fix import

fleetrec/core/factory.py

@@ -28,10 +28,7 @@ class TrainerFactory(object):
     def _build_trainer(yaml_path):
         print(envs.pretty_print_envs(envs.get_global_envs()))
 
-        train_mode = envs.get_global_env("train.trainer")
-
-        if train_mode is None:
-            train_mode = envs.get_runtime_envion("train.trainer")
+        train_mode = envs.get_training_mode()
 
         if train_mode == "SingleTraining":
             from fleetrec.core.trainers.single_trainer import SingleTrainer

fleetrec/core/trainers/ctr_coding_trainer.py

@@ -12,10 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-import sys
-import time
-import json
-import datetime
 import numpy as np
 
 import paddle.fluid as fluid

fleetrec/core/utils/envs.py

@@ -29,6 +29,14 @@ def get_runtime_envion(key):
     return os.getenv(key, None)
 
 
+def get_training_mode():
+    train_mode = get_global_env("train.trainer")
+
+    if train_mode is None:
+        train_mode = get_runtime_envion("train.trainer")
+    return train_mode
+
+
 def set_global_envs(envs):
     assert isinstance(envs, dict)
 

fleetrec/examples/__init__.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-examples for user
-"""
\ No newline at end of file

fleetrec/examples/build_in/cluster_training_mpi.yaml

-
-trainer:  "MPIClusterTraining"
-
-pserver_num: 2
-trainer_num: 2
-start_port: 36001
-log_dirname: "logs"
-
-strategy:
-  mode: "async"

fleetrec/examples/build_in/ctr-dnn_train.yaml → fleetrec/examples/ctr-dnn_train.yaml

@@ -27,7 +27,7 @@ train:
     hyper_parameters:
       sparse_inputs_slots: 27
       sparse_feature_number: 1000001
-      sparse_feature_dim: 8
+      sparse_feature_dim: 9
       dense_input_dim: 13
       fc_sizes: [512, 256, 128, 32]
       learning_rate: 0.001

fleetrec/models/ctr_dnn/model.py

@@ -60,13 +60,18 @@ class Model(ModelBase):
         self._data_var.append(self.label_input)
 
     def net(self):
-        def embedding_layer(input):
-            sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self.namespace)
-            sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self.namespace)
+        train_mode = envs.get_training_mode()
+
+        is_distributed = True if train_mode == "CtrTraining" else False
+        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self.namespace)
+        sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self.namespace)
+        sparse_feature_dim = 9 if train_mode == "CtrTraining" else sparse_feature_dim
 
+        def embedding_layer(input):
             emb = fluid.layers.embedding(
                 input=input,
                 is_sparse=True,
+                is_distributed=is_distributed,
                 size=[sparse_feature_number, sparse_feature_dim],
                 param_attr=fluid.ParamAttr(
                     name="SparseFeatFactors",

fleetrec/run.py

@@ -103,6 +103,15 @@ if __name__ == "__main__":
             local_mpi_engine(cluster_envs, args.model)
     elif args.engine.upper() == "CLUSTER":
         print("launch ClusterTraining with cluster to run model: {}".format(args.model))
+
+        if version.is_transpiler():
+            print("use ClusterTraining to run model: {}".format(args.model))
+            cluster_envs = {"train.trainer": "ClusterTraining"}
+            envs.set_runtime_envions(cluster_envs)
+        else:
+            cluster_envs = {"train.trainer": "CtrTraining"}
+            envs.set_runtime_envions(cluster_envs)
+
         run(args.model)
     elif args.engine.upper() == "USER_DEFINE":
         engine_file = args.engine_extras