CLI Reference

`neuropt run`

Run LLM-guided search on a training script.

neuropt run train.py
neuropt run train.py --backend claude
neuropt run train.py --backend none -n 50 --log results.jsonl

Your script must define:

train_fn(config) — returns dict with at least {"score": float}
search_space dict or model (nn.Module) — one of the two

Optional: ml_context string with domain knowledge for the LLM.

Option	Default	Description
`--backend`	auto	`auto`, `claude`, `openai`, `qwen`, `none`
`--log`	search.jsonl	Log file path (supports resume)
`-b` / `--batch-size`	3	Configs proposed per LLM call
`-n` / `--max-evals`	unlimited	Stop after N experiments
`--device`	auto	`cuda`, `mps`, `cpu`
`--timeout`	600	Max seconds per experiment

`neuropt inspect`

Show what neuropt would search over for a given model.

neuropt inspect train.py

Model: 11,689,512 parameters
  Activations: ReLU (9 layers)
  BatchNorm: 20 layers
  Pooling: 1 layers (current: avg)

Search space (7 params):
  activation: Categorical(['relu', 'gelu', 'silu', 'leaky_relu', 'mish', 'hardswish', 'prelu'])
  use_batchnorm: Categorical([True, False])
  pool_type: Categorical(['avg', 'max', 'attention'])
  lr: LogUniform(0.0001, 0.1)
  wd: LogUniform(1e-06, 0.01)
  optimizer: Categorical(['sgd', 'adam', 'adamw'])

Only works with scripts that define a model variable.

`neuropt results`

Analyze a search log.

neuropt results search.jsonl
neuropt results search.jsonl --top 20

Shows total experiments, top N results with configs, and convergence over time.