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, 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