16eb41936d
* Handle the new `device` parameter in dask and demos. - Check no ordinal is specified in the dask interface. - Update demos. - Update dask doc. - Update the condition for QDM.
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
"""
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Example of training with Dask on CPU
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====================================
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"""
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from dask import array as da
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from dask.distributed import Client, LocalCluster
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import xgboost as xgb
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from xgboost.dask import DaskDMatrix
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def main(client):
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# generate some random data for demonstration
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m = 100000
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n = 100
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X = da.random.random(size=(m, n), chunks=100)
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y = da.random.random(size=(m,), chunks=100)
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# DaskDMatrix acts like normal DMatrix, works as a proxy for local
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# DMatrix scatter around workers.
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dtrain = DaskDMatrix(client, X, y)
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# Use train method from xgboost.dask instead of xgboost. This
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# distributed version of train returns a dictionary containing the
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# resulting booster and evaluation history obtained from
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# evaluation metrics.
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output = xgb.dask.train(
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client,
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{"verbosity": 1, "tree_method": "hist"},
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dtrain,
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num_boost_round=4,
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evals=[(dtrain, "train")],
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)
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bst = output["booster"]
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history = output["history"]
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# you can pass output directly into `predict` too.
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prediction = xgb.dask.predict(client, bst, dtrain)
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print("Evaluation history:", history)
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return prediction
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if __name__ == "__main__":
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# or use other clusters for scaling
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with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
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with Client(cluster) as client:
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main(client)
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