# Complexa Sweep

> Use this skill whenever the user wants to run a parameter sweep over a Proteina-Complexa design pipeline — cartesian-product hyperparameter scans, Pareto search over generation/reward/evaluation knobs, or any "compare configurations" workflow. Trigger phrases include "sweep beam width", "sweep nsteps", "hyperparameter sweep", "parameter scan", "scan beam_width and temperature", "compare configurations", "find the best generation params", "what's the optimal nsteps", "Pareto search for binder quality vs wall-clock", "complexa sweep", "tune Complexa", "ablate the reward weights", "configs/sweeps", "--sweeper", "run beam_width.yaml". This is the only skill that owns sweeper YAML authoring, cartesian-product expansion, and per-config result ranking.

- Skill: `nvidia-bionemo/complexa-sweep` (Agent Skill, multi-file: 17 files)
- Install (CLI): `npx skillmds@latest add nvidia-bionemo/complexa-sweep`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nvidia-bionemo/complexa-sweep/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra, AI & ML
- Author: nvidia-bionemo (https://skillmd.com/u/nvidia-bionemo)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nvidia-bionemo/complexa-sweep

---


# 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.

## What this skill enables

- Pick an existing sweeper YAML from `configs/sweeps/` (`beam_width`, `bb_ca_temperature`, `search_replicas`, `example`).
- Author a new sweeper YAML with arbitrary dot-notation axes (cartesian product).
- Generate N inference + evaluation config pairs with `script_utils/generate_inference_configs.py`.
- Loop `complexa design` over the generated configs (one at a time on a single GPU, or in parallel on a multi-GPU host by sharding the config list across `CUDA_VISIBLE_DEVICES`).
- Walk per-config output directories and parse the analyze-step CSV from each.
- Emit `sweep_summary.csv` (one row per config: axis values + success rate + mean iPAE + diversity) and `sweep_manifest.json`.
- Identify the best config by success rate and the Pareto frontier (wall-clock vs success).

## Step 1: Pre-flight

```bash
bash .claude/skills/_shared/scripts/preflight.sh
```

Read `./complexa_setup/preflight.json`. A sweep multiplies GPU time by the number of configs. **Before launching, confirm the cost with the user**:

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

If `gpu.available=false`, stop — sweeps are not feasible on CPU.

## Step 2: Pick the pipeline + target

Use the same dialogue as `complexa-design` — do **not** duplicate it here. See [`.claude/skills/complexa-design/SKILL.md`](../complexa-design/SKILL.md) Step 2 ("Pick the pipeline") and Step 3 ("Gather parameters"). Capture:

- `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.

## 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 |

If one matches the user's intent, use it as-is. Otherwise author a new file.

### Authoring a new sweeper

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

```yaml
# 3 beam widths × 2 nsteps = 6 configs
generation.search.beam_search.beam_width:
  - 2
  - 4
  - 8

generation.args.nsteps:
  - 200
  - 400
```

Rules (from `script_utils/generate_inference_configs.py:load_sweeper_file`):

- Top-level mapping only. Keys are dot-notation Hydra paths.
- Values must be **lists**. A scalar is auto-wrapped into a single-element list (which pins a value without adding a dimension).
- Cartesian product: total configs = product of list lengths. Two 4-value axes = 16 configs; budget accordingly.
- If a key appears in both the sweeper file and an `--override`, the override wins and that axis collapses.

See [reference/sweep_axes.md](reference/sweep_axes.md) for the full catalogue of swept keys (typical ranges, cost multipliers, what improves/regresses).

### Dry-run preview before generating

Always confirm the config count first:

```bash
python script_utils/generate_inference_configs.py \
    --config_name search_binder_local_pipeline \
    --sweeper configs/sweeps/my_sweep.yaml \
    --override generation.task_name=22_DerF21 \
    --run_name my_sweep \
    --dryrun
```

The output lists every axis + value list and prints `DRY RUN — would generate N config pair(s)`.

## Step 4: Generate configs + loop `complexa design`

Once the dry-run looks right, drop `--dryrun` to materialize `inf_{idx}_{run_name}.yaml` + `eval_{idx}_{run_name}.yaml` pairs under `configs/inference_configs/` and `configs/eval_configs/`. Then loop `complexa design` over the inference configs.

```bash
# 1. Generate one inf_*.yaml + one eval_*.yaml per combination.
python script_utils/generate_inference_configs.py \
    --config_name search_binder_local_pipeline \
    --sweeper configs/sweeps/my_sweep.yaml \
    --override generation.task_name=22_DerF21 \
    --run_name my_sweep

# 2. Loop complexa design over each generated inference config.
for cfg in configs/inference_configs/inf_*_my_sweep.yaml; do
    complexa design "$cfg" || echo "FAILED: $cfg"
done
```

This serialises the sweep on a single GPU — be honest with the user that wall-clock = N × per-run.

### Multi-GPU host (optional speed-up)

On a host with K GPUs, shard the config list K ways and launch one loop per GPU in parallel. Each `complexa design` call uses exactly one GPU at default `gen_njobs=1` / `eval_njobs=1`, so pinning via `CUDA_VISIBLE_DEVICES` keeps them from colliding:

```bash
CONFIGS=(configs/inference_configs/inf_*_my_sweep.yaml)
N=${#CONFIGS[@]}; K=4   # 4 GPUs
for gpu in $(seq 0 $((K-1))); do
    (
        for i in $(seq $gpu $K $((N-1))); do
            CUDA_VISIBLE_DEVICES=$gpu complexa design "${CONFIGS[$i]}" \
                || echo "FAILED on GPU $gpu: ${CONFIGS[$i]}"
        done
    ) &
done
wait
```

Drop `gen_njobs` / `eval_njobs` overrides into the loop only if you intentionally want each `complexa design` invocation to consume multiple GPUs (and you have a way to keep them out of each other's way).

## Step 5: Collect results

Each swept config writes to its own `./inference/inf_{idx}_{run_name}/` directory; the analyze step writes `./evaluation_results/eval_{idx}_{run_name}/results_*.csv`. After the sweep finishes:

```bash
ls -d ./inference/inf_*_my_sweep/
ls ./evaluation_results/eval_*_my_sweep/results_*.csv
```

For each config, parse the analyze CSV (one row per generated binder). Standard columns used for ranking: `i_pae`, `i_plddt`, `sc_rmsd`, `binder_seq`, `passes_filter` (bool). If `passes_filter` is missing, derive a success flag with the user's chosen thresholds (defaults: `i_pae < 10`, `i_plddt > 0.7`, `sc_rmsd < 2.0` — confirm with user).

## Step 6: Rank configs

Emit `sweep_summary.csv` to the run directory. One row per config:

| Column | How to compute |
|---|---|
| `config_id` | The `{idx}` from `inf_{idx}_{run_name}` |
| `<axis_1>`, `<axis_2>`, ... | The swept value at this combination (read from the per-config `inf_*.yaml`) |
| `n_samples` | Row count in the analyze CSV |
| `success_rate` | `passes_filter.mean()` |
| `mean_i_pae` | `i_pae.mean()` (lower = better) |
| `mean_i_plddt` | `i_plddt.mean()` (higher = better) |
| `diversity_score` | Unique sequence count / `n_samples` (or use TM-score clustering if available) |
| `wall_clock_min` | From the per-config log timestamps |

Then report:

- **Best config** = argmax of `success_rate`. Tie-break on `mean_i_pae` (lower).
- **Pareto frontier** over (`wall_clock_min`, `success_rate`): a config is on the frontier iff no other config is both faster AND has higher success rate.

Print the best config + the frontier to the terminal. Save the full table to `sweep_summary.csv`.

## Step 7: Emit manifest

Capture the resolved invocation + outputs for replay. The shared helper takes a single `--output-dir` (it walks for CSVs and pulls the Hydra config from the run's `.hydra/`), so call it against the best-config's evaluation directory and pass the parent `complexa design` command for that run:

```bash
BEST_ID=4   # from Step 6 ranking
python3 .claude/skills/_shared/scripts/write_manifest.py \
    --output-dir ./evaluation_results/eval_${BEST_ID}_my_sweep \
    --command "python script_utils/generate_inference_configs.py --config_name search_binder_local_pipeline --sweeper configs/sweeps/my_sweep.yaml --override generation.task_name=22_DerF21 --run_name my_sweep && for cfg in configs/inference_configs/inf_*_my_sweep.yaml; do complexa design \"\$cfg\"; done" \
    --skill complexa-sweep \
    --out ./sweep_runs/my_sweep/sweep_manifest.json
```

Alongside the manifest, save the ranked `sweep_summary.csv` from Step 6 to `./sweep_runs/my_sweep/sweep_summary.csv` and surface both paths to the user.

## Recommended sweep recipes

| Symptom | Sweep this | Why |
|---|---|---|
| Quality not good enough | `beam_width × nsteps` (use `example.yaml` as a starting point) | Both raise compute → quality; find the cheapest combination that lands. |
| Too slow / want speed-up | `nsteps` downward (e.g. `[100, 200, 400]`) with fixed `beam_width=4` | Find the smallest nsteps that retains success rate. |
| Mode collapse / low diversity | `generation.model.bb_ca.simulation_step_params.sc_scale_noise` (use `bb_ca_temperature.yaml`) | Higher noise → more diverse backbones. |
| Reward over-fitting (high reward, bad metrics) | `generation.reward_model.reward_models.af2folding.reward_weights.{i_pae, plddt}` ratio | Re-balance composite reward. |
| Want statistical robustness on one config | `search_replicas.yaml` (`best_of_n.replicas`) | Same config, more samples → tighter success-rate estimate. |
| Algorithm shoot-out | `generation.search.algorithm` over `[single-pass, best-of-n, beam-search, fk-steering]` | Compare search regimes; pin everything else. |

## Hardware

Total GPU-time for a sweep = `N_configs × per_run_GPU_time`. A single `search_binder_local_pipeline` run on one A100 is roughly 30–90 min (binder length + nsteps dependent). A 4-axis × 4-value sweep = 256 configs × ~60 min = ~256 GPU-hours — at that scale, plan on a multi-GPU host with the Step-4 sharding pattern.

Refer to the shared hardware reference for the per-run baseline + VRAM minima.

## Troubleshooting

| Symptom | Cause | Fix |
|---|---|---|
| `Sweeper file not found` from `generate_inference_configs.py` | Path resolved from wrong CWD | Use a path relative to the repo root; or pass an absolute path. |
| `Sweeper YAML must be a mapping` | Top-level YAML is a list or scalar | Rewrite as `key: [v1, v2]` mapping. |
| `No configs were generated` | One of the value lists is empty `[]` | Sweeper file has a `key: []` line — add at least one value. |
| `complexa design` rejects `--sweeper` | Confusion between entrypoints | The CLI does not accept `--sweeper`. Use `generate_inference_configs.py` first, then loop `complexa design` over the generated configs. |
| Override silently collapses a sweep axis | `--override key=v` shadowed a sweep key | Drop either the override OR the matching key from the sweeper file. |
| One config in the loop fails, sweep keeps going | The `\|\| echo "FAILED…"` in Step 4 swallows the error | Re-run the failed `inf_*.yaml` standalone; check the per-config log under `./logs/`. Skip failed `config_id` when ranking. |

---

For per-axis reference (typical ranges, cost, what gets better/worse), see [reference/sweep_axes.md](reference/sweep_axes.md).

For the user-facing sweep system overview (config generation, output layout), see [`docs/SWEEP.md`](reference/SWEEP.md).