Skip to main content

A Python toolbox for performing gradient-free optimization

Project description

Support Ukraine CircleCI

Nevergrad - A gradient-free optimization platform

Nevergrad

nevergrad is a Python 3.8+ library. It can be installed with:

pip install nevergrad

More installation options, including windows installation, and complete instructions are available in the "Getting started" section of the documentation.

You can join Nevergrad users Facebook group here.

Minimizing a function using an optimizer (here NGOpt) is straightforward:

import nevergrad as ng

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

optimizer = ng.optimizers.NGOpt(parametrization=2, budget=100)
recommendation = optimizer.minimize(square)
print(recommendation.value)  # recommended value
>>> [0.49971112 0.5002944]

nevergrad can also support bounded continuous variables as well as discrete variables, and mixture of those. To do this, one can specify the input space:

import nevergrad as ng

def fake_training(learning_rate: float, batch_size: int, architecture: str) -> float:
    # optimal for learning_rate=0.2, batch_size=4, architecture="conv"
    return (learning_rate - 0.2)**2 + (batch_size - 4)**2 + (0 if architecture == "conv" else 10)

# Instrumentation class is used for functions with multiple inputs
# (positional and/or keywords)
parametrization = ng.p.Instrumentation(
    # a log-distributed scalar between 0.001 and 1.0
    learning_rate=ng.p.Log(lower=0.001, upper=1.0),
    # an integer from 1 to 12
    batch_size=ng.p.Scalar(lower=1, upper=12).set_integer_casting(),
    # either "conv" or "fc"
    architecture=ng.p.Choice(["conv", "fc"])
)

optimizer = ng.optimizers.NGOpt(parametrization=parametrization, budget=100)
recommendation = optimizer.minimize(fake_training)

# show the recommended keyword arguments of the function
print(recommendation.kwargs)
>>> {'learning_rate': 0.1998, 'batch_size': 4, 'architecture': 'conv'}

Learn more on parametrization in the documentation!

Example of optimization

Convergence of a population of points to the minima with two-points DE.

Documentation

Check out our documentation! It's still a work in progress, don't hesitate to submit issues and/or PR to update it and make it clearer! The last version of our data and the last version of our PDF report.

Citing

@misc{nevergrad,
    author = {J. Rapin and O. Teytaud},
    title = {{Nevergrad - A gradient-free optimization platform}},
    year = {2018},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://GitHub.com/FacebookResearch/Nevergrad}},
}

License

nevergrad is released under the MIT license. See LICENSE for additional details about it. See also our Terms of Use and Privacy Policy.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nevergrad-0.13.0.tar.gz (373.3 kB view details)

Uploaded Source

Built Distribution

nevergrad-0.13.0-py3-none-any.whl (466.2 kB view details)

Uploaded Python 3

File details

Details for the file nevergrad-0.13.0.tar.gz.

File metadata

  • Download URL: nevergrad-0.13.0.tar.gz
  • Upload date:
  • Size: 373.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.18

File hashes

Hashes for nevergrad-0.13.0.tar.gz
Algorithm Hash digest
SHA256 d362cdebda4e0e5ea4321a79a6510f6e64cd4550b395650c4174d4c99b24993d
MD5 70b7ce30c0df958a75f4f58f134de9ba
BLAKE2b-256 d4295a7bcb70830fcf923c1d4c2682ed2f3bb5f42a1095dcc55e9ca921f90f56

See more details on using hashes here.

Provenance

File details

Details for the file nevergrad-0.13.0-py3-none-any.whl.

File metadata

  • Download URL: nevergrad-0.13.0-py3-none-any.whl
  • Upload date:
  • Size: 466.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.18

File hashes

Hashes for nevergrad-0.13.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9650040095cb0cf5091a9f09707e56aaea1d36b68bf954e5c2d06bb695890b23
MD5 7383128941e529f95cefa5b6ab12457a
BLAKE2b-256 0257b81458767d273b7167746099b5ac7be49e7133f4b5b48b03720ff47f3479

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page