The Reconnaissance Before the Build: A Methodical Environment Probe for Prometheus and Grafana

In the middle of an intensive optimization campaign for DeepSeek-V4-Flash on 8× RTX PRO 6000 Blackwell GPUs, the assistant and user had just achieved a remarkable ~17× throughput breakthrough by discovering and fixing an indexer bottleneck that was computing scores over the full ~1M-token max context every decode step. With production deployment in progress and the user now asking for monitoring infrastructure, the assistant faced a seemingly mundane but critical task: installing Prometheus and Grafana from scratch on a bare Ubuntu 24.04 server with no Docker, no pre-existing monitoring stack, and a complex multi-service inference deployment already running. Message [msg 12735] captures the very first step of this effort — a reconnaissance probe that would determine the entire installation strategy.

The Trigger: A User Request for Visibility

The chain of events leading to this message began when the user asked a simple question: "do we have any metrics / prometheus / grafana that we can use to see how full kv-cache is?" (see [msg 12724]). The assistant had already enabled --enable-metrics on the prefill and decode servers, verified that KV-cache gauges like sglang:token_usage, sglang:kv_used_tokens, and sglang:max_total_num_tokens were live on port 30002, and even created a helper script (/root/kv_usage.sh) for real-time monitoring. But the user wanted more — a proper dashboard with persistent monitoring. When asked "Want the full Prometheus + Grafana stack?", the user answered with a decisive "Yes."

This set the stage for message [msg 12735]. The assistant had already laid out a structured plan in its todo list: check Docker availability, install and configure Prometheus to scrape the three metrics endpoints (decode on 30002, prefill on 30000, router on 29001), install Grafana bound to port 3000 with Prometheus as a datasource, and provision a KV-cache dashboard. But before any of that could happen, the assistant needed to understand the environment it was working with.

What the Probe Checks — and Why

The bash command in message [msg 12735] is a compact but thorough environment survey, executing four distinct checks in sequence:

Docker availability. The command docker info >/dev/null 2>&1 && echo "docker OK: $(docker --version)" || echo "docker NOT available" tests whether Docker is installed and the daemon is reachable. This is the most consequential check because it determines the entire installation approach. If Docker were available, the assistant could pull official images for Prometheus and Grafana, run them with --network host to reach the localhost metrics endpoints, and have a working stack in minutes with minimal configuration. The result — "docker NOT available" — forces a more laborious binary-based installation.

Architecture and operating system. uname -m confirms x86_64, and sourcing /etc/os-release reveals Ubuntu 24.04. These details matter because Prometheus and Grafana distribute precompiled binaries for specific platforms. Knowing the exact OS version and architecture ensures the assistant downloads the correct tarball. Ubuntu 24.04 is recent enough that standard Linux AMD64 binaries will work without compatibility issues.

Port availability. The command ss -tlnp | grep -E ":9090|:3000" || echo "free" checks whether ports 9090 (Prometheus default) and 3000 (Grafana default) are already occupied. This is a critical prerequisite — if another service were already listening on these ports, the assistant would need to choose alternative ports or resolve conflicts. The output reveals a subtle issue that deserves closer examination.

Outbound network connectivity. curl -s --max-time 8 -o /dev/null -w "%{http_code}" https://github.com tests whether the server can reach GitHub, which hosts the Prometheus and Grafana release tarballs. A return code of 200 confirms outbound internet access works, meaning the assistant can download binaries directly rather than requiring an alternative distribution method like SCP from another machine.

The Subtle Regex Flaw in the Port Check

One of the most interesting aspects of this message is a subtle bug in the port-checking logic. The grep pattern :9090|:3000 is intended to match lines containing either :9090 or :3000 in the ss output. However, the regex :3000 is a substring match — it will match any port number that contains the digits 3000, including 30000, 30001, and 30002. The output confirms this: the grep returned lines for ports 30001, 30002, and 30000, all of which are sglang services already running. The "free" fallback (|| echo "free") never triggered because grep found matches — just not the right ones.

This is a classic "false positive" scenario in shell scripting. The assistant likely intended to check whether ports 9090 and 3000 were specifically in use, but the loose regex captured other ports instead. In practice, this doesn't cause a critical failure because the assistant can visually inspect the output and see that neither :9090 nor :3000 appears as an exact match. But it means the script's logic doesn't reliably distinguish between "ports are free" and "ports are occupied by other services with similar numbers." A more precise check would use awk to extract the port number and compare it exactly, or use a pattern like :9090\b or :3000\b with word boundaries.

This kind of subtle imprecision is common in ad-hoc shell reconnaissance, especially when working under time pressure in a complex deployment. It's a reminder that even experienced practitioners make small mistakes, and that the robustness of infrastructure automation depends on getting these details right.

How the Results Shape the Installation Strategy

The four checks collectively paint a clear picture of the environment:

No Docker → The assistant must install Prometheus and Grafana as native binaries, creating systemd service units for each to ensure they persist across reboots. This is more work but also more transparent — the configuration files are directly accessible, and there's no container networking complexity.
Ubuntu 24.04 x86_64 → The standard linux-amd64 tarballs from the Prometheus and Grafana release pages will work. No special compilation or compatibility layers needed.
Ports 9090 and 3000 are effectively free (despite the grep false positive) → The assistant can use the default ports without modification. This simplifies configuration because Prometheus's default web.listen-address and Grafana's default http_port can be used as-is.
Outbound internet works → The assistant can download the tarballs directly using wget or curl, avoiding the need to transfer files from another machine. This is especially important for Grafana, whose tarball is several hundred megabytes. The absence of Docker is the most significant finding. It means the assistant will need to handle binary downloads, extraction to /opt, configuration file creation, systemd unit file creation, and service lifecycle management — all manually. This is more error-prone than Docker but also more educational, as it exposes the full configuration surface of both tools.

The Broader Context: A Deployment in Motion

This message sits within a much larger narrative. The assistant had just completed a grueling kernel optimization campaign that transformed DeepSeek-V4-Flash's throughput from ~30 tok/s to over 500 tok/s on Blackwell GPUs. The production deployment included a prefill-decode disaggregation architecture spread across 8 GPUs with systemd services, a router on port 30001, and KV cache capacity of 2.58 million tokens. The monitoring stack was the final piece of production readiness — without it, the team would be operating blind, unable to track KV cache pressure, request latencies, or system health over time.

The methodical approach in this message — checking prerequisites before installing — reflects the assistant's engineering discipline. Rather than blindly downloading and installing, it first understands the environment, ensuring that the installation plan is grounded in reality. This is the same discipline that led to the earlier breakthrough: profile first, then optimize. Here, the assistant is profiling the deployment environment before adding the monitoring layer.

Conclusion: The Quiet Importance of Reconnaissance

Message [msg 12735] is not flashy. It doesn't contain a breakthrough kernel or a dramatic performance chart. But it represents a critical engineering practice: understanding your environment before modifying it. The assistant could have jumped straight to downloading Prometheus and Grafana, only to discover that Docker wasn't available halfway through, or that a port conflict required reconfiguration, or that the server couldn't reach the internet. By spending 25 seconds on this probe, the assistant saved itself from those surprises and laid the groundwork for a smooth installation.

The subtle regex mistake in the port check is a reminder that even careful reconnaissance can have blind spots. But the overall approach — structured checks, clear output, and a plan informed by results — is a model for how to approach infrastructure tasks in complex production environments. The next messages in the conversation would build on this foundation, downloading binaries, creating configuration files, and ultimately provisioning a 17-panel Grafana dashboard that gave the user full visibility into the KV cache and inference pipeline.