neuropt 0.5.1

LLM-guided ML optimization — point it at a training script, it reads the curves and designs better models

neuropt

An LLM reads your training curves and designs your next experiment.

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Point it at a training script, let it run overnight. The LLM sees full per-epoch train/val curves, spots overfitting, and proposes what to try next — like a research assistant who never sleeps and actually reads the loss plots.

vs Optuna and random search

Same 15-eval budget on two tasks: CNN architecture search (14 params) and XGBoost tuning (9 params, 7-class Covertype). These results use Claude Haiku 4.5 (the smallest and cheapest of their 4.5 models). We expect even stronger results with Sonnet or Opus. Optuna's TPE was configured with n_startup_trials=3 for a fair comparison (default is 10, which would make it purely random for most of the budget).

In a separate 200-eval run on the CNN task, neuropt again beat the others within 15 evals and kept improving — reaching 0.337 val loss by eval 200 (vs 0.454 for Optuna's best at 15). Local Qwen backend (experimental) also beat Optuna at 15 evals (0.440 vs 0.454) despite a 40% JSON parse failure rate.

Quick start

pip install neuropt[llm]
export ANTHROPIC_API_KEY="sk-ant-..."

Option 1 — define what to search over:

# train.py
search_space = {
    "lr": (1e-4, 1e-1),                    # auto-detects log-scale
    "hidden_dim": (32, 512),                # auto-detects integer
    "activation": ["relu", "gelu", "silu"], # categorical
}

def train_fn(config):
    model = build_my_model(config["hidden_dim"], config["activation"])
    # ... train, return per-epoch losses for smarter LLM decisions ...
    return {"score": val_loss, "train_losses": [...], "val_losses": [...]}

Option 2 — just give it a model, we figure out the rest:

# train.py
model = torchvision.models.resnet18(num_classes=10)  # neuropt introspects this

def train_fn(config):
    m = config["model"].to("cuda")  # deep copy with modifications applied
    # ... train ...
    return {"score": val_loss, "train_losses": [...], "val_losses": [...]}

Then run:

neuropt run train.py

Runs until Ctrl+C. Crash-safe, resumable. Works in notebooks too:

from neuropt import ArchSearch

search = ArchSearch(train_fn=train_fn, search_space=search_space, backend="claude")
search.run(max_evals=50)

Documentation

See the full documentation for:

• How it works — what the LLM sees, training curve analysis
• CLI reference — neuropt run, inspect, results
• Python API — ArchSearch, from_model, search space types
• Model Introspection — from_model() auto-detection, activations, dropout, pooling
• Fine-Tuning — pretrained models, freeze strategies, L2-SP
• Examples — CNN search, ResNet tuning
• Benchmarks — vs Optuna, random search

Installation

pip install neuropt                # core
pip install neuropt[llm]           # + Claude API (recommended)
pip install neuropt[llm-openai]    # + OpenAI API
pip install neuropt[all]           # everything

License

MIT