add paddle_cloud_train of ctr

fluid/PaddleRec/ctr/train.py

@@ -56,12 +56,26 @@ def parse_args():
         type=int,
         default=1000001,
         help='sparse feature hashing space for index processing')
-
     parser.add_argument(
         '--is_local',
         type=int,
         default=1,
         help='Local train or distributed train (default: 1)')
+    parser.add_argument(
+        '--cloud_train',
+        type=int,
+        default=0,
+        help='Local train or distributed train on paddlecloud (default: 0)')
+    parser.add_argument(
+        '--async_mode',
+        action='store_true',
+        default=False,
+        help='Whether start pserver in async mode to support ASGD')
+    parser.add_argument(
+        '--no_split_var',
+        action='store_true',
+        default=False,
+        help='Whether split variables into blocks when update_method is pserver')
     # the following arguments is used for distributed train, if is_local == false, then you should set them
     parser.add_argument(
         '--role',
@@ -134,6 +148,21 @@ def train():
     loss, data_list, auc_var, batch_auc_var = ctr_dnn_model(args.embedding_size, args.sparse_feature_dim)
     optimizer = fluid.optimizer.Adam(learning_rate=1e-4)
     optimizer.minimize(loss)
+    if args.cloud_train:
+        # the port of all pservers, needed by both trainer and pserver
+        port = os.getenv("PADDLE_PORT", "6174")
+        # comma separated ips of all pservers, needed by trainer and
+        pserver_ips = os.getenv("PADDLE_PSERVERS", "")
+        eplist = []
+        for ip in pserver_ips.split(","):
+            eplist.append(':'.join([ip, port]))
+        args.endpoints = ",".join(eplist)
+        args.trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
+        args.current_endpoint = os.getenv("POD_IP", "localhost") + ":" + port
+        args.role = os.getenv("TRAINING_ROLE", "TRAINER")
+        args.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+        args.is_local = bool(int(os.getenv("PADDLE_IS_LOCAL", 0)))
+
 
     if args.is_local:
         logger.info("run local training")
@@ -143,18 +172,22 @@ def train():
         logger.info("run dist training")
         t = fluid.DistributeTranspiler()
         t.transpile(args.trainer_id, pservers=args.endpoints, trainers=args.trainers)
-        if args.role == "pserver":
+        if args.role == "pserver" or args.role == "PSERVER":
             logger.info("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":
+        elif args.role == "trainer" or args.role == "TRAINING_ROLE":
             logger.info("run trainer")
             train_prog = t.get_trainer_program()
             train_loop(args, train_prog, data_list, loss, auc_var, batch_auc_var, 
                        args.trainers, args.trainer_id)
+        else:
+            raise ValueError(
+                'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
+            )
 
 
 if __name__ == '__main__':