fromdaskimportarrayasdafromdask.distributedimportClient,LocalClusterfromxgboostimportdaskasdxgbfromxgboost.daskimportDaskDMatrixdefmain(client:Client)->None:# generate some random data for demonstrationm=100000n=100rng=da.random.default_rng(1)X=rng.normal(size=(m,n),chunks=(10000,-1))y=X.sum(axis=1)# 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=dxgb.train(client,{"verbosity":1,"tree_method":"hist"},dtrain,num_boost_round=4,evals=[(dtrain,"train")],)bst=output["booster"]history=output["history"]# you can pass output directly into `predict` too.prediction=dxgb.predict(client,bst,dtrain)print("Evaluation history:",history)print("Error:",da.sqrt((prediction-y)**2).mean().compute())if__name__=="__main__":# or use other clusters for scalingwithLocalCluster(n_workers=7,threads_per_worker=4)ascluster:withClient(cluster)asclient:main(client)

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