 40612 root@141.0.85.211 which is running cuiro and cuzk, curio is cordoned; upload new cuzk (built in docker) and test if memory constraints work correctly." The assistant had already committed the changes. Now it needed to understand the target environment before deploying.

This reconnaissance message was written because deploying a memory manager blind is reckless. The assistant needed answers to several concrete questions before it could proceed:

What is the total system memory? The memory budget system has an auto mode that detects system RAM and sets the budget accordingly. But the user mentioned "~500GB memory," which was a rough estimate. The actual value would determine whether the budget auto-detection would work correctly or whether an explicit cap was needed.

How much memory is currently in use? The machine runs Curio (a Filecoin storage provider system) alongside other processes. The memory manager's safety_margin configuration exists precisely to account for co-located processes. Knowing the baseline memory consumption (227 GiB used out of 755 GiB) would inform how large the safety margin needed to be.

What GPU is available? The cuzk proving engine is GPU-accelerated. Different GPUs have different VRAM capacities, which affects how many proofs can be processed concurrently. The RTX 5090 with 32 GiB VRAM is a powerful card, but its memory capacity imposes hard constraints on batch sizes.

How many CPU cores are available? Synthesis is CPU-bound in many phases. The 64 cores available would influence how many partition threads could run concurrently before CPU contention became a bottleneck.

Is cuzk currently running? Deploying a new binary while the old one is running requires a restart strategy. If cuzk is managed by systemd, the deployment process differs from a manual restart. If cuzk is not running at all, the deployment is simpler but requires ensuring the service starts correctly.

Where is the existing binary located? The assistant needs to know the path to replace it. The fallback chain in the second SSH command (/usr/local/bin/cuzk*, /usr/bin/cuzk*, which cuzk-daemon, find / -name "cuzk-daemon") shows the assistant considering multiple possible naming conventions and locations.

How was cuzk built? The existing binary is 27 MB and dated March 13. The assistant needs to know whether to build a Docker container with CUDA dependencies or whether a static binary would suffice. The kernel version (5.15.0-170-generic) and the fact that this is an Ubuntu system inform the Docker build configuration.

How Decisions Were Made

This message is primarily about information gathering rather than decision execution, but several design choices are visible in how the assistant structured the reconnaissance.

The two-command split is deliberate. The first SSH command gathers static hardware information: memory, kernel, GPU, CPU count. These are facts that do not change between runs. The second SSH command gathers dynamic state: whether cuzk is running, where the binaries are, whether a systemd service exists. This separation reflects a mental model of the deployment: hardware facts inform the memory budget configuration, while process state informs the deployment strategy.

The use of fallback chains reveals engineering judgment. The binary location check uses a cascading series of commands: ls -la /usr/local/bin/cuzk* first (the most common location for manually installed binaries), then /usr/bin/cuzk* (the standard system binary path), then which cuzk-daemon (checking if the binary is named with the "-daemon" suffix common in Go projects), and finally a recursive find as a last resort. This chain shows that the assistant anticipated multiple possible naming and location conventions and built robustness into the check.

The 2>/dev/null || true pattern is a defensive coding choice. Commands like systemctl status cuzk will fail with a non-zero exit code if the service does not exist. By piping errors to /dev/null and using || true, the assistant ensures that a missing systemd service does not cause the entire SSH command chain to abort. This is particularly important because the SSH session executes a single quoted string — if any command in the chain fails, the entire session could terminate prematurely.

The echo "===" separators are a parsing aid. When multiple commands are chained in a single SSH call, their outputs blend together. The === markers provide clear delimiters that allow the assistant to programmatically or visually separate the results of each command. This is a small but meaningful design choice that reflects an awareness of how the output will be consumed.

Assumptions Embedded in the Reconnaissance

Every reconnaissance mission carries assumptions, and this message is no exception. Some of these assumptions are explicit in the commands; others are implicit in what the assistant chose to check (and not check).

Assumption: The machine is accessible via SSH on port 40612. The user specified this port explicitly, and the assistant trusts that specification. The ConnectTimeout=10 and StrictHostKeyChecking=no options suggest the assistant anticipates potential network delays and is willing to accept unknown host keys for automation purposes.

Assumption: root access is available and sufficient. The assistant connects as root, which implies that either passwordless sudo is configured or the root account has direct SSH access. This is a strong assumption about the security posture of the remote machine.

Assumption: cuzk might be managed as a systemd service. The systemctl status cuzk command tests this hypothesis. The empty output (no error, no status) suggests that either the service does not exist or systemd is not the init system. This assumption was disproven by the result, which is itself valuable information.

Assumption: The binary might be named "cuzk-daemon" rather than "cuzk." The fallback chain includes which cuzk-daemon and find / -name "cuzk-daemon", suggesting the assistant expected a "-daemon" suffix based on common naming patterns in similar projects. In fact, the binary is simply named "cuzk," so this assumption was incorrect but harmless — the fallback chain handled it gracefully.

Assumption: The existing binary is the one that needs replacement. The assistant finds /usr/local/bin/cuzk at 27 MB and assumes this is the production binary. This is a reasonable assumption given the path and size, but it is worth noting that the assistant does not verify the binary's provenance (e.g., by checking a checksum or build timestamp against the source repository).

Assumption: The machine has approximately 500 GB of memory as stated by the user. The user said "~500GB memory," but the actual total is 755 GiB with 528 GiB available. This discrepancy matters: if the assistant had configured the memory budget based on the user's estimate of 500 GB, it would have left significant headroom unused. The reconnaissance corrects this assumption with hard data.

Mistakes and Incorrect Assumptions

While the reconnaissance is thorough, it is not without flaws. Several incorrect assumptions and potential blind spots deserve examination.

The most significant incorrect assumption is that cuzk is currently running. The user stated "which is running cuiro and cuzk," but the ps aux | grep cuzk command returns no output. Either cuzk was not running at the moment of the check, or the process name does not match the "cuzk" pattern. This is a critical finding: if cuzk is not running, the deployment strategy changes from "hot-swap and restart" to "start from scratch." The assistant does not comment on this discrepancy in the message, but it is a data point that will influence subsequent decisions.

The assumption that systemd manages cuzk is incorrect. The systemctl status cuzk command produces no output (not even an error message like "Unit cuzk.service could not be found"), which is unusual. A missing systemd unit typically produces a clear error message on stderr, but the assistant redirects stderr to /dev/null. This means the assistant cannot distinguish between "the service does not exist" and "systemctl is not available on this machine." The || true ensures the command does not fail, but it also masks the distinction.

The assistant does not check for Docker availability. The user explicitly asked to build cuzk in Docker, but the reconnaissance does not verify whether Docker is installed on the remote machine or on the build machine. This information will be needed in subsequent steps.

The assistant does not check the Curio version or configuration. The user mentioned that Curio is running and cordoned, but the reconnaissance does not inspect Curio's state beyond the memory footprint visible in free -h. Understanding Curio's memory usage pattern (steady vs. bursty, pinned vs. swappable) would inform the safety margin calculation.

The GPU check only reports total VRAM, not current utilization. The RTX 5090 has 32 GiB of VRAM, but nvidia-smi without the --query-gpu=memory.used attribute does not report how much is currently in use. This information would be relevant for understanding whether the GPU is available for proving work or whether other processes (e.g., Curio's GPU tasks) are consuming VRAM.

Input Knowledge Required

To fully understand this message, a reader needs familiarity with several domains:

The cuzk project architecture. The reader must understand that cuzk is a GPU-accelerated proving engine for Filecoin proofs, that it uses SRS (Structured Reference Strings) pinned in GPU memory, PCE (Pre-Compiled Constraint Evaluators) stored in host memory, and that synthesis working sets are temporary allocations that need to be managed carefully to avoid OOM conditions.

The memory manager design. The reader must know that the newly implemented system uses a MemoryBudget with auto detection, a safety_margin for co-located processes, and an LRU eviction policy for SRS and PCE caches. The reconnaissance directly feeds into configuring these parameters.

Linux system administration. The commands used (free -h, uname -a, nvidia-smi, nproc, systemctl, ps aux, ls, which, find) are standard Linux administration tools. Understanding their output and limitations is essential.

SSH and remote execution patterns. The use of -o StrictHostKeyChecking=no, -o ConnectTimeout=10, and the chaining of commands with echo "===" separators are patterns familiar to engineers who automate remote deployments.

GPU computing concepts. The RTX 5090 with 32 GiB VRAM is a high-end consumer GPU. Understanding that 32 GiB is large for a consumer card but small compared to server GPUs (like the A100 with 80 GiB) provides context for the memory constraints the proving engine operates under.

Filecoin proof types. The reader should know that different proof types (PoRep, WindowPoSt, WinningPoSt, SnapDeals) have different memory footprints and that the memory manager must accommodate all of them.

Output Knowledge Created

This message produces concrete, actionable knowledge about the deployment target:

Hardware inventory: The machine has 755 GiB of system RAM (528 GiB available), an NVIDIA RTX 5090 with 32 GiB VRAM, and 64 CPU cores running a 5.15 Linux kernel on Ubuntu. This is the foundational data for configuring the memory budget.

Memory pressure baseline: 227 GiB is already in use by Curio and other system processes. This means the safety margin must be at least this large, plus headroom for bursts. A naive auto budget that uses 100% of detected RAM would cause immediate OOM.

Binary deployment target: The existing cuzk binary is at /usr/local/bin/cuzk (27 MB, dated March 13). The cuzk-bench utility is at /usr/local/bin/cuzk-bench (5.7 MB). These paths inform the deployment script.

Service management gap: cuzk is not running and is not managed by systemd. This means the deployment will need to either create a systemd unit, use a different process manager, or start cuzk manually. This is a significant finding that will shape the deployment strategy.

GPU capability confirmed: The RTX 5090 is present and accessible via nvidia-smi. The proving engine's GPU acceleration path is viable on this machine.

Kernel compatibility: The 5.15 kernel is recent enough to support the CUDA drivers and Docker containers needed for the build.

The Thinking Process Visible in the Message

The assistant's reasoning is not explicitly stated in this message — there is no "Agent Reasoning" block as seen in other messages. Instead, the thinking process is encoded in the structure of the commands themselves.

The first SSH command reveals a prioritization: hardware facts first. The assistant asks for memory, kernel, GPU, and CPU count in a single call. These are the inputs to the memory budget calculation. The assistant is thinking: "Before I can deploy the memory manager, I need to know how much memory I'm working with, what GPU I have, and how many cores are available for parallelism."

The second SSH command reveals a process-oriented mindset: service status, process list, binary location. The assistant is thinking: "Now I need to understand how cuzk is deployed here. Is it a systemd service? Is it running? Where is the binary? What's it named?"

The fallback chain for finding the binary reveals probabilistic reasoning: "The binary is most likely in /usr/local/bin/. If not, it might be in /usr/bin/. If not, it might be named cuzk-daemon. If all else fails, I'll search the entire filesystem." This is a classic engineering pattern: try the most likely case first, then fall back to progressively more expensive or less likely alternatives.

The use of 2>/dev/null || true reveals a defensive mindset: "I don't want a single failed command to abort the entire SSH session. I'd rather get partial results than no results." This is particularly important in automation contexts where a partial result is more valuable than a silent failure.

The absence of certain checks also reveals the assistant's mental model. The assistant does not check:

Conclusion

Message 2298 is a textbook example of pre-deployment reconnaissance in a production engineering context. It is not glamorous — it is two SSH commands and their output — but it is the kind of work that separates successful deployments from catastrophic ones. The assistant gathers hardware specifications, checks process state, locates existing binaries, and identifies service management gaps. It does all of this with defensive coding patterns, fallback chains, and a clear mental model of what information is needed and why.

The output of this message will directly inform the next steps: building a Docker container with CUDA dependencies, uploading the binary to /usr/local/bin/cuzk, configuring the memory budget with a safety margin large enough to accommodate the 227 GiB baseline consumption, and starting cuzk on a machine that currently has no running cuzk process. Every subsequent decision in the deployment pipeline will trace back to the data gathered in these two SSH commands.

In the broader narrative of the opencode session, this message marks the transition from implementation to deployment. The memory manager has been designed, implemented, tested, and committed. Now it must survive contact with the real world. The reconnaissance in message 2298 is the first step in that journey — and as the subsequent chunks will show, the real world has surprises in store that no amount of unit testing could have predicted.