gplearn-CUDA 0.4.3

GPU-accelerated Genetic Programming in Python (CUDA extension of gplearn)

Welcome to gplearn-CUDA!

gplearn-CUDA implements GPU-accelerated Genetic Programming in Python, with a scikit-learn inspired and compatible API.

This project is a high-performance extension of the original gplearn library developed by Trevor Stephens. It maintains all the original functionality while introducing massive parallelization via NVIDIA CUDA.

Overview

While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn-CUDA is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.

Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship.

CUDA Acceleration

The core contribution of gplearn-CUDA is high-performance GPU acceleration via NVIDIA CUDA. By setting device='cuda', the library utilizes a custom virtual machine interpreter on the GPU to achieve 2x–4x speedups on massive datasets and large populations.

from gplearn.genetic import SymbolicRegressor
# Enable CUDA acceleration
est = SymbolicRegressor(device='cuda', population_size=5000)
est.fit(X, y)

Installation

gplearn-CUDA requires a recent version of scikit-learn. The CUDA acceleration is compatible with CUDA 11.2 through 13.x.

To install the GPU-enabled version via pip (targeting CUDA 12.x by default):

pip install gplearn-CUDA[cuda]

For other CUDA versions or Conda installation, please see the Installation Guide.

License & Credits

gplearn-CUDA is released under the BSD 3-Clause License, following the licensing of the original project.

• Original Author: Trevor Stephens (@trevorstephens)

• CUDA Extension: LGA-Personal

For the latest development version, first get the source from github:

git clone https://github.com/LGA-Personal/gplearn-CUDA.git

Then navigate into the local directory and simply run:

pip install .

If you come across any issues in running or installing the package, please submit a bug report.