Skip to main content

Bayesian Optimization Library with GPU support

Project description

Bayesian Optimization Library

The bayesian optimization algorithm is a surrogate-based optimizer that can
optimize expensive black-box functions. This implementation is specifically
tuned to optimize deel neural networks. It is able to handle paralell
evaluations on multiple GPUs, and can use a Random Forest surrogate model.

For additional details see our paper: <https://coming_soon>`_.

Project details

Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
ParallelBayesOpt-0.9.2-py2.py3-none-any.whl (12.2 kB) Copy SHA256 hash SHA256 Wheel py2.py3
ParallelBayesOpt-0.9.2.tar.gz (13.2 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page