Example of training with Dask on CPU

import xgboost as xgb
from xgboost.dask import DaskDMatrix
from dask.distributed import Client
from dask.distributed import LocalCluster
from dask import array as da

def main(client):
    # generate some random data for demonstration
    m = 100000
    n = 100
    X = da.random.random(size=(m, n), chunks=100)
    y = da.random.random(size=(m, ), chunks=100)

    # DaskDMatrix acts like normal DMatrix, works as a proxy for local
    # DMatrix scatter around workers.
    dtrain = DaskDMatrix(client, X, y)

    # Use train method from xgboost.dask instead of xgboost.  This
    # distributed version of train returns a dictionary containing the
    # resulting booster and evaluation history obtained from
    # evaluation metrics.
    output = xgb.dask.train(client,
                            {'verbosity': 1,
                             'tree_method': 'hist'},
                            num_boost_round=4, evals=[(dtrain, 'train')])
    bst = output['booster']
    history = output['history']

    # you can pass output directly into `predict` too.
    prediction = xgb.dask.predict(client, bst, dtrain)
    print('Evaluation history:', history)
    return prediction

if __name__ == '__main__':
    # or use other clusters for scaling
    with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
        with Client(cluster) as client:

Total running time of the script: ( 0 minutes 0.000 seconds)

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