Getting started


Nevergrad is a Python 3.6+ library. It can be installed with:

pip install nevergrad

You can also install the master branch instead of the latest release with:

pip install git+

Alternatively, you can clone the repository and run pip install -e . from inside the repository folder.

By default, this only installs requirements for the optimization and parametrization subpackages. If you are also interested in the benchmarking part, you should install with the [benchmark] flag (example: pip install nevergrad[benchmark]), and if you also want the test tools, use the [all] flag (example: pip install -e .[all]).


  • with zsh you will need to run pip install 'nevergrad[all]' instead of pip install nevergrad[all]

  • under Windows, you may need to preinstall torch (for benchmark or all installations) using Pytorch installation instructions.

Installing on Windows

For Windows installation, please refer to the Windows documention.

Basic optimization example

By default all optimizers assume a centered and reduced prior at the beginning of the optimization (i.e. 0 mean and unitary standard deviation).

Optimizing (minimizing!) a function using an optimizer (here OnePlusOne) can be easily run with:

import nevergrad as ng

def square(x):
    return sum((x - .5)**2)

# optimization on x as an array of shape (2,)
optimizer = ng.optimizers.OnePlusOne(parametrization=2, budget=100)
recommendation = optimizer.minimize(square)  # best value
# >>> [0.49971112 0.5002944]

parametrization=n is a shortcut to state that the function has only one variable, of dimension n, See the parametrization tutorial for more complex parametrizations.

recommendation holds the optimal value(s) found for the provided function. It can be directly accessed through recommendation.value which is here a np.ndarray of size 2.

You can print the full list of optimizers with:

import nevergrad as ng

The [optimization documentation](docs/ contains more information on how to use several workers, take full control of the optimization through the ask and tell interface, perform multiobjective optimization, as well as pieces of advice on how to choose the proper optimizer for your problem.

Structure of the package

The goals of this package are to provide:

  • gradient/derivative-free optimization algorithms, including algorithms able to handle noise.

  • tools to parametrize any code, making it painless to optimize your parameters/hyperparameters, whether they are continuous, discrete or a mixture of continuous and discrete parameters.

  • functions on which to test the optimization algorithms.

  • benchmark routines in order to compare algorithms easily.

The structure of the package follows its goal, you will therefore find subpackages:

  • optimization: implementing optimization algorithms

  • parametrization: specifying what are the parameters you want to optimize

  • functions: implementing both simple and complex benchmark functions

  • benchmark: for running experiments comparing the algorithms on benchmark functions

  • common: a set of tools used throughout the package