Validating Distributed LLM Serving Benchmarks with NVIDIA srt-slurm, SLURM Recipes, Parameter Sweeps, and Pareto Analysis

In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for distributed LLM serving. We set up the project in Google Colab, inspect its internal architecture, define a cluster configuration, dry-run built-in and custom recipes, and model a disaggregated prefill-and-decode deployment for DeepSeek-R1. We also generate parameter sweeps, interact with the typed Python API, validate expanded configurations, and analyze simulated benchmark results through a throughput-versus-latency Pareto frontier. Although Colab does not provide a real SLURM environment, we use it as a practical development workspace to understand, validate, and prepare production-grade benchmark recipes before we submit them to an actual GPU cluster. Copy CodeCopiedUse a different Browserimport os, sys, subprocess, textwrap, json, shutil, importlib from pathlib import Path def run(cmd, check=True, quiet=False): """Run a shell command, stream output.""" print(f"\n$ {cmd}") r = subprocess.run(cmd, shell=True, text=True, capture_output=True) out = (r.stdout or "") + (r.stderr or "") if not quiet: print(out[-6000:]) if check and r.returncode != 0: raise RuntimeError(f"Command failed ({r.returncode}): {cmd}") return out def section(title): print("\n" + "═"*78 + f"\n {title}\n" + "═"*78) section("1. Install srt-slurm") REPO = Path("/content/srt-slurm") if Path("/content").exists() else Path.cwd()/"srt-slurm" if not REPO.exists(): run(f"git clone --depth 1 https://github.com/NVIDIA/srt-slurm.git {REPO}", quiet=True) run(f"{sys.executable} -m pip install -q -e {REPO}", quiet=True) sys.path.insert(0, str(REPO / "src")) importlib.invalidate_caches() os.chdir(REPO) run("srtctl --help") We prepare the Colab environment by importing the required modules and defining reusable helper functions for command execution and section formatting. We clone the NVIDIA srt-slurm repository, install it in editable mode, and expose its source directory to the active Python runtime. We then switch to the repository directory and verify that the srtctl command-line interface is installed correctly. Copy CodeCopiedUse a different Browsersection("2. Repository architecture") print(textwrap.dedent(""" src/srtctl/ cli/ submit.py (apply/dry-run/preflight/monitor), do_sweep, interactive core/ schema.py (typed config), sweep.py, slurm.py (sbatch gen), validation.py, health.py, topology.py, fingerprint.py backends/ sglang.py, trtllm.py, vllm.py, mocker.py ← engine adapters frontends/ Dynamo / router frontends templates/ Jinja2 → sbatch + orchestrator scripts recipes/ ready-made benchmarks per platform (gb200-fp4, h100, b200-fp8, qwen3-32b, dsv4-pro, mocker smoke tests, ...) analysis/ srtlog (log parsers) + Streamlit dashboard (Pareto, latency...) docs/ sweeps.md, profiling.md, analyzing.md, config-reference.md """)) for d in ["recipes", "docs"]: print(f"{d}/ →", ", ".join(sorted(p.name for p in (REPO/d).iterdir()))[:300]) section("3. Cluster configuration (srtslurm.yaml)") (REPO/"srtslurm.yaml").write_text(textwrap.dedent("""\ cluster: "colab-demo" default_account: "demo-account" default_partition: "gpu" default_time_limit: "01:00:00" gpus_per_node: 4 use_gpus_per_node_directive: true use_segment_sbatch_directive: true containers: dynamo-sglang: "/containers/dynamo-sglang.sqsh" lmsysorg+sglang+v0.5.5.post2.sqsh: "/containers/sglang-v0.5.5.sqsh" model_paths: deepseek-r1: "/models/DeepSeek-R1" """)) print((REPO/"srtslurm.yaml").read_text()) We inspect the repository structure to understand how srtctl organizes its command-line tools, schemas, backends, templates, recipes, and analysis components. We then create a local srtslurm.yaml file containing simulated cluster defaults, container