CudaTree is an implementation of Leo Breiman’s Random Forests adapted to run on the GPU. A random forest is an ensemble of randomized decision trees which vote together to predict new labels. CudaTree parallelizes the construction of each individual tree in the ensemble and thus is able to train faster than the latest version of scikits-learn.
Usage
import numpy as np
from cudatree import load_data, RandomForestClassifier
x_train, y_train = load_data("digits")
forest = RandomForestClassifier(n_estimators = 50, max_features = 6)
forest.fit(x_train, y_train)
forest.predict(x_train)
Dependencies
CudaTree is writen for Python 2.7 and depends on:
scikit-learn
NumPy
PyCUDA
Parakeet
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Metadata
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Release files for cudatree 0.6
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| File | Size | Uploaded | |
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Release files / cudatree-0.6.tar.gz
| Download URL | cudatree-0.6.tar.gz |
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