fix import

fleetrec/core/factory.py

@@ -25,7 +25,7 @@ class TrainerFactory(object):
         pass
 
     @staticmethod
-    def _build_trainer(config, yaml_path):
+    def _build_trainer(yaml_path):
         print(envs.pretty_print_envs(envs.get_global_envs()))
 
         train_mode = envs.get_global_env("train.trainer")
@@ -40,8 +40,8 @@ class TrainerFactory(object):
             from fleetrec.core.trainers.cluster_trainer import ClusterTrainer
             trainer = ClusterTrainer(yaml_path)
         elif train_mode == "CtrTraining":
-            from fleetrec.core.trainers.ctr_modul_trainer import CtrPaddleTrainer
-            trainer = CtrPaddleTrainer(config)
+            from fleetrec.core.trainers.ctr_coding_trainer import CtrPaddleTrainer
+            trainer = CtrPaddleTrainer(yaml_path)
         elif train_mode == "UserDefineTraining":
             train_location = envs.get_global_env("train.location")
             train_dirname = os.path.dirname(train_location)
@@ -63,7 +63,7 @@ class TrainerFactory(object):
             raise ValueError("fleetrec's config only support yaml")
 
         envs.set_global_envs(_config)
-        trainer = TrainerFactory._build_trainer(_config, config)
+        trainer = TrainerFactory._build_trainer(config)
         return trainer
 
 

fleetrec/core/trainer.py

@@ -14,6 +14,8 @@
 
 import abc
 import time
+
+import yaml
 from paddle import fluid
 
 
@@ -28,7 +30,10 @@ class Trainer(object):
         self._exe = fluid.Executor(self._place)
         self._exector_context = {}
         self._context = {'status': 'uninit', 'is_exit': False}
-        self._config = config
+        self._config_yaml = config
+
+        with open(config, 'r') as rb:
+            self._config = yaml.load(rb.read(), Loader=yaml.FullLoader)
 
     def regist_context_processor(self, status_name, processor):
         """

fleetrec/core/trainers/ctr_coding_trainer.py

@@ -62,7 +62,7 @@ class CtrPaddleTrainer(Trainer):
         reader_class = envs.get_global_env("class", None, namespace)
         abs_dir = os.path.dirname(os.path.abspath(__file__))
         reader = os.path.join(abs_dir, '../utils', 'reader_instance.py')
-        pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN", self._config)
+        pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN", self._config_yaml)
         train_data_path = envs.get_global_env("train_data_path", None, namespace)
 
         dataset = fluid.DatasetFactory().create_dataset()

fleetrec/run.py

@@ -17,7 +17,6 @@ def run(model_yaml):
 
 def single_engine(single_envs, model_yaml):
     print(envs.pretty_print_envs(single_envs, ("Single Envs", "Value")))
-
     envs.set_runtime_envions(single_envs)
     run(model_yaml)
 
@@ -33,8 +32,8 @@ def local_cluster_engine(cluster_envs, model_yaml):
 
 def local_mpi_engine(cluster_envs, model_yaml):
     from fleetrec.core.engine.local_mpi_engine import LocalMPIEngine
-    print(envs.pretty_print_envs(cluster_envs, ("Local MPI Cluster Envs", "Value")))
 
+    print(envs.pretty_print_envs(cluster_envs, ("Local MPI Cluster Envs", "Value")))
     envs.set_runtime_envions(cluster_envs)
     launch = LocalMPIEngine(cluster_envs, model_yaml)
     launch.run()
@@ -79,7 +78,7 @@ if __name__ == "__main__":
             if not mpi_path:
                 raise RuntimeError("can not find mpirun, please check environment")
 
-            cluster_envs = {"mpirun": mpi_path, "train.trainer": "CtrTraining"}
+            cluster_envs = {"mpirun": mpi_path, "train.trainer": "CtrTraining", "log_dir": "logs"}
             local_mpi_engine(cluster_envs, args.model)
     elif args.engine.upper() == "LOCAL_CLUSTER":
         print("use 1X1 ClusterTraining at localhost to run model: {}".format(args.model))
@@ -100,7 +99,7 @@ if __name__ == "__main__":
             if not mpi_path:
                 raise RuntimeError("can not find mpirun, please check environment")
 
-            cluster_envs = {"mpirun": mpi_path, "train.trainer": "CtrTraining"}
+            cluster_envs = {"mpirun": mpi_path, "train.trainer": "CtrTraining", "log_dir": "logs"}
             local_mpi_engine(cluster_envs, args.model)
     elif args.engine.upper() == "CLUSTER":
         print("launch ClusterTraining with cluster to run model: {}".format(args.model))