[PYTHON-DIST] Distributed xgboost python training API.

tests/distributed/runtests.sh

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+#!/bin/bash
+
+PYTHONPATH=../../python-package/ ../../dmlc-core/tracker/dmlc-submit  --cluster=local --num-workers=3\
+  python test_basic.py

tests/distributed/test_basic.py

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+#!/usr/bin/python
+import numpy as np
+import scipy.sparse
+import pickle
+import xgboost as xgb
+
+# Load file, file will be automatically sharded in distributed mode.
+dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
+dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
+
+# specify parameters via map, definition are same as c++ version
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+
+# specify validations set to watch performance
+watchlist  = [(dtest,'eval'), (dtrain,'train')]
+num_round = 20
+
+# Run training, all the features in training API is available.
+# Currently, this script only support calling train once for fault recovery purpose.
+bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)
+
+# save the model, only ask process 0 to save the model.
+if xgb.rabit.get_rank() == 0:
+    bst.save_model("test.model")
+    xgb.rabit.tracker_print("Finished training\n")
+
+# Notify the tracker all training has been successful
+# This is only needed in distributed training.
+xgb.rabit.finalize()