LegateBoost

GBM implementation on Legate. The primary goals of LegateBoost is to provide a state-of-the-art distributed GBM implementation on Legate, capable of running on CPUs or GPUs at supercomputer scale.

For developers - see contributing

Example

Run with the legate launcher

legate example_script.py

import cunumeric as cn
import legateboost as lb

X = cn.random.random((1000, 10))
y = cn.random.random(X.shape[0])
model = lb.LBRegressor(verbose=1, n_estimators=100, random_state=0, max_depth=2).fit(
    X, y
)

Features

Probabilistic regression

Legateboost can learn distributions for continuous data. This is useful in cases where simply predicting the mean does not carry enough information about the training data:

The above example can be found here: examples/probabilistic_regression.

Installation

Dependencies:

• cunumeric
• sklearn

From the project directory

pip install .