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.
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
"""
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Use scikit-learn regressor interface with GPU histogram tree method
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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
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# It's recommended to use dask_cuda for GPU assignment
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from dask_cuda import LocalCUDACluster
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import xgboost
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def main(client):
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# generate some random data for demonstration
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n = 100
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m = 1000000
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partition_size = 10000
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X = da.random.random((m, n), partition_size)
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y = da.random.random(m, partition_size)
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regressor = xgboost.dask.DaskXGBRegressor(verbosity=1)
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# set the device to CUDA
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regressor.set_params(tree_method="hist", device="cuda")
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# assigning client here is optional
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regressor.client = client
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regressor.fit(X, y, eval_set=[(X, y)])
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prediction = regressor.predict(X)
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bst = regressor.get_booster()
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history = regressor.evals_result()
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print("Evaluation history:", history)
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# returned prediction is always a dask array.
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assert isinstance(prediction, da.Array)
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return bst # returning the trained model
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if __name__ == "__main__":
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# With dask cuda, one can scale up XGBoost to arbitrary GPU clusters.
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# `LocalCUDACluster` used here is only for demonstration purpose.
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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main(client)
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