> For clean Markdown content of this page, append .md to this URL. For the complete documentation index, see https://docs.nvidia.com/dynamo/llms.txt. For full content including API reference and SDK examples, see https://docs.nvidia.com/dynamo/llms-full.txt. # DynoSim Sweep Reference `aisimulate recommend --stack dynamo` searches AISimulate engine configuration plus Dynamo Router and Planner domains. Each selected result is a concrete prediction YAML. For a guided workflow, see [Sweep DynoSim Configurations](/dynamo/knowledge-base/concepts/simulation/simulation-sweeps). ## Command ```bash aisimulate recommend --stack dynamo --config recommendation.yaml ``` | Option | Default | Purpose | | ----------------------- | --------------------- | ---------------------------------------------------------------------- | | `-c`, `--config` | Required | Recommendation YAML | | `--stack` | `engine` | Set to `dynamo` to load the Dynamo runner and adapters | | `--set PATH=YAML_VALUE` | None | Override a configuration path after loading YAML; repeatable | | `--output-dir` | `./aisimulate-output` | Recommendation output directory | | `--overwrite` | `false` | Replace known AISimulate output files while preserving unrelated files | | `--format` | `table` | Standard-output format: `table` or `json` | ## Configuration Sections Recommendation accepts the prediction sections plus search controls: | Section | Required | Purpose | | -------------- | -------- | ----------------------------------------------------------------------- | | `traffic` | No | Workload source, load pattern, and stopping condition | | `engine` | Yes | Backend, topology, parallelism, scheduler, KV cache, and timing domains | | `router` | No | Dynamo routing domains | | `planner` | No | Dynamo Planner domains and presets | | `evaluation` | No | Service-level objective (SLO) thresholds | | `optimization` | Yes | Objective, hardware resolution, and candidate GPU constraints | | `optimizer` | No | Algorithm, trial budget, worker fan-out, timeout, and seed | Unknown fields are rejected. Conditional validation runs after each candidate is materialized. When `traffic` is omitted, recommendation uses the same default as prediction: 100 independent synthetic requests at concurrency 10, with 1,024 input tokens and 128 output tokens per request. ## Domain Syntax ### Choices Use `choices` for categorical or explicitly enumerated values: ```yaml engine: backend: {choices: [vllm, sglang]} router: policy: {choices: [round_robin, kv_router]} ``` ### Numeric Ranges Use `range` for numeric domains: ```yaml traffic: load: type: constant_rate requests_per_second: {range: {min: 4, max: 32, step: 4, scale: linear}} ``` `scale` is `linear` by default. Use `log` only for positive bounds. A stepped integer range includes its upper bound when the step lands on it. ### Presets A preset covers every knob in its smallest configuration group. * Omit `preset` or set it to `default` to use the built-in complete mappings. * Supply a list of names or complete mappings to search those mappings as categorical choices. * Set `preset: false` or `preset: {}` to search the group's independent knob domains. * Do not combine an active preset with an independent domain covered by that preset. Parallelism presets are special. The default invokes AISimulate's feasible parallel-configuration enumeration and projection. A custom list treats each complete parallelism mapping as one flat choice. With `preset: false`, each parallelism knob becomes an independent dimension before feasibility filtering. ## Traffic Domains Recommendation accepts `choices` or `range` on `traffic.load.concurrency`, request or session rate, `traffic.load.speedup` for trace timestamps, and `traffic.load.fraction` for KV-capacity-relative load. The `kv_capacity_fraction` load type is recommendation-only: it derives candidate concurrency from that candidate's estimated KV capacity. Fractions above 1 are valid and model oversubscribed load. For a swept load, use `stop.requests_per_load_unit` or `stop.sessions_per_load_unit` to scale the simulation length with each concrete candidate. Fixed `stop.requests` and `stop.sessions` keep the same simulation length across the load domain. ## Optimization `optimization.target` accepts: | Target | Ordering | | --------------------- | -------------------------------------------------------------------------------------------------------- | | `throughput` | Highest output throughput | | `throughput_per_gpu` | Highest output throughput per simulated GPU | | `throughput_per_user` | Highest per-user output throughput | | `goodput` | Highest SLO-compliant request throughput | | `goodput_per_gpu` | Highest goodput per simulated GPU | | `ttft` | Lowest Time to First Token (TTFT) | | `e2e_latency` | Lowest end-to-end latency | | `pareto` | Complete nondominated front over the public CLI's fixed pair: throughput per GPU and throughput per user | `goodput` and `goodput_per_gpu` require `evaluation.sla` with either `e2e_ms` or both `ttft_ms` and `itl_ms`. The public recommendation schema does not expose `pareto_objectives`. Custom objective lists remain available only through the legacy Sweeper Python SDK. `optimization.constraints.min_candidate_gpus` and `max_candidate_gpus` bound the simulated GPU footprint. When `engine.hardware: auto`, set one concrete `optimization.hardware` value. Recommendation searches aggregated and disaggregated modes by default, and vLLM and SGLang backends by default. TensorRT-LLM can be selected explicitly for aggregated mode. The default `engine.context_length: max` resolves from the model's Hugging Face configuration. ## Optimizer Controls | Field | Default | Purpose | | ------------------------------------- | ---------: | ------------------------------------------------------------ | | `optimizer.algorithm` | `bayesian` | `bayesian` or `random` search | | `optimizer.max_trials` | `320` | Hard total suggestion budget across active topology branches | | `optimizer.parallelism` | `16` | Concurrent candidate workers | | `optimizer.candidate_timeout_seconds` | `600` | Wall-clock timeout per candidate | | `optimizer.seed` | `42` | Stable base seed for branch samplers | Failed, infeasible, unsupported, and cached suggestions consume the trial budget. Every active topology branch must receive at least one trial. ## Output Recommendation writes: ```text aisimulate-output/ └── recommendations/ ├── 0001.yaml ├── 0002.yaml └── ... ``` Each numbered YAML contains one concrete prediction configuration with no `optimization`, `optimizer`, preset, domain, or `auto` value. Run any result directly: ```bash aisimulate predict \ --stack dynamo \ --config aisimulate-output/recommendations/0001.yaml ``` Scalar objectives write all feasible candidates in deterministic best-to-worst order. Pareto writes the complete nondominated front; its deterministic file order is not a scalar ranking. ## Related Pages * [Run a DynoSim Simulation](/dynamo/knowledge-base/concepts/simulation/simulation-runs) * [DynoSim Replay CLI Reference](/dynamo/reference/components/dyno-sim-replay-cli-reference) * [DynoSim Architecture](/dynamo/knowledge-base/concepts/simulation/dyno-sim-architecture)