093e2227e3
* Serialise booster after training to reset state * Prevent process_type being set on load * Check for correct updater sequence
29 lines
1.0 KiB
Python
29 lines
1.0 KiB
Python
#!/usr/bin/python
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import xgboost as xgb
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# Always call this before using distributed module
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xgb.rabit.init()
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# Load file, file will be automatically sharded in distributed mode.
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dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
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dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
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# Specify parameters via map, definition are same as c++ version
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param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic' }
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# Specify validations set to watch performance
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 20
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# Run training, all the features in training API is available.
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# Currently, this script only support calling train once for fault recovery purpose.
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bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)
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# Save the model, only ask process 0 to save the model.
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bst.save_model("test.model{}".format(xgb.rabit.get_rank()))
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xgb.rabit.tracker_print("Finished training\n")
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# Notify the tracker all training has been successful
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# This is only needed in distributed training.
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xgb.rabit.finalize()
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