Bayesian Optimization Library with GPU support
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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>`_.
=======================
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>`_.
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| 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 |
