diff --git a/core/trainer.py b/core/trainer.py
index bbba6250529283d24389e2719b7110f8aa321973..8951e69f6f2a69270e1688979533b0346d27b521 100755
--- a/core/trainer.py
+++ b/core/trainer.py
@@ -76,9 +76,6 @@ class Trainer(object):
 
         _config = envs.load_yaml(config)
 
-        self._context["env"] = _config
-        self._context["dataset"] = _config.get("dataset")
-
         phases = []
         if phase_names is None:
             phases = _config.get("phase")
@@ -86,8 +83,10 @@ class Trainer(object):
             for phase in _config.get("phase"):
                 if phase["name"] in phase_names:
                     phases.append(phase)
-
         self._context["phases"] = phases
+        _config["phase"] = phases
+        self._context["env"] = _config
+        self._context["dataset"] = _config.get("dataset")
         print("PaddleRec: Runner {} Begin".format(self._runner_name))
         self.which_engine()
         self.which_device()
diff --git a/core/trainers/framework/network.py b/core/trainers/framework/network.py
index 7d7a8273b6a402bd163f653a7beb3900de899ae3..2a9a3a4003f36627aff4fe7ab4f86a4979c46525 100644
--- a/core/trainers/framework/network.py
+++ b/core/trainers/framework/network.py
@@ -238,8 +238,8 @@ class PSNetwork(NetworkBase):
         else:
             context["fleet"].init_worker()
             context["dataset"] = {}
-            for dataset in context["env"]["dataset"]:
-                type = envs.get_global_env("dataset." + dataset["name"] +
+            for phase in context["env"]["phase"]:
+                type = envs.get_global_env("dataset." + phase["dataset_name"] +
                                            ".type")
                 if type == "DataLoader":
                     data_loader = DataLoader(context)
@@ -247,9 +247,9 @@ class PSNetwork(NetworkBase):
                                                model._data_loader)
                 elif type == "QueueDataset":
                     dataset_class = QueueDataset(context)
-                    context["dataset"][dataset[
-                        "name"]] = dataset_class.create_dataset(
-                            dataset["name"], context)
+                    context["dataset"][phase[
+                        "dataset_name"]] = dataset_class.create_dataset(
+                            phase["dataset_name"], context)
             context["status"] = "startup_pass"
 
     def _build_strategy(self, context):
@@ -336,7 +336,7 @@ class PslibNetwork(NetworkBase):
             self._server(context)
         else:
             context["dataset"] = {}
-            for dataset in context["env"]["dataset"]:
+            for phase in context["env"]["phase"]:
                 type = envs.get_global_env("dataset." + dataset["name"] +
                                            ".type")
                 if type == "DataLoader":
@@ -363,6 +363,7 @@ class CollectiveNetwork(NetworkBase):
     def build_network(self, context):
         context["model"] = {}
         if len(context["env"]["phase"]) > 1:
+            print("CollectiveNetwork phase:{}".format(context["env"]["phase"]))
             warnings.warn(
                 "Cluster Train Only Support One Phase.",
                 category=UserWarning,
@@ -407,16 +408,17 @@ class CollectiveNetwork(NetworkBase):
                 context["model"][model_dict["name"]]["compiled_program"] = None
 
         context["dataset"] = {}
-        for dataset in context["env"]["dataset"]:
-            type = envs.get_global_env("dataset." + dataset["name"] + ".type")
+        for phase in context["env"]["phase"]:
+            type = envs.get_global_env("dataset." + phase["dataset_name"] +
+                                       ".type")
             if type == "QueueDataset":
                 raise ValueError(
                     "Collective don't support QueueDataset training, please use DataLoader."
                 )
                 dataset_class = QueueDataset(context)
-                context["dataset"][dataset[
-                    "name"]] = dataset_class.create_dataset(dataset["name"],
-                                                            context)
+                context["dataset"][phase[
+                    "dataset_name"]] = dataset_class.create_dataset(
+                        phase["dataset_name"], context)
         context["status"] = "startup_pass"
 
     def _build_strategy(self, context):
diff --git a/core/trainers/framework/runner.py b/core/trainers/framework/runner.py
index 9064989ad467d3104adf50d64300b7903376e7ce..b837cd676d80f2cddddf5c2079c5f39e96a21ab7 100644
--- a/core/trainers/framework/runner.py
+++ b/core/trainers/framework/runner.py
@@ -436,9 +436,11 @@ class RunnerBase(object):
             dirname = envs.get_global_env(name + "save_step_path", None)
             if dirname is None or dirname == "":
                 return
-            dirname = os.path.join(dirname, str(batch_id))
-            logging.info("\tsave batch_id:%d model into: \"%s\"" %
-                         (batch_id, dirname))
+            dirname = os.path.join(dirname,
+                                   "epoch_" + str(context["current_epoch"]) +
+                                   "_batch_" + str(batch_id))
+            logging.info("\tsave epoch_id:%d, batch_id:%d model into: \"%s\"" %
+                         (context["current_epoch"], batch_id, dirname))
             if is_fleet:
                 if context["fleet"].worker_index() == 0:
                     context["fleet"].save_persistables(context["exe"], dirname)
diff --git a/models/recall/word2vec/README.md b/models/recall/word2vec/README.md
index 88edf362cc90834826b959f799449c96017cdba8..31aadc0be7543de225accfe4ba4fa5f080fb512b 100644
--- a/models/recall/word2vec/README.md
+++ b/models/recall/word2vec/README.md
@@ -242,7 +242,7 @@ python -m paddlerec.run -m /home/your/dir/config.yaml #调试模式 直接指定
 
 2. 使用自定义预测程序预测全量测试集：
 ```
-python infer.py --test_dir ./data/all_test --dict_path ./data/all_dict/word_id_dict.txt --batch_size 20000 --model_dir ./increment_w2v/  --start_index 0 --last_index 5 --emb_size 300
+python infer.py --test_dir ./data/all_test --dict_path ./data/all_dict/word_id_dict.txt --batch_size 10000 --model_dir ./increment_w2v/  --start_index 0 --last_index 4 --emb_size 300
 ```
 
 结论：使用cpu训练5轮，自定义预测准确率为0.540，每轮训练时间7小时左右。