Fixed incorrect order in initialization of booster for distributed training. (#1741)

python-package/lightgbm/basic.py

@@ -1481,6 +1481,22 @@ class Booster(object):
                 raise TypeError('Training data should be Dataset instance, met {}'
                                 .format(type(train_set).__name__))
             params_str = param_dict_to_str(params)
+            # set network if necessary
+            for alias in ["machines", "workers", "nodes"]:
+                if alias in params:
+                    machines = params[alias]
+                    if isinstance(machines, string_type):
+                        num_machines = len(machines.split(','))
+                    elif isinstance(machines, (list, set)):
+                        num_machines = len(machines)
+                        machines = ','.join(machines)
+                    else:
+                        raise ValueError("Invalid machines in params.")
+                    self.set_network(machines,
+                                     local_listen_port=params.get("local_listen_port", 12400),
+                                     listen_time_out=params.get("listen_time_out", 120),
+                                     num_machines=params.get("num_machines", num_machines))
+                    break
             # construct booster object
             self.handle = ctypes.c_void_p()
             _safe_call(_LIB.LGBM_BoosterCreate(
@@ -1507,22 +1523,6 @@ class Booster(object):
             self.__is_predicted_cur_iter = [False]
             self.__get_eval_info()
             self.pandas_categorical = train_set.pandas_categorical
-            # set network if necessary
-            for alias in ["machines", "workers", "nodes"]:
-                if alias in params:
-                    machines = params[alias]
-                    if isinstance(machines, string_type):
-                        num_machines = len(machines.split(','))
-                    elif isinstance(machines, (list, set)):
-                        num_machines = len(machines)
-                        machines = ','.join(machines)
-                    else:
-                        raise ValueError("Invalid machines in params.")
-                    self.set_network(machines,
-                                     local_listen_port=params.get("local_listen_port", 12400),
-                                     listen_time_out=params.get("listen_time_out", 120),
-                                     num_machines=params.get("num_machines", num_machines))
-                    break
         elif model_file is not None:
             # Prediction task
             out_num_iterations = ctypes.c_int(0)