Rewrite Dask interface. (#4819)

demo/dask/cpu_training.py

@@ -0,0 +1,35 @@
+import xgboost as xgb
+from xgboost.dask import DaskDMatrix
+from dask.distributed import Client
+from dask.distributed import LocalCluster
+from dask import array as da
+
+
+def main(client):
+    n = 100
+    m = 100000
+    partition_size = 1000
+    X = da.random.random((m, n), partition_size)
+    y = da.random.random(m, partition_size)
+
+    dtrain = DaskDMatrix(client, X, y)
+
+    output = xgb.dask.train(client,
+                            {'verbosity': 2,
+                             'nthread': 1,
+                             'tree_method': 'hist'},
+                            dtrain,
+                            num_boost_round=4, evals=[(dtrain, 'train')])
+    bst = output['booster']
+    history = output['history']
+
+    prediction = xgb.dask.predict(client, bst, dtrain)
+    print('Evaluation history:', history)
+    return prediction
+
+
+if __name__ == '__main__':
+    # or use any other clusters
+    cluster = LocalCluster(n_workers=4, threads_per_worker=1)
+    client = Client(cluster)
+    main(client)