add dist train

fluid/recommendation/ctr/train.py

@@ -50,14 +50,14 @@ def parse_args():
 
     parser.add_argument(
         '--is_local',
-        type=bool,
-        default=True,
-        help='Local train or distributed train (default: True)')
+        type=int,
+        default=1,
+        help='Local train or distributed train (default: 1)')
     # the following arguments is used for distributed train, if is_local == false, then you should set them
     parser.add_argument(
         '--role',
         type=str,
-        default='trainer', # trainer or pserver
+        default='pserver', # trainer or pserver
         help='The path for model to store (default: models)')
     parser.add_argument(
         '--endpoints',
@@ -124,18 +124,22 @@ def train():
     optimizer.minimize(loss)
 
     if args.is_local:
+        print_log("run local training")
         main_program = fluid.default_main_program()
         train_loop(args, main_program, data_list, loss, auc_var, batch_auc_var)
     else:
+        print_log("run dist training")
         t = fluid.DistributeTranspiler()
         t.transpile(args.trainer_id, pservers=args.endpoints, trainers=args.trainers)
         if args.role == "pserver":
-            prog = t.get_pserver_program(args.curargs.rent_endpoint)
+            print_log("run pserver")
+            prog = t.get_pserver_program(args.current_endpoint)
             startup = t.get_startup_program(args.current_endpoint, pserver_program=prog)
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(startup)
             exe.run(prog)
         elif args.role == "trainer":
+            print_log("run trainer")
             train_prog = t.get_trainer_program()
             train_loop(args, train_prog, data_list, loss, auc_var, batch_auc_var)