79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
"""
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Example of training with Dask on GPU
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====================================
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"""
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import dask_cudf
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from dask import array as da
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from dask import dataframe as dd
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from dask.distributed import Client
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from dask_cuda import LocalCUDACluster
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import xgboost as xgb
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from xgboost import dask as dxgb
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from xgboost.dask import DaskDMatrix
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def using_dask_matrix(client: Client, X, y):
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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(client,
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{'verbosity': 2,
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# Golden line for GPU training
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'tree_method': 'gpu_hist'},
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dtrain,
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num_boost_round=4, evals=[(dtrain, 'train')])
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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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def using_quantile_device_dmatrix(client: Client, X, y):
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"""`DaskQuantileDMatrix` is a data type specialized for `gpu_hist`, tree
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method that reduces memory overhead. When training on GPU pipeline, it's
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preferred over `DaskDMatrix`.
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.. versionadded:: 1.2.0
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"""
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# Input must be on GPU for `DaskQuantileDMatrix`.
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X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X))
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y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y))
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# `DaskQuantileDMatrix` is used instead of `DaskDMatrix`, be careful
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# that it can not be used for anything else other than training.
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dtrain = dxgb.DaskQuantileDMatrix(client, X, y)
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output = xgb.dask.train(
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client, {"verbosity": 2, "tree_method": "gpu_hist"}, dtrain, num_boost_round=4
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)
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prediction = xgb.dask.predict(client, output, X)
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return prediction
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if __name__ == '__main__':
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# `LocalCUDACluster` is used for assigning GPU to XGBoost processes. Here
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# `n_workers` represents the number of GPUs since we use one GPU per worker
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# process.
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with LocalCUDACluster(n_workers=2, threads_per_worker=4) as cluster:
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with Client(cluster) as 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=10000)
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y = da.random.random(size=(m, ), chunks=10000)
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print('Using DaskQuantileDMatrix')
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from_ddqdm = using_quantile_device_dmatrix(client, X, y)
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print('Using DMatrix')
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from_dmatrix = using_dask_matrix(client, X, y)
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