{"library":"nevergrad","title":"Nevergrad","description":"Nevergrad is a Python 3.6+ library for performing gradient-free optimization. Developed by Facebook AI Research, it provides a rich collection of optimization algorithms (evolutionary, bandit, Bayesian, etc.) and robust tools for parameter and hyperparameter tuning. It can optimize functions with continuous, discrete, or mixed variable types, even in noisy environments. The library maintains an active development status with regular releases.","language":"python","status":"active","last_verified":"Sun May 17","install":{"commands":["pip install nevergrad"],"cli":null},"imports":["import nevergrad as ng","from nevergrad.optimization import NGOpt","from nevergrad import parametrization as p\nparametrization = p.Instrumentation(...)"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import nevergrad as ng\nimport numpy as np\n\ndef objective_function(learning_rate: float, batch_size: int, architecture: str) -> float:\n    # Simulate a training process; optimal for lr=0.2, bs=4, arch='conv'\n    return (learning_rate - 0.2)**2 + (batch_size - 4)**2 + (0 if architecture == 'conv' else 10)\n\n# Define the parameter space using Instrumentation\nparametrization = ng.p.Instrumentation(\n    # Log-distributed scalar for learning_rate\n    learning_rate=ng.p.Log(lower=0.001, upper=1.0),\n    # Integer scalar for batch_size\n    batch_size=ng.p.Scalar(lower=1, upper=12).set_integer_casting(),\n    # Categorical choice for architecture\n    architecture=ng.p.Choice([\"conv\", \"fc\"]),\n)\n\n# Choose an optimizer (NGOpt is a recommended adaptive optimizer)\noptimizer = ng.optimizers.NGOpt(parametrization=parametrization, budget=100)\n\n# Minimize the objective function\nrecommendation = optimizer.minimize(objective_function)\n\nprint(f\"Optimal hyperparameters: {recommendation.kwargs}\")\nprint(f\"Best objective value: {objective_function(**recommendation.kwargs)}\")","lang":"python","description":"This quickstart demonstrates how to define a function with mixed continuous, discrete, and categorical parameters using `nevergrad.parametrization.Instrumentation` and then optimize it using `nevergrad.optimizers.NGOpt`. 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