# Configure Aiperf Benchmark > Selects and freezes a question-driven AIPerf workload, objective, load policy, and Kubernetes execution manifest for a successfully deployed Dynamo candidate. Use when a candidate needs performance characterization or a comparable measurement against a reference. - Skill: `ai-dynamo-dynamo/configure-aiperf-benchmark` (Agent Skill) - Install (CLI): `npx skillmds@latest add ai-dynamo-dynamo/configure-aiperf-benchmark` - Raw SKILL.md: https://api.skillmd.com/api/skills/ai-dynamo-dynamo/configure-aiperf-benchmark/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - License: Apache-2.0 - Author: ai-dynamo (https://skillmd.com/u/ai-dynamo-dynamo) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/ai-dynamo-dynamo/configure-aiperf-benchmark --- # Configure AIPerf Benchmark Create a reproducible benchmark that answers the current performance question without changing the deployed candidate. Freeze semantics only for runs used in the same direct comparison. Read `agent-docs/rules/execution/user-workload.md`, `agent-docs/rules/benchmarking/benchmark-isolation.md`, `agent-docs/rules/benchmarking/comparison-uncertainty.md`, `agent-docs/rules/benchmarking/concurrency-grid.md`, `agent-docs/rules/benchmarking/evidence-eligibility.md`, `agent-docs/rules/benchmarking/proxy-workload-selection.md`, `agent-docs/rules/benchmarking/series-boundaries.md`, and `agent-docs/rules/benchmarking/tool-version.md` before selecting flags. Also read the user workload, deployment ledger, and the AIPerf documentation matching the pinned source or runtime. Inspect matching Dynamo recipe `perf.yaml` files when available. ## Inputs Require: - exact user-workload path and SHA256; - current `DEPLOY_ROOT`, applied DGD, deployment ledger, and successful smoke test; - the baseline-characterization goal or approved candidate's performance question and target operating region; and - an exact existing benchmark-plan path, SHA256, and series ID only when considering series reuse. ## Workflow 1. Require a successful smoke test and resolve the in-cluster frontend endpoint, served model, tokenizer, Kubernetes context, namespace, artifact collection path, and GPU count. 2. Decide whether the run is an absolute characterization or a direct comparison. For a comparison, identify the candidate and reference measurements needed to answer the question. 3. Reuse an existing series only when its workload and measurement semantics still answer the question. Otherwise assign a new series ID and require any comparison reference to be measured under the new plan. 4. Select workload input in this order: - user-provided Mooncake trace; - exact user-provided ISL/OSL and traffic controls; - closest Dynamo recipe trace as a `recipe_proxy`. 5. Validate a selected trace as JSONL. Record its path, SHA256, row count, timestamp range, ISL/OSL distribution, prefix/hash information, and any rows outside the served context limit. Do not silently filter or clip rows. 6. Choose the experiment that best exposes the behavior under investigation: - `trace_fidelity`: preserve timestamps and fixed-schedule behavior; - `static_shape`: preserve the exact synthetic ISL/OSL target; - `capacity`: vary concurrency or request rate over a bounded range. 7. Select the objective and metrics needed for the question: - with target SLOs, use goodput/good-request fraction and the stated attainment constraints; - without sufficient SLOs, preserve a throughput/latency/error Pareto view. 8. Configure one measured run by default. Set the measurement duration to 30 minutes or less. For request-count or fixed-schedule workloads, choose or validate a count or schedule that is expected to complete within that limit and set a bounded execution timeout. Stop rather than silently truncate or weaken a workload that cannot fit. 9. Do not enable repetitions only to obtain confidence intervals. Configure more than one run only when prior analysis documents why another run is necessary to resolve a consequential decision and why that value justifies the GPU cost. Exception: the once-per-series noise-floor pilot (n=3) required by `agent-docs/rules/benchmarking/comparison-uncertainty.md` is pre-authorized; it arrives as a `repeat_decision: necessary` from `analyze-aiperf-results` whose rationale names the series pilot, and runs as repetitions of one unchanged configuration. Plans stay immutable; no plan marker is involved. 10. Pin the AIPerf runtime version or source commit per `agent-docs/rules/benchmarking/tool-version.md`: resolve the latest stable release (or apply that rule's reference-reproduction/operator-override exceptions) and record the resolution mechanism, its output, and the date in the plan. Write the immutable series plan and the run-scoped `/benchmark/aiperf-config.yaml` and `/benchmark/perf.yaml`. ## Benchmark Plan For a new series, write: ```text /inputs/benchmark-plans/.json ``` Use a filesystem-safe series ID. Never overwrite a plan. When reusing a series, use its exact existing path and SHA256 and change only endpoint address, Job identity, namespace, or artifact wiring in the run-scoped files. The plan must identify: - performance question, target operating region, and characterization or comparison intent; - plan ID, benchmark-series ID, and any required reference candidates; - user-workload path and SHA256; - workload source: `user_trace`, `user_static`, or `recipe_proxy`; - trace path/hash or exact synthetic distribution; - fixed-schedule, concurrency, request-rate, request-count/duration, warmup, a per-run measurement limit of no more than 30 minutes, repetitions with a default of one, any approved repeat rationale, confidence policy and level when needed, and seed; - endpoint type, streaming behavior, model, and tokenizer; - target metrics, SLOs, goodput thresholds, and optimization direction; - AIPerf source commit when available and required runtime version, plus the version-resolution mechanism, its output, and the resolution date (`agent-docs/rules/benchmarking/tool-version.md`); - proxy rationale and limitations when applicable. ## Manifest Rules - `perf.yaml` is a Kubernetes Job, not the AIPerf-native config. - Run AIPerf inside the cluster and use the target context and namespace. - Prefer an existing compatible recipe Job as a starting point. Modify only the run-scoped copy. - Enforce the 30-minute measurement limit with AIPerf workload controls. Do not rely only on a Kubernetes Job timeout. - Ensure raw artifacts remain available after Job completion, either on a PVC or in the completed pod until copied. - Read referenced secret names from existing manifests; never copy secret values into benchmark files. - Do not embed a local host path that is unavailable to the benchmark pod. Stop with a concrete configuration blocker when the performance question is missing, no defensible workload can be selected, a required comparison cannot be made, or the planned run cannot fit within 30 minutes without changing its semantics.