/bionemo-complexa-sweep

name: complexa-sweep
description: Agent runbook for Proteina-Complexa parameter sweeps through the separately installed first-party CLI and config generator. Use for cartesian-product hyperparameter scans, sweeper YAML authoring, configuration comparisons, ablations, tuning, success-rate ranking, or Pareto searches over quality and wall-clock cost. Do not use for a single existing-job launch or SLURM submission template. SKILL.md performs the orchestration; bundled executables only inspect local readiness and write a run manifest.
allowed-tools: Bash, Read, Write, Env, AskUserQuestion
metadata:
  author: Ohad Mosafi <omosafi@nvidia.com>

complexa-sweep

Run cartesian-product parameter sweeps over Proteina-Complexa design pipelines. Pick or author a sweeper YAML in `configs/sweeps/`, expand it to N inference configs with `script_utils/generate_inference_configs.py`, loop `complexa design` over those configs, then aggregate per-config success metrics into a ranked summary CSV plus a manifest.

> **Important:** The `complexa design` CLI does **NOT** accept `--sweeper` directly. Sweeps are driven by `script_utils/generate_inference_configs.py`, which writes one `configs/inference_configs/inf_{idx}_{run_name}.yaml` per sweep combination. You then loop `complexa design` over those generated configs.

Instructions

Sweeper authoring, Cartesian enumeration, cost estimates, and analysis of supplied CSVs need only the relevant configs and data. Complete those artifacts without setting up inference. Read the relevant axis section in [sweep_axes.md](references/sweep_axes.md), then check the matching keys in the selected source config for current defaults. Summary CSVs with supplied counts and objective columns do not require the full inference or metric guides. For a partial snapshot, enumerate combinations locally and provide the future generator command; running that command requires the full project and its dependencies.

For a requested launch, check readiness and the budget below. A single existing job or a SLURM template does not require a parameter sweep.

Step 1: Pre-flight

Before GPU execution, or when the request conditions its workflow on hardware, set `SKILL_DIR` to the directory containing this manifest and run preflight once. Use a separate shell assignment; an inline environment assignment cannot set `$SKILL_DIR`'s expansion in the same command. Reuse an existing probe from the same request if the environment is unchanged:

bash "$SKILL_DIR"/scripts/preflight.sh

Read `./complexa_setup/preflight.json`. A sweep multiplies GPU time by the number of configs and targets. Compute the budget before launch and obtain confirmation if the user has not already authorized that scope and cost:

> "This sweep produces N configs × ~M minutes per config ≈ TOTAL GPU-hours. OK to proceed? (y / reduce / cancel)"

If `gpu.available=false`, stop before GPU execution. Sweeper authoring, Cartesian-product enumeration, cost estimates, and analysis of supplied CSVs can run on CPU. For those requests, finish the requested artifacts and record the launch prerequisites. Label synthetic input data as synthetic and keep estimated GPU time separate from measured runtime.

For a hardware-conditional request, verify the allocated hardware before selecting the CPU fallback. Missing weights, dependencies, or sufficient VRAM for an allocated GPU are launch blockers, not evidence of a CPU-only environment. Report probe or execution failures without reclassifying the selected branch. The shared preflight's `gpu.available=false` also covers failed or missing `nvidia-smi`; check device/allocation evidence before treating it as GPU absence. In containers, `/proc/driver/nvidia/gpus` and loaded kernel modules may describe the host, not this task's allocation. Check container device exposure and any available scheduler allocation information together. A CPU allocation can share a GPU host; an allocated GPU with inaccessible devices is blocked. If allocation remains unclear, report it as undetermined.

Step 2: Pick the pipeline + target

Use the user's supplied pipeline, targets, and run name. Protein, ligand, and AME runs use `search_binder_local_pipeline`, `search_ligand_binder_local_pipeline`, and `search_ame_local_pipeline`, respectively. Ask only for missing values:

• `pipeline_config_name` — e.g. `search_binder_local_pipeline` (default), `search_ligand_binder_local_pipeline`, `search_ame_local_pipeline`.
• `task_name` — e.g. `02_PDL1`, `22_DerF21`, `39_7V11_LIGAND`. Passed as `--override generation.task_name=<task>`.
• `run_name` — short tag for output dir naming.

Examples

Step 3: Pick or author the sweeper YAML

Sweeper YAMLs live in `configs/sweeps/`. Each key is a dot-notation Hydra path; each value is a list. The cartesian product becomes N configs.

Canned sweepers

| File | Axis | Values | Configs | |---|---|---|---| | `configs/sweeps/beam_width.yaml` | `generation.search.beam_search.beam_width` | 1, 2, 4, 8 | 4 | | `configs/sweeps/bb_ca_temperature.yaml` | `generation.model.bb_ca.simulation_step_params.sc_scale_noise` | 0.1, 0.4 | 2 | | `configs/sweeps/search_replicas.yaml` | `generation.search.best_of_n.replicas` | 1, 4, 16, 64 | 4 | | `configs/sweeps/example.yaml` | beam_width × nsteps | (2,4) × (200,400) | 4 |

Use a matching sweeper, then check that its axes affect the selected algorithm. The binder config defaults to `best-of-n`: a beam-width sweep must also pin `generation.search.algorithm: [beam-search]`. A replicas sweep pins `generation.search.algorithm: [best-of-n]`. For AME, reward-guided search also requires a configured reward model; its default is `null`.

Authoring a new sweeper

Minimal multi-axis example (saved to `configs/sweeps/my_sweep.yaml`):

# 3 beam widths × 2 nsteps = 6 configs
generation.search.algorithm: [beam-search]

generation.search.beam_search.beam_width:
  - 2
  - 4
  - 8

generation.args.nsteps:
  - 200
  - 400

Rules