diff --git a/core/factory.py b/core/factory.py
index 3490a8f385b410f7032886af20db84ecbaa017c2..9430c88283800e69db7043aa141b6f735212c79f 100755
--- a/core/factory.py
+++ b/core/factory.py
@@ -14,7 +14,6 @@
 
 import os
 import sys
-import yaml
 from paddlerec.core.utils import envs
 
 trainer_abs = os.path.join(
@@ -61,7 +60,6 @@ class TrainerFactory(object):
     def create(config):
         _config = envs.load_yaml(config)
         envs.set_global_envs(_config)
-        envs.update_workspace()
         trainer = TrainerFactory._build_trainer(config)
         return trainer
 
diff --git a/core/model.py b/core/model.py
index 30bbb7fc327f1df236aeee181ebbb0a7fca0b930..e96d01fe0bbcc0541a2b5aea458e2cb34fa89303 100755
--- a/core/model.py
+++ b/core/model.py
@@ -35,7 +35,6 @@ class ModelBase(object):
         self._data_loader = None
         self._infer_data_loader = None
         self._fetch_interval = 20
-        self._namespace = "train.model"
         self._platform = envs.get_platform()
         self._init_hyper_parameters()
         self._env = config
@@ -50,7 +49,7 @@ class ModelBase(object):
         self._slot_inited = True
         dataset = {}
         model_dict = {}
-        for i in self._env["executor"]:
+        for i in self._env["phase"]:
             if i["name"] == kargs["name"]:
                 model_dict = i
                 break
@@ -89,7 +88,7 @@ class ModelBase(object):
                 self._data_var.append(l)
                 self._sparse_data_var.append(l)
 
-        dataset_class = dataset["type"]
+        dataset_class = envs.get_global_env(name + "type")
         if dataset_class == "DataLoader":
             self._init_dataloader()
 
@@ -139,10 +138,7 @@ class ModelBase(object):
             os.environ["FLAGS_communicator_is_sgd_optimizer"] = '0'
 
         if name == "SGD":
-            reg = envs.get_global_env("hyper_parameters.reg", 0.0001,
-                                      self._namespace)
-            optimizer_i = fluid.optimizer.SGD(
-                lr, regularization=fluid.regularizer.L2DecayRegularizer(reg))
+            optimizer_i = fluid.optimizer.SGD(lr)
         elif name == "ADAM":
             optimizer_i = fluid.optimizer.Adam(lr, lazy_mode=True)
         elif name == "ADAGRAD":
@@ -206,31 +202,8 @@ class ModelBase(object):
     def net(self, is_infer=False):
         return None
 
-    def _construct_reader(self, is_infer=False):
-        if is_infer:
-            self._infer_data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._infer_data_var,
-                capacity=64,
-                use_double_buffer=False,
-                iterable=False)
-        else:
-            dataset_class = envs.get_global_env("dataset_class", None,
-                                                "train.reader")
-            if dataset_class == "DataLoader":
-                self._data_loader = fluid.io.DataLoader.from_generator(
-                    feed_list=self._data_var,
-                    capacity=64,
-                    use_double_buffer=False,
-                    iterable=False)
-
     def train_net(self):
-        input_data = self.input_data(is_infer=False)
-        self._data_var = input_data
-        self._construct_reader(is_infer=False)
-        self.net(input_data, is_infer=False)
+        pass
 
     def infer_net(self):
-        input_data = self.input_data(is_infer=True)
-        self._infer_data_var = input_data
-        self._construct_reader(is_infer=True)
-        self.net(input_data, is_infer=True)
+        pass
diff --git a/core/reader.py b/core/reader.py
index dd20d588a5c36754190e708484505895d19c41a4..589e6c192330baf04c202952cefb04177f3e4297 100755
--- a/core/reader.py
+++ b/core/reader.py
@@ -17,7 +17,6 @@ import abc
 import os
 from functools import reduce
 import paddle.fluid.incubate.data_generator as dg
-import yaml
 from paddlerec.core.utils import envs
 
 
@@ -28,7 +27,6 @@ class ReaderBase(dg.MultiSlotDataGenerator):
         dg.MultiSlotDataGenerator.__init__(self)
         _config = envs.load_yaml(config)
         envs.set_global_envs(_config)
-        envs.update_workspace()
 
     @abc.abstractmethod
     def init(self):
@@ -47,7 +45,6 @@ class SlotReader(dg.MultiSlotDataGenerator):
         dg.MultiSlotDataGenerator.__init__(self)
         _config = envs.load_yaml(config)
         envs.set_global_envs(_config)
-        envs.update_workspace()
 
     def init(self, sparse_slots, dense_slots, padding=0):
         from operator import mul
diff --git a/core/trainer.py b/core/trainer.py
index 9694d0d04f9f6809e72b02045ddce11e3bbe30c8..8b1afd449a70265d5bcae9996d42795a1235197a 100755
--- a/core/trainer.py
+++ b/core/trainer.py
@@ -16,7 +16,6 @@ import abc
 import os
 import time
 import sys
-import yaml
 import traceback
 
 from paddle import fluid
@@ -64,19 +63,31 @@ class Trainer(object):
         self.increment_models = []
         self._exector_context = {}
         self._context = {'status': 'uninit', 'is_exit': False}
-        self._config_yaml = config
-        self._context["config_yaml"] = self._config_yaml
+        self._context["config_yaml"] = config
 
-        self._config = envs.load_yaml(config)
-
-        self._context["env"] = self._config
         self._model = {}
         self._dataset = {}
-        envs.set_global_envs(self._config)
-        envs.update_workspace()
-        self._runner_name = envs.get_global_env("mode")
+
+        self._runner_name = envs.get_runtime_environ("mode")
         self._context["runner_name"] = self._runner_name
 
+        phase_names = envs.get_global_env(
+            "runner." + self._runner_name + ".phases", None)
+
+        _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")
+        else:
+            for phase in _config.get("phase"):
+                if phase["name"] in phase_names:
+                    phases.append(phase)
+
+        self._context["phases"] = phases
         print("PaddleRec: Runner {} Begin".format(self._runner_name))
         self.which_engine()
         self.which_device()
@@ -89,19 +100,21 @@ class Trainer(object):
         """
         device = envs.get_global_env(
             "runner." + self._runner_name + ".device", default_value="CPU")
-        if device.upper() == 'GPU':
+        device = device.upper()
+
+        if device == 'GPU':
             self.check_gpu()
             self.device = Device.GPU
             gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
             self._place = fluid.CUDAPlace(gpu_id)
             self._exe = fluid.Executor(self._place)
-        elif device.upper() == "CPU":
+        elif device == "CPU":
             self.device = Device.CPU
             self._place = fluid.CPUPlace()
             self._exe = fluid.Executor(self._place)
         else:
             raise ValueError("Not Support device {}".format(device))
-        self._context["device"] = device.upper()
+        self._context["device"] = device
         self._context["exe"] = self._exe
         self._context["place"] = self._place
 
@@ -119,7 +132,6 @@ class Trainer(object):
         try:
             if not fluid.is_compiled_with_cuda():
                 raise RuntimeError(err)
-                sys.exit(1)
         except Exception as e:
             pass
 
@@ -191,17 +203,10 @@ class Trainer(object):
             None : run a processor for this status
         """
         status = context['status']
-        try:
-            if status in self._status_processor:
-                self._status_processor[context['status']](context)
-            else:
-                self.other_status_processor(context)
-        except Exception as err:
-            traceback.print_exc()
-            print('Catch Exception:%s' % str(err))
-            sys.stdout.flush()
-            self._context['is_exit'] = self.handle_processor_exception(
-                status, context, err)
+        if status in self._status_processor:
+            self._status_processor[context['status']](context)
+        else:
+            self.other_status_processor(context)
 
     def other_status_processor(self, context):
         """
@@ -212,14 +217,17 @@ class Trainer(object):
         print('unknow context_status:%s, do nothing' % context['status'])
         time.sleep(60)
 
-    def handle_processor_exception(self, status, context, exception):
+    def handle_processor_exception(self, context, exception):
         """
         when exception throwed from processor, will call this func to handle it 
         Return:
             bool exit_app or not
         """
-        print('Exit app. catch exception in precoss status:%s, except:%s' %
-              (context['status'], str(exception)))
+        print("\n--------------------------------\nPaddleRec Error Message "
+              "Summary:\n--------------------------------\n")
+        print(
+            'Exit PaddleRec. catch exception in precoss status: [%s], except: %s'
+            % (context['status'], str(exception)))
         return True
 
     def reload_train_context(self):
@@ -233,19 +241,14 @@ class Trainer(object):
         keep running by statu context.
         """
         while True:
-            self.reload_train_context()
-            self.context_process(self._context)
-            if self._context['is_exit']:
-                break
-
-
-def user_define_engine(engine_yaml):
-    _config = envs.load_yaml(engine_yaml)
-    envs.set_runtime_environs(_config)
-    train_location = envs.get_global_env("engine.file")
-    train_dirname = os.path.dirname(train_location)
-    base_name = os.path.splitext(os.path.basename(train_location))[0]
-    sys.path.append(train_dirname)
-    trainer_class = envs.lazy_instance_by_fliename(base_name,
-                                                   "UserDefineTraining")
-    return trainer_class
+            try:
+                self.reload_train_context()
+                self.context_process(self._context)
+                if self._context['is_exit']:
+                    break
+            except Exception as err:
+                traceback.print_exc()
+                print('Catch Exception:%s' % str(err))
+                sys.stdout.flush()
+                self.handle_processor_exception(self._context, err)
+                sys.exit(type(err).__name__)
diff --git a/core/trainers/framework/dataset.py b/core/trainers/framework/dataset.py
index 47122ad38c7806a262dfffc91f364c9fcca1344a..00652e358e9919f85ed9e8938dda8e2122de2fb6 100644
--- a/core/trainers/framework/dataset.py
+++ b/core/trainers/framework/dataset.py
@@ -126,7 +126,7 @@ class QueueDataset(DatasetBase):
             file_list = context["fleet"].split_files(file_list)
 
         dataset.set_filelist(file_list)
-        for model_dict in context["env"]["phase"]:
+        for model_dict in context["phases"]:
             if model_dict["dataset_name"] == dataset_name:
                 model = context["model"][model_dict["name"]]["model"]
                 thread_num = int(model_dict["thread_num"])
diff --git a/core/trainers/framework/network.py b/core/trainers/framework/network.py
index 3660521892f8831847c4922fced150a05f744ca8..71f2a4e7fa6ef671c3a07724183edf2e759aec5e 100644
--- a/core/trainers/framework/network.py
+++ b/core/trainers/framework/network.py
@@ -48,7 +48,7 @@ class SingleNetwork(NetworkBase):
 
     def build_network(self, context):
         context["model"] = {}
-        for model_dict in context["env"]["phase"]:
+        for model_dict in context["phases"]:
             context["model"][model_dict["name"]] = {}
             train_program = fluid.Program()
             startup_program = fluid.Program()
@@ -58,9 +58,8 @@ class SingleNetwork(NetworkBase):
             with fluid.program_guard(train_program, startup_program):
                 with fluid.unique_name.guard():
                     with fluid.scope_guard(scope):
-                        model_path = model_dict["model"].replace(
-                            "{workspace}",
-                            envs.path_adapter(context["env"]["workspace"]))
+                        model_path = envs.os_path_adapter(
+                            envs.workspace_adapter(model_dict["model"]))
                         model = envs.lazy_instance_by_fliename(
                             model_path, "Model")(context["env"])
 
@@ -98,7 +97,8 @@ class SingleNetwork(NetworkBase):
 
         context["dataset"] = {}
         for dataset in context["env"]["dataset"]:
-            if dataset["type"] != "DataLoader":
+            type = envs.get_global_env("dataset." + dataset["name"] + ".type")
+            if type != "DataLoader":
                 dataset_class = QueueDataset(context)
                 context["dataset"][dataset[
                     "name"]] = dataset_class.create_dataset(dataset["name"],
@@ -123,8 +123,8 @@ class PSNetwork(NetworkBase):
         context["model"][model_dict["name"]] = {}
         dataset_name = model_dict["dataset_name"]
 
-        model_path = model_dict["model"].replace(
-            "{workspace}", envs.path_adapter(context["env"]["workspace"]))
+        model_path = envs.os_path_adapter(
+            envs.workspace_adapter(model_dict["model"]))
         model = envs.lazy_instance_by_fliename(model_path,
                                                "Model")(context["env"])
         model._data_var = model.input_data(
@@ -156,7 +156,9 @@ class PSNetwork(NetworkBase):
             context["fleet"].init_worker()
             context["dataset"] = {}
             for dataset in context["env"]["dataset"]:
-                if dataset["type"] != "DataLoader":
+                type = envs.get_global_env("dataset." + dataset["name"] +
+                                           ".type")
+                if type != "DataLoader":
                     dataset_class = QueueDataset(context)
                     context["dataset"][dataset[
                         "name"]] = dataset_class.create_dataset(
@@ -216,10 +218,8 @@ class PslibNetwork(NetworkBase):
             with fluid.unique_name.guard():
                 with fluid.scope_guard(scope):
                     context["model"][model_dict["name"]] = {}
-
-                    model_path = model_dict["model"].replace(
-                        "{workspace}",
-                        envs.path_adapter(context["env"]["workspace"]))
+                    model_path = envs.os_path_adapter(
+                        envs.workspace_adapter(model_dict["model"]))
                     model = envs.lazy_instance_by_fliename(
                         model_path, "Model")(context["env"])
                     model._data_var = model.input_data(
@@ -251,7 +251,9 @@ class PslibNetwork(NetworkBase):
         else:
             context["dataset"] = {}
             for dataset in context["env"]["dataset"]:
-                if dataset["type"] != "DataLoader":
+                type = envs.get_global_env("dataset." + dataset["name"] +
+                                           ".type")
+                if type != "DataLoader":
                     dataset_class = QueueDataset(context)
                     context["dataset"][dataset[
                         "name"]] = dataset_class.create_dataset(
@@ -284,9 +286,9 @@ class CollectiveNetwork(NetworkBase):
         scope = fluid.Scope()
         with fluid.program_guard(train_program, startup_program):
             with fluid.scope_guard(scope):
-                model_path = model_dict["model"].replace(
-                    "{workspace}",
-                    envs.path_adapter(context["env"]["workspace"]))
+                model_path = envs.os_path_adapter(
+                    envs.workspace_adapter(model_dict["model"]))
+
                 model = envs.lazy_instance_by_fliename(model_path,
                                                        "Model")(context["env"])
                 model._data_var = model.input_data(
@@ -315,7 +317,8 @@ class CollectiveNetwork(NetworkBase):
 
         context["dataset"] = {}
         for dataset in context["env"]["dataset"]:
-            if dataset["type"] != "DataLoader":
+            type = envs.get_global_env("dataset." + dataset["name"] + ".type")
+            if type != "DataLoader":
                 dataset_class = QueueDataset(context)
                 context["dataset"][dataset[
                     "name"]] = dataset_class.create_dataset(dataset["name"],
diff --git a/core/trainers/framework/runner.py b/core/trainers/framework/runner.py
index d11a9c0daeb2f6d1a66c5bd757a77c1cea77d582..46a7a1c49ef2e993b9974263efd07b5e81dbae00 100644
--- a/core/trainers/framework/runner.py
+++ b/core/trainers/framework/runner.py
@@ -40,6 +40,7 @@ class RunnerBase(object):
     def _run(self, context, model_dict):
         reader_name = model_dict["dataset_name"]
         name = "dataset." + reader_name + "."
+
         if envs.get_global_env(name + "type") == "DataLoader":
             self._executor_dataloader_train(model_dict, context)
         else:
@@ -156,7 +157,7 @@ class RunnerBase(object):
             gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
         else:
             raise ValueError(
-                "Unsurpported config. gradient_scale_strategy must be one of [0, 1, 2]."
+                "Unsupported config. gradient_scale_strategy must be one of [0, 1, 2]."
             )
         _build_strategy.gradient_scale_strategy = gradient_scale_strategy
 
@@ -285,7 +286,7 @@ class SingleRunner(RunnerBase):
             envs.get_global_env("runner." + context["runner_name"] +
                                 ".epochs"))
         for epoch in range(epochs):
-            for model_dict in context["env"]["phase"]:
+            for model_dict in context["phases"]:
                 begin_time = time.time()
                 self._run(context, model_dict)
                 end_time = time.time()
@@ -384,7 +385,7 @@ class PslibRunner(RunnerBase):
                 day = begin_day + datetime.timedelta(days=day, hours=hour)
                 day_s = day.strftime('%Y%m%d/%H')
 
-                for dataset in context["env"]["dataset"]:
+                for dataset in envs.get_global_env("dataset"):
                     if dataset["type"] != "DataLoader":
                         name = dataset["name"]
                         train_data_path = envs.get_global_env(name +
diff --git a/core/trainers/framework/startup.py b/core/trainers/framework/startup.py
index c96042c8cdcb6badb816d6cd4ee754e8820cee07..82e244723ecae607eb8cfc1110c0a29642bd4615 100644
--- a/core/trainers/framework/startup.py
+++ b/core/trainers/framework/startup.py
@@ -54,7 +54,7 @@ class SingleStartup(StartupBase):
         pass
 
     def startup(self, context):
-        for model_dict in context["env"]["phase"]:
+        for model_dict in context["phases"]:
             with fluid.scope_guard(context["model"][model_dict["name"]][
                     "scope"]):
                 train_prog = context["model"][model_dict["name"]][
diff --git a/core/trainers/general_trainer.py b/core/trainers/general_trainer.py
index eeae63e71eb3c41e549e3e1bdc34c7b42d42cf0c..ded99e1caa903db2eea2c16fa8f811764991f16a 100644
--- a/core/trainers/general_trainer.py
+++ b/core/trainers/general_trainer.py
@@ -17,10 +17,7 @@ General Trainer, applicable to many situations: Single/Cluster/Local_Cluster + P
 from __future__ import print_function
 
 import os
-import time
-import warnings
 
-import paddle.fluid as fluid
 from paddlerec.core.utils import envs
 from paddlerec.core.trainer import Trainer, EngineMode, FleetMode, Device
 from paddlerec.core.trainers.framework.dataset import *
diff --git a/core/trainers/single_infer.py b/core/trainers/single_infer.py
deleted file mode 100755
index 235e101ebbec22032b891c0c3cfe2aacf7c5f79c..0000000000000000000000000000000000000000
--- a/core/trainers/single_infer.py
+++ /dev/null
@@ -1,369 +0,0 @@
-# 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.
-"""
-Training use fluid with one node only.
-"""
-
-from __future__ import print_function
-
-import time
-import logging
-import os
-import json
-import numpy as np
-import paddle.fluid as fluid
-
-from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
-from paddlerec.core.utils import envs
-from paddlerec.core.reader import SlotReader
-from paddlerec.core.utils import dataloader_instance
-
-logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger("fluid")
-logger.setLevel(logging.INFO)
-
-
-class SingleInfer(TranspileTrainer):
-    def __init__(self, config=None):
-        super(TranspileTrainer, self).__init__(config)
-        self._env = self._config
-        device = envs.get_global_env("device")
-        if device == 'gpu':
-            self._place = fluid.CUDAPlace(0)
-        elif device == 'cpu':
-            self._place = fluid.CPUPlace()
-        self._exe = fluid.Executor(self._place)
-        self.processor_register()
-        self._model = {}
-        self._dataset = {}
-        envs.set_global_envs(self._config)
-        envs.update_workspace()
-        self._runner_name = envs.get_global_env("mode")
-        device = envs.get_global_env("runner." + self._runner_name + ".device")
-        if device == 'gpu':
-            self._place = fluid.CUDAPlace(0)
-        elif device == 'cpu':
-            self._place = fluid.CPUPlace()
-        self._exe = fluid.Executor(self._place)
-
-    def processor_register(self):
-        self.regist_context_processor('uninit', self.instance)
-        self.regist_context_processor('init_pass', self.init)
-        self.regist_context_processor('startup_pass', self.startup)
-        self.regist_context_processor('train_pass', self.executor_train)
-        self.regist_context_processor('terminal_pass', self.terminal)
-
-    def instance(self, context):
-        context['status'] = 'init_pass'
-
-    def _get_dataset(self, dataset_name):
-        name = "dataset." + dataset_name + "."
-        thread_num = envs.get_global_env(name + "thread_num")
-        batch_size = envs.get_global_env(name + "batch_size")
-        reader_class = envs.get_global_env(name + "data_converter")
-        abs_dir = os.path.dirname(os.path.abspath(__file__))
-        reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
-        sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
-        dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
-        if sparse_slots == "" and dense_slots == "":
-            pipe_cmd = "python {} {} {} {}".format(reader, reader_class,
-                                                   "TRAIN", self._config_yaml)
-        else:
-            if sparse_slots == "":
-                sparse_slots = "?"
-            if dense_slots == "":
-                dense_slots = "?"
-            padding = envs.get_global_env(name + "padding", 0)
-            pipe_cmd = "python {} {} {} {} {} {} {} {}".format(
-                reader, "slot", "slot", self._config_yaml, "fake", \
-                sparse_slots.replace(" ", "?"), dense_slots.replace(" ", "?"), str(padding))
-
-        dataset = fluid.DatasetFactory().create_dataset()
-        dataset.set_batch_size(envs.get_global_env(name + "batch_size"))
-        dataset.set_pipe_command(pipe_cmd)
-        train_data_path = envs.get_global_env(name + "data_path")
-        file_list = [
-            os.path.join(train_data_path, x)
-            for x in os.listdir(train_data_path)
-        ]
-        dataset.set_filelist(file_list)
-        for model_dict in self._env["phase"]:
-            if model_dict["dataset_name"] == dataset_name:
-                model = self._model[model_dict["name"]][3]
-                inputs = model._infer_data_var
-                dataset.set_use_var(inputs)
-                break
-        return dataset
-
-    def _get_dataloader(self, dataset_name, dataloader):
-        name = "dataset." + dataset_name + "."
-        thread_num = envs.get_global_env(name + "thread_num")
-        batch_size = envs.get_global_env(name + "batch_size")
-        reader_class = envs.get_global_env(name + "data_converter")
-        abs_dir = os.path.dirname(os.path.abspath(__file__))
-        sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
-        dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
-        if sparse_slots == "" and dense_slots == "":
-            reader = dataloader_instance.dataloader_by_name(
-                reader_class, dataset_name, self._config_yaml)
-            reader_class = envs.lazy_instance_by_fliename(reader_class,
-                                                          "TrainReader")
-            reader_ins = reader_class(self._config_yaml)
-        else:
-            reader = dataloader_instance.slotdataloader_by_name(
-                "", dataset_name, self._config_yaml)
-            reader_ins = SlotReader(self._config_yaml)
-        if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
-            dataloader.set_sample_list_generator(reader)
-        else:
-            dataloader.set_sample_generator(reader, batch_size)
-        return dataloader
-
-    def _create_dataset(self, dataset_name):
-        name = "dataset." + dataset_name + "."
-        sparse_slots = envs.get_global_env(name + "sparse_slots")
-        dense_slots = envs.get_global_env(name + "dense_slots")
-        thread_num = envs.get_global_env(name + "thread_num")
-        batch_size = envs.get_global_env(name + "batch_size")
-        type_name = envs.get_global_env(name + "type")
-        if envs.get_platform() != "LINUX":
-            print("platform ", envs.get_platform(),
-                  " change reader to DataLoader")
-            type_name = "DataLoader"
-        padding = 0
-
-        if type_name == "DataLoader":
-            return None
-        else:
-            return self._get_dataset(dataset_name)
-
-    def init(self, context):
-        for model_dict in self._env["phase"]:
-            self._model[model_dict["name"]] = [None] * 5
-            train_program = fluid.Program()
-            startup_program = fluid.Program()
-            scope = fluid.Scope()
-            dataset_name = model_dict["dataset_name"]
-            opt_name = envs.get_global_env("hyper_parameters.optimizer.class")
-            opt_lr = envs.get_global_env(
-                "hyper_parameters.optimizer.learning_rate")
-            opt_strategy = envs.get_global_env(
-                "hyper_parameters.optimizer.strategy")
-            with fluid.program_guard(train_program, startup_program):
-                with fluid.unique_name.guard():
-                    with fluid.scope_guard(scope):
-                        model_path = model_dict["model"].replace(
-                            "{workspace}",
-                            envs.path_adapter(self._env["workspace"]))
-                        model = envs.lazy_instance_by_fliename(
-                            model_path, "Model")(self._env)
-                        model._infer_data_var = model.input_data(
-                            is_infer=True,
-                            dataset_name=model_dict["dataset_name"])
-                        if envs.get_global_env("dataset." + dataset_name +
-                                               ".type") == "DataLoader":
-                            model._init_dataloader(is_infer=True)
-                            self._get_dataloader(dataset_name,
-                                                 model._data_loader)
-                        model.net(model._infer_data_var, True)
-            self._model[model_dict["name"]][0] = train_program
-            self._model[model_dict["name"]][1] = startup_program
-            self._model[model_dict["name"]][2] = scope
-            self._model[model_dict["name"]][3] = model
-            self._model[model_dict["name"]][4] = train_program.clone()
-
-        for dataset in self._env["dataset"]:
-            if dataset["type"] != "DataLoader":
-                self._dataset[dataset["name"]] = self._create_dataset(dataset[
-                    "name"])
-
-        context['status'] = 'startup_pass'
-
-    def startup(self, context):
-        for model_dict in self._env["phase"]:
-            with fluid.scope_guard(self._model[model_dict["name"]][2]):
-                self._exe.run(self._model[model_dict["name"]][1])
-        context['status'] = 'train_pass'
-
-    def executor_train(self, context):
-        epochs = int(
-            envs.get_global_env("runner." + self._runner_name + ".epochs", 1))
-        for j in range(epochs):
-            for model_dict in self._env["phase"]:
-                if j == 0:
-                    with fluid.scope_guard(self._model[model_dict["name"]][2]):
-                        train_prog = self._model[model_dict["name"]][0]
-                        startup_prog = self._model[model_dict["name"]][1]
-                        with fluid.program_guard(train_prog, startup_prog):
-                            self.load()
-                reader_name = model_dict["dataset_name"]
-                name = "dataset." + reader_name + "."
-                begin_time = time.time()
-                if envs.get_global_env(name + "type") == "DataLoader":
-                    self._executor_dataloader_train(model_dict)
-                else:
-                    self._executor_dataset_train(model_dict)
-                with fluid.scope_guard(self._model[model_dict["name"]][2]):
-                    train_prog = self._model[model_dict["name"]][4]
-                    startup_prog = self._model[model_dict["name"]][1]
-                    with fluid.program_guard(train_prog, startup_prog):
-                        self.save(j)
-                end_time = time.time()
-                seconds = end_time - begin_time
-            print("epoch {} done, time elasped: {}".format(j, seconds))
-        context['status'] = "terminal_pass"
-
-    def _executor_dataset_train(self, model_dict):
-        reader_name = model_dict["dataset_name"]
-        model_name = model_dict["name"]
-        model_class = self._model[model_name][3]
-        fetch_vars = []
-        fetch_alias = []
-        fetch_period = int(
-            envs.get_global_env("runner." + self._runner_name +
-                                ".print_interval", 20))
-        metrics = model_class.get_infer_results()
-        if metrics:
-            fetch_vars = metrics.values()
-            fetch_alias = metrics.keys()
-        scope = self._model[model_name][2]
-        program = self._model[model_name][0]
-        reader = self._dataset[reader_name]
-        with fluid.scope_guard(scope):
-            self._exe.infer_from_dataset(
-                program=program,
-                dataset=reader,
-                fetch_list=fetch_vars,
-                fetch_info=fetch_alias,
-                print_period=fetch_period)
-
-    def _executor_dataloader_train(self, model_dict):
-        reader_name = model_dict["dataset_name"]
-        model_name = model_dict["name"]
-        model_class = self._model[model_name][3]
-        program = self._model[model_name][0].clone()
-        fetch_vars = []
-        fetch_alias = []
-        metrics = model_class.get_infer_results()
-        if metrics:
-            fetch_vars = metrics.values()
-            fetch_alias = metrics.keys()
-        metrics_varnames = []
-        metrics_format = []
-        fetch_period = int(
-            envs.get_global_env("runner." + self._runner_name +
-                                ".print_interval", 20))
-        metrics_format.append("{}: {{}}".format("batch"))
-        metrics_indexes = dict()
-        for name, var in metrics.items():
-            metrics_varnames.append(var.name)
-            metrics_indexes[var.name] = len(metrics_varnames) - 1
-            metrics_format.append("{}: {{}}".format(name))
-        metrics_format = ", ".join(metrics_format)
-
-        reader = self._model[model_name][3]._data_loader
-        reader.start()
-        batch_id = 0
-        scope = self._model[model_name][2]
-
-        infer_results = []
-        with fluid.scope_guard(scope):
-            try:
-                while True:
-                    metrics_rets = self._exe.run(program=program,
-                                                 fetch_list=metrics_varnames,
-                                                 return_numpy=False)
-                    metrics = [batch_id]
-                    metrics.extend(metrics_rets)
-
-                    batch_infer_result = {}
-                    for k, v in metrics_indexes.items():
-                        batch_infer_result[k] = np.array(metrics_rets[
-                            v]).tolist()
-                    infer_results.append(batch_infer_result)
-
-                    if batch_id % fetch_period == 0 and batch_id != 0:
-                        print(metrics_format.format(*metrics))
-                    batch_id += 1
-            except fluid.core.EOFException:
-                reader.reset()
-
-    def terminal(self, context):
-        context['is_exit'] = True
-
-    def load(self, is_fleet=False):
-        name = "runner." + self._runner_name + "."
-        dirname = envs.get_global_env(name + "init_model_path", None)
-        if dirname is None or dirname == "":
-            return
-        print("single_infer going to load ", dirname)
-        if is_fleet:
-            fleet.load_persistables(self._exe, dirname)
-        else:
-            fluid.io.load_persistables(self._exe, dirname)
-
-    def save(self, epoch_id, is_fleet=False):
-        def need_save(epoch_id, epoch_interval, is_last=False):
-            if is_last:
-                return True
-            if epoch_id == -1:
-                return False
-
-            return epoch_id % epoch_interval == 0
-
-        def save_inference_model():
-            name = "runner." + self._runner_name + "."
-            save_interval = int(
-                envs.get_global_env(name + "save_inference_interval", -1))
-            if not need_save(epoch_id, save_interval, False):
-                return
-            feed_varnames = envs.get_global_env(
-                name + "save_inference_feed_varnames", None)
-            fetch_varnames = envs.get_global_env(
-                name + "save_inference_fetch_varnames", None)
-            if feed_varnames is None or fetch_varnames is None or feed_varnames == "":
-                return
-            fetch_vars = [
-                fluid.default_main_program().global_block().vars[varname]
-                for varname in fetch_varnames
-            ]
-            dirname = envs.get_global_env(name + "save_inference_path", None)
-
-            assert dirname is not None
-            dirname = os.path.join(dirname, str(epoch_id))
-
-            if is_fleet:
-                fleet.save_inference_model(self._exe, dirname, feed_varnames,
-                                           fetch_vars)
-            else:
-                fluid.io.save_inference_model(dirname, feed_varnames,
-                                              fetch_vars, self._exe)
-
-        def save_persistables():
-            name = "runner." + self._runner_name + "."
-            save_interval = int(
-                envs.get_global_env(name + "save_checkpoint_interval", -1))
-            if not need_save(epoch_id, save_interval, False):
-                return
-            dirname = envs.get_global_env(name + "save_checkpoint_path", None)
-            if dirname is None or dirname == "":
-                return
-            dirname = os.path.join(dirname, str(epoch_id))
-            if is_fleet:
-                fleet.save_persistables(self._exe, dirname)
-            else:
-                fluid.io.save_persistables(self._exe, dirname)
-
-        save_persistables()
-        save_inference_model()
diff --git a/core/trainers/single_trainer.py b/core/trainers/single_trainer.py
deleted file mode 100755
index d782dfa42f5695c8b04e9e2db6ba92b676b8b0f6..0000000000000000000000000000000000000000
--- a/core/trainers/single_trainer.py
+++ /dev/null
@@ -1,391 +0,0 @@
-# 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.
-"""
-Training use fluid with one node only.
-"""
-
-from __future__ import print_function
-
-import time
-import logging
-import os
-import paddle.fluid as fluid
-
-from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
-from paddlerec.core.utils import envs
-from paddlerec.core.reader import SlotReader
-from paddlerec.core.utils import dataloader_instance
-
-logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger("fluid")
-logger.setLevel(logging.INFO)
-
-
-class SingleTrainer(TranspileTrainer):
-    def __init__(self, config=None):
-        super(TranspileTrainer, self).__init__(config)
-        self._env = self._config
-        self.processor_register()
-        self._model = {}
-        self._dataset = {}
-        envs.set_global_envs(self._config)
-        envs.update_workspace()
-        self._runner_name = envs.get_global_env("mode")
-        device = envs.get_global_env("runner." + self._runner_name + ".device")
-        if device == 'gpu':
-            self._place = fluid.CUDAPlace(0)
-        elif device == 'cpu':
-            self._place = fluid.CPUPlace()
-        self._exe = fluid.Executor(self._place)
-
-    def processor_register(self):
-        self.regist_context_processor('uninit', self.instance)
-        self.regist_context_processor('init_pass', self.init)
-        self.regist_context_processor('startup_pass', self.startup)
-        self.regist_context_processor('train_pass', self.executor_train)
-        self.regist_context_processor('terminal_pass', self.terminal)
-
-    def instance(self, context):
-        context['status'] = 'init_pass'
-
-    def _get_dataset(self, dataset_name):
-        name = "dataset." + dataset_name + "."
-        thread_num = envs.get_global_env(name + "thread_num")
-        batch_size = envs.get_global_env(name + "batch_size")
-        reader_class = envs.get_global_env(name + "data_converter")
-        abs_dir = os.path.dirname(os.path.abspath(__file__))
-        reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
-        sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
-        dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
-        if sparse_slots == "" and dense_slots == "":
-            pipe_cmd = "python {} {} {} {}".format(reader, reader_class,
-                                                   "TRAIN", self._config_yaml)
-        else:
-            if sparse_slots == "":
-                sparse_slots = "?"
-            if dense_slots == "":
-                dense_slots = "?"
-            padding = envs.get_global_env(name + "padding", 0)
-            pipe_cmd = "python {} {} {} {} {} {} {} {}".format(
-                reader, "slot", "slot", self._config_yaml, "fake", \
-                sparse_slots.replace(" ", "?"), dense_slots.replace(" ", "?"), str(padding))
-
-        dataset = fluid.DatasetFactory().create_dataset()
-        dataset.set_batch_size(envs.get_global_env(name + "batch_size"))
-        dataset.set_pipe_command(pipe_cmd)
-        train_data_path = envs.get_global_env(name + "data_path")
-        file_list = [
-            os.path.join(train_data_path, x)
-            for x in os.listdir(train_data_path)
-        ]
-        dataset.set_filelist(file_list)
-        for model_dict in self._env["phase"]:
-            if model_dict["dataset_name"] == dataset_name:
-                model = self._model[model_dict["name"]][3]
-                inputs = model._data_var
-                dataset.set_use_var(inputs)
-                break
-        return dataset
-
-    def _get_dataloader(self, dataset_name, dataloader):
-        name = "dataset." + dataset_name + "."
-        sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
-        dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
-        thread_num = envs.get_global_env(name + "thread_num")
-        batch_size = envs.get_global_env(name + "batch_size")
-        reader_class = envs.get_global_env(name + "data_converter")
-        abs_dir = os.path.dirname(os.path.abspath(__file__))
-        if sparse_slots == "" and dense_slots == "":
-            reader = dataloader_instance.dataloader_by_name(
-                reader_class, dataset_name, self._config_yaml)
-            reader_class = envs.lazy_instance_by_fliename(reader_class,
-                                                          "TrainReader")
-            reader_ins = reader_class(self._config_yaml)
-        else:
-            reader = dataloader_instance.slotdataloader_by_name(
-                "", dataset_name, self._config_yaml)
-            reader_ins = SlotReader(self._config_yaml)
-        if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
-            dataloader.set_sample_list_generator(reader)
-        else:
-            dataloader.set_sample_generator(reader, batch_size)
-        return dataloader
-
-    def _create_dataset(self, dataset_name):
-        name = "dataset." + dataset_name + "."
-        sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
-        dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
-        thread_num = envs.get_global_env(name + "thread_num")
-        batch_size = envs.get_global_env(name + "batch_size")
-        type_name = envs.get_global_env(name + "type")
-        if envs.get_platform() != "LINUX":
-            print("platform ", envs.get_platform(),
-                  " change reader to DataLoader")
-            type_name = "DataLoader"
-        padding = 0
-
-        if type_name == "DataLoader":
-            return None
-        else:
-            return self._get_dataset(dataset_name)
-
-    def init(self, context):
-        for model_dict in self._env["phase"]:
-            self._model[model_dict["name"]] = [None] * 5
-            train_program = fluid.Program()
-            startup_program = fluid.Program()
-            scope = fluid.Scope()
-            dataset_name = model_dict["dataset_name"]
-            with fluid.program_guard(train_program, startup_program):
-                with fluid.unique_name.guard():
-                    with fluid.scope_guard(scope):
-                        model_path = model_dict["model"].replace(
-                            "{workspace}",
-                            envs.path_adapter(self._env["workspace"]))
-                        model = envs.lazy_instance_by_fliename(
-                            model_path, "Model")(self._env)
-                        model._data_var = model.input_data(
-                            dataset_name=model_dict["dataset_name"])
-                        if envs.get_global_env("dataset." + dataset_name +
-                                               ".type") == "DataLoader":
-                            model._init_dataloader(is_infer=False)
-                            self._get_dataloader(dataset_name,
-                                                 model._data_loader)
-                        model.net(model._data_var, False)
-                        optimizer = model.optimizer()
-                        optimizer.minimize(model._cost)
-            self._model[model_dict["name"]][0] = train_program
-            self._model[model_dict["name"]][1] = startup_program
-            self._model[model_dict["name"]][2] = scope
-            self._model[model_dict["name"]][3] = model
-            self._model[model_dict["name"]][4] = train_program.clone()
-
-        for dataset in self._env["dataset"]:
-            if dataset["type"] != "DataLoader":
-                self._dataset[dataset["name"]] = self._create_dataset(dataset[
-                    "name"])
-
-        context['status'] = 'startup_pass'
-
-    def startup(self, context):
-        for model_dict in self._env["phase"]:
-            with fluid.scope_guard(self._model[model_dict["name"]][2]):
-                self._exe.run(self._model[model_dict["name"]][1])
-        context['status'] = 'train_pass'
-
-    def executor_train(self, context):
-        epochs = int(
-            envs.get_global_env("runner." + self._runner_name + ".epochs"))
-        for j in range(epochs):
-            for model_dict in self._env["phase"]:
-                if j == 0:
-                    with fluid.scope_guard(self._model[model_dict["name"]][2]):
-                        train_prog = self._model[model_dict["name"]][0]
-                        startup_prog = self._model[model_dict["name"]][1]
-                        with fluid.program_guard(train_prog, startup_prog):
-                            self.load()
-                reader_name = model_dict["dataset_name"]
-                name = "dataset." + reader_name + "."
-                begin_time = time.time()
-                if envs.get_global_env(name + "type") == "DataLoader":
-                    self._executor_dataloader_train(model_dict)
-                else:
-                    self._executor_dataset_train(model_dict)
-                with fluid.scope_guard(self._model[model_dict["name"]][2]):
-                    train_prog = self._model[model_dict["name"]][4]
-                    startup_prog = self._model[model_dict["name"]][1]
-                    with fluid.program_guard(train_prog, startup_prog):
-                        self.save(j)
-                end_time = time.time()
-                seconds = end_time - begin_time
-            print("epoch {} done, time elasped: {}".format(j, seconds))
-        context['status'] = "terminal_pass"
-
-    def _executor_dataset_train(self, model_dict):
-        reader_name = model_dict["dataset_name"]
-        model_name = model_dict["name"]
-        model_class = self._model[model_name][3]
-        fetch_vars = []
-        fetch_alias = []
-        fetch_period = int(
-            envs.get_global_env("runner." + self._runner_name +
-                                ".print_interval", 20))
-        metrics = model_class.get_metrics()
-        if metrics:
-            fetch_vars = metrics.values()
-            fetch_alias = metrics.keys()
-        scope = self._model[model_name][2]
-        program = self._model[model_name][0]
-        reader = self._dataset[reader_name]
-        threads = model_dict.get("thread_num", 1)
-        with fluid.scope_guard(scope):
-            self._exe.train_from_dataset(
-                program=program,
-                dataset=reader,
-                thread=threads,
-                fetch_list=fetch_vars,
-                fetch_info=fetch_alias,
-                print_period=fetch_period)
-
-    def _executor_dataloader_train(self, model_dict):
-        reader_name = model_dict["dataset_name"]
-        model_name = model_dict["name"]
-        model_class = self._model[model_name][3]
-        program = self._model[model_name][0].clone()
-
-        _build_strategy = fluid.BuildStrategy()
-        _exe_strategy = fluid.ExecutionStrategy()
-
-        # 0: kCoeffNumDevice; 1: One; 2: Customized
-        _gradient_scale_strategy = model_dict.get("gradient_scale_strategy", 0)
-        if _gradient_scale_strategy == 0:
-            gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.CoeffNumDevice
-        elif _gradient_scale_strategy == 1:
-            gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.One
-        elif _gradient_scale_strategy == 2:
-            gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
-        else:
-            raise ValueError(
-                "Unsurpported config. gradient_scale_strategy must be one of [0, 1, 2]."
-            )
-        _build_strategy.gradient_scale_strategy = gradient_scale_strategy
-
-        if "thread_num" in model_dict and model_dict["thread_num"] > 1:
-            _build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
-            _exe_strategy.num_threads = model_dict["thread_num"]
-            os.environ['CPU_NUM'] = str(_exe_strategy.num_threads)
-
-        program = fluid.compiler.CompiledProgram(program).with_data_parallel(
-            loss_name=model_class.get_avg_cost().name,
-            build_strategy=_build_strategy,
-            exec_strategy=_exe_strategy)
-
-        fetch_vars = []
-        fetch_alias = []
-        fetch_period = int(
-            envs.get_global_env("runner." + self._runner_name +
-                                ".print_interval", 20))
-        metrics = model_class.get_metrics()
-        if metrics:
-            fetch_vars = metrics.values()
-            fetch_alias = metrics.keys()
-        metrics_varnames = []
-        metrics_format = []
-        metrics_format.append("{}: {{}}".format("batch"))
-        for name, var in metrics.items():
-            metrics_varnames.append(var.name)
-            metrics_format.append("{}: {{}}".format(name))
-        metrics_format = ", ".join(metrics_format)
-
-        reader = self._model[model_name][3]._data_loader
-        reader.start()
-        batch_id = 0
-        scope = self._model[model_name][2]
-        with fluid.scope_guard(scope):
-            try:
-                while True:
-                    metrics_rets = self._exe.run(program=program,
-                                                 fetch_list=metrics_varnames)
-                    metrics = [batch_id]
-                    metrics.extend(metrics_rets)
-
-                    if batch_id % fetch_period == 0 and batch_id != 0:
-                        print(metrics_format.format(*metrics))
-                    batch_id += 1
-            except fluid.core.EOFException:
-                reader.reset()
-
-    def terminal(self, context):
-        context['is_exit'] = True
-
-    def load(self, is_fleet=False):
-        dirname = envs.get_global_env(
-            "runner." + self._runner_name + ".init_model_path", None)
-        load_vars = envs.get_global_env(
-            "runner." + self._runner_name + ".load_vars", None)
-
-        def name_has_embedding(var):
-            res = False
-            for var_name in load_vars:
-                if var_name == var.name:
-                    return True
-            return res
-
-        if dirname is None or dirname == "":
-            return
-        print("going to load ", dirname)
-        if is_fleet:
-            fleet.load_persistables(self._exe, dirname)
-        else:
-            if load_vars is None or len(load_vars) == 0:
-                fluid.io.load_persistables(self._exe, dirname)
-            else:
-                fluid.io.load_vars(
-                    self._exe, dirname, predicate=name_has_embedding)
-
-    def save(self, epoch_id, is_fleet=False):
-        def need_save(epoch_id, epoch_interval, is_last=False):
-            if is_last:
-                return True
-            if epoch_id == -1:
-                return False
-
-            return epoch_id % epoch_interval == 0
-
-        def save_inference_model():
-            name = "runner." + self._runner_name + "."
-            save_interval = int(
-                envs.get_global_env(name + "save_inference_interval", -1))
-            if not need_save(epoch_id, save_interval, False):
-                return
-            feed_varnames = envs.get_global_env(
-                name + "save_inference_feed_varnames", [])
-            fetch_varnames = envs.get_global_env(
-                name + "save_inference_fetch_varnames", [])
-            if feed_varnames is None or fetch_varnames is None or feed_varnames == "" or fetch_varnames == "" or \
-               len(feed_varnames) == 0 or len(fetch_varnames) == 0:
-                return
-            fetch_vars = [
-                fluid.default_main_program().global_block().vars[varname]
-                for varname in fetch_varnames
-            ]
-            dirname = envs.get_global_env(name + "save_inference_path", None)
-
-            assert dirname is not None
-            dirname = os.path.join(dirname, str(epoch_id))
-
-            if is_fleet:
-                fleet.save_inference_model(self._exe, dirname, feed_varnames,
-                                           fetch_vars)
-            else:
-                fluid.io.save_inference_model(dirname, feed_varnames,
-                                              fetch_vars, self._exe)
-
-        def save_persistables():
-            name = "runner." + self._runner_name + "."
-            save_interval = int(
-                envs.get_global_env(name + "save_checkpoint_interval", -1))
-            if not need_save(epoch_id, save_interval, False):
-                return
-            dirname = envs.get_global_env(name + "save_checkpoint_path", None)
-            if dirname is None or dirname == "":
-                return
-            dirname = os.path.join(dirname, str(epoch_id))
-            if is_fleet:
-                fleet.save_persistables(self._exe, dirname)
-            else:
-                fluid.io.save_persistables(self._exe, dirname)
-
-        save_persistables()
-        save_inference_model()
diff --git a/core/utils/envs.py b/core/utils/envs.py
index bbb2a824e6d3c5268d6ae17caed99410f714f353..36993a93a9ea1f83f1cd24fc80a1b9623b61b445 100755
--- a/core/utils/envs.py
+++ b/core/utils/envs.py
@@ -20,9 +20,8 @@ import socket
 import sys
 import traceback
 
-import yaml
-
 global_envs = {}
+global_envs_flatten = {}
 
 
 def flatten_environs(envs, separator="."):
@@ -92,6 +91,16 @@ def set_global_envs(envs):
 
     fatten_env_namespace([], envs)
 
+    for name, value in global_envs.items():
+        if isinstance(value, str):
+            value = os_path_adapter(workspace_adapter(value))
+            global_envs[name] = value
+
+    if get_platform() != "LINUX":
+        for dataset in envs["dataset"]:
+            name = ".".join(["dataset", dataset["name"], "type"])
+            global_envs[name] = "DataLoader"
+
 
 def get_global_env(env_name, default_value=None, namespace=None):
     """
@@ -106,7 +115,7 @@ def get_global_envs():
     return global_envs
 
 
-def path_adapter(path):
+def paddlerec_adapter(path):
     if path.startswith("paddlerec."):
         package = get_runtime_environ("PACKAGE_BASE")
         l_p = path.split("paddlerec.")[1].replace(".", "/")
@@ -115,24 +124,28 @@ def path_adapter(path):
         return path
 
 
-def windows_path_converter(path):
+def os_path_adapter(value):
     if get_platform() == "WINDOWS":
-        return path.replace("/", "\\")
+        value = value.replace("/", "\\")
     else:
-        return path.replace("\\", "/")
+        value = value.replace("\\", "/")
+    return value
 
 
-def update_workspace():
+def workspace_adapter(value):
     workspace = global_envs.get("workspace")
-    if not workspace:
+    workspace = paddlerec_adapter(workspace)
+    value = value.replace("{workspace}", workspace)
+    return value
+
+
+def reader_adapter():
+    if get_platform() != "WINDOWS":
         return
-    workspace = path_adapter(workspace)
 
-    for name, value in global_envs.items():
-        if isinstance(value, str):
-            value = value.replace("{workspace}", workspace)
-            value = windows_path_converter(value)
-            global_envs[name] = value
+    datasets = global_envs.get("dataset")
+    for dataset in datasets:
+        dataset["type"] = "DataLoader"
 
 
 def pretty_print_envs(envs, header=None):
diff --git a/doc/custom_reader.md b/doc/custom_reader.md
index bf0e5433b06c90dab7440b7dbf19eaa6ef975d1c..748f82c5499721857a011b08076af1b732914cc0 100644
--- a/doc/custom_reader.md
+++ b/doc/custom_reader.md
@@ -208,7 +208,7 @@ CTR-DNN训练及测试数据集选用[Display Advertising Challenge](https://www
 稀疏参数输入的定义:
 ```python
 def sparse_inputs():
-    ids = envs.get_global_env("hyper_parameters.sparse_inputs_slots", None, self._namespace)
+    ids = envs.get_global_env("hyper_parameters.sparse_inputs_slots", None)
 
     sparse_input_ids = [
         fluid.layers.data(name="S" + str(i),
@@ -222,7 +222,7 @@ def sparse_inputs():
 稠密参数输入的定义：
 ```python
 def dense_input():
-    dim = envs.get_global_env("hyper_parameters.dense_input_dim", None, self._namespace)
+    dim = envs.get_global_env("hyper_parameters.dense_input_dim", None)
 
     dense_input_var = fluid.layers.data(name="D",
                                         shape=[dim],
diff --git a/doc/design.md b/doc/design.md
index c09889dc437e001d617625c93e6eac2211bd1072..2a9d94533d2c40bb2f96b97fd2873364ee62f0cd 100644
--- a/doc/design.md
+++ b/doc/design.md
@@ -111,7 +111,7 @@ Engine的自定义实现，可以参考[local_cluster.py](../core/engine/local_c
 我们以GeneralTrainer为例，概览Trainer行为：
 
 ```python 
-class SingleTrainer(TranspileTrainer):
+class GeneralTrainer(Trainer):
     def processor_register(self):
         print("processor_register begin")
         self.regist_context_processor('uninit', self.instance)
@@ -151,7 +151,6 @@ class Model(object):
         self._data_loader = None
         self._infer_data_loader = None
         self._fetch_interval = 20
-        self._namespace = "train.model"
         self._platform = envs.get_platform()
 
     def get_inputs(self):
@@ -211,7 +210,6 @@ class Reader(dg.MultiSlotDataGenerator):
         dg.MultiSlotDataGenerator.__init__(self)
         _config = envs.load_yaml(config)
         envs.set_global_envs(_config)
-        envs.update_workspace()
 
     @abc.abstractmethod
     def init(self):
@@ -231,9 +229,6 @@ class Reader(dg.MultiSlotDataGenerator):
 
 完成reader的构建工作。
 
-Reader数据处理的逻辑，可以参考[criteo_reader.py](../../models/rank/../../paddlerec/models/rank/criteo_reader.py)
-
-
 
 ## Metric
 
diff --git a/doc/model.md b/doc/model.md
index ca76dd360c9c9740b1d607e78682c80aded4898e..d4c09733bc2489bafb811d74489f6e6556788e9e 100644
--- a/doc/model.md
+++ b/doc/model.md
@@ -24,8 +24,7 @@ hyper_parameters:
 
 ```python
 if name == "SGD":
-    reg = envs.get_global_env("hyper_parameters.reg", 0.0001,
-                                self._namespace)
+    reg = envs.get_global_env("hyper_parameters.reg", 0.0001)
     optimizer_i = fluid.optimizer.SGD(
         lr, regularization=fluid.regularizer.L2DecayRegularizer(reg))
 elif name == "ADAM":
diff --git a/doc/predict.md b/doc/predict.md
index 4957713d4a9973c910ea7ce9bdb156589fa730ae..c26fdf9ab438dc14369a07d55e101edaade7eda7 100644
--- a/doc/predict.md
+++ b/doc/predict.md
@@ -13,6 +13,7 @@ runner:
   device: cpu # 执行在 cpu 上
   init_model_path: "init_model" # 指定初始化模型的地址
   print_interval: 10 # 预测信息的打印间隔，以batch为单位
+  phases: phase_infer
 ```
 
 再定义具体的执行内容：
diff --git a/doc/train.md b/doc/train.md
index 3987bdc5fe583af3217e508dcdf0a5d6d5f7dbb7..e629eb628521e0cd6012348db7222517218690d6 100644
--- a/doc/train.md
+++ b/doc/train.md
@@ -65,7 +65,7 @@ python -m paddlerec.run -m paddlerec.models.recall.word2vec
 - **`runner`** : runner是训练的引擎，亦可称之为运行器，在runner中定义执行设备（cpu、gpu），执行的模式（训练、预测、单机、多机等），以及运行的超参，例如训练轮数，模型保存地址等。
 - **`phase`** : phase是训练中的阶段的概念，是引擎具体执行的内容，该内容是指：具体运行哪个模型文件，使用哪个reader。
 
-PaddleRec每次运行时，会执行一个运行器，通过`mode`指定`runner`的名字。每个运行器可以执行多个`phase`，所以PaddleRec支持一键启动多阶段的训练。
+PaddleRec每次运行时，会执行一个或多个运行器，通过`mode`指定`runner`的名字。每个运行器可以执行一个或多个`phase`，所以PaddleRec支持一键启动多阶段的训练。
 
 ### 单机CPU训练
 
@@ -73,6 +73,7 @@ PaddleRec每次运行时，会执行一个运行器，通过`mode`指定`runner`
 
 ```yaml
 mode: single_cpu_train # 执行名为 single_cpu_train 的运行器
+# mode 也支持多个runner的执行，此处可以改为 mode: [single_cpu_train, single_cpu_infer]
 
 runner:
 - name: single_cpu_train # 定义 runner 名为 single_cpu_train
@@ -88,6 +89,8 @@ runner:
   save_inference_fetch_varnames: [] # inference model 的fetch参数的名字
   init_model_path: "" # 如果是加载模型热启，则可以指定初始化模型的地址
   print_interval: 10 # 训练信息的打印间隔，以batch为单位
+  phases: [phase_train] # 若没有指定phases，则会默认运行所有phase
+  # phase 也支持自定多个phase的执行，此处可以改为 phases: [phase_train, phase_infer]
 ```
 
 再定义具体的执行内容：
diff --git a/models/rank/dataset/Criteo_data/get_slot_data.py b/models/rank/dataset/Criteo_data/get_slot_data.py
index 5d77ec0cd71b22331b96d8e4ac7d75c5b9c29c8a..b9a4adc3feff53499f4ff190d4a321bc58cf2033 100644
--- a/models/rank/dataset/Criteo_data/get_slot_data.py
+++ b/models/rank/dataset/Criteo_data/get_slot_data.py
@@ -12,10 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import yaml
-import os
-
-from paddlerec.core.reader import ReaderBase
 from paddlerec.core.utils import envs
 import paddle.fluid.incubate.data_generator as dg
 try:
@@ -27,12 +23,7 @@ except ImportError:
 class Reader(dg.MultiSlotDataGenerator):
     def __init__(self, config):
         dg.MultiSlotDataGenerator.__init__(self)
-
-        if os.path.isfile(config):
-            with open(config, 'r') as rb:
-                _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
-        else:
-            raise ValueError("reader config only support yaml")
+        _config = envs.load_yaml(config)
 
     def init(self):
         self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
diff --git a/models/rank/dnn/config.yaml b/models/rank/dnn/config.yaml
index 603e6399f0971e11be9fc5c42a2e216f27973389..539cbb00fbb83197b120238a584ae13d348cc49e 100755
--- a/models/rank/dnn/config.yaml
+++ b/models/rank/dnn/config.yaml
@@ -51,7 +51,7 @@ hyper_parameters:
   fc_sizes: [512, 256, 128, 32]
 
 # select runner by name
-mode: single_cpu_train
+mode: [single_cpu_train, single_cpu_infer]
 # config of each runner.
 # runner is a kind of paddle training class, which wraps the train/infer process.
 runner:
@@ -69,21 +69,8 @@ runner:
   save_inference_fetch_varnames: [] # fetch vars of save inference
   init_model_path: "" # load model path
   print_interval: 10
-- name: single_gpu_train
-  class: train
-  # num of epochs
-  epochs: 4
-  # device to run training or infer
-  device: gpu
-  selected_gpus: "2"
-  save_checkpoint_interval: 2 # save model interval of epochs
-  save_inference_interval: 4 # save inference
-  save_checkpoint_path: "increment" # save checkpoint path
-  save_inference_path: "inference" # save inference path
-  save_inference_feed_varnames: [] # feed vars of save inference
-  save_inference_fetch_varnames: [] # fetch vars of save inference
-  init_model_path: "" # load model path
-  print_interval: 10
+  phases: [phase1]
+
 - name: single_cpu_infer
   class: infer
   # num of epochs
@@ -91,31 +78,7 @@ runner:
   # device to run training or infer
   device: cpu
   init_model_path: "increment/0" # load model path
-- name: local_cluster_cpu_ps_train
-  class: local_cluster
-  epochs: 4
-  device: cpu
-  save_checkpoint_interval: 2 # save model interval of epochs
-  save_inference_interval: 4 # save inference
-  save_checkpoint_path: "increment" # save checkpoint path
-  save_inference_path: "inference" # save inference path
-  save_inference_feed_varnames: [] # feed vars of save inference
-  save_inference_fetch_varnames: [] # fetch vars of save inference
-  init_model_path: "" # load model path
-  print_interval: 1
-- name: multi_gpu_train
-  class: train
-  epochs: 4
-  device: gpu
-  selected_gpus: "2,3"
-  save_checkpoint_interval: 2 # save model interval of epochs
-  save_inference_interval: 4 # save inference
-  save_checkpoint_path: "increment" # save checkpoint path
-  save_inference_path: "inference" # save inference path
-  save_inference_feed_varnames: [] # feed vars of save inference
-  save_inference_fetch_varnames: [] # fetch vars of save inference
-  init_model_path: "" # load model path
-  print_interval: 10
+  phases: [phase2]
 
 # runner will run all the phase in each epoch
 phase:
@@ -123,7 +86,8 @@ phase:
   model: "{workspace}/model.py" # user-defined model
   dataset_name: dataloader_train # select dataset by name
   thread_num: 1
-#- name: phase2
-#  model: "{workspace}/model.py" # user-defined model
-#  dataset_name: dataset_infer # select dataset by name
-#  thread_num: 1
+
+- name: phase2
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataset_infer # select dataset by name
+  thread_num: 1
diff --git a/models/rank/logistic_regression/data/get_slot_data.py b/models/rank/logistic_regression/data/get_slot_data.py
index 1a40a9bfae046e5f53e8f0d396c2fc2cbf193929..f0b95a79c70d7c9da8b327884c17206e6b76f731 100644
--- a/models/rank/logistic_regression/data/get_slot_data.py
+++ b/models/rank/logistic_regression/data/get_slot_data.py
@@ -12,10 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import yaml
-import os
-
-from paddlerec.core.reader import ReaderBase
 from paddlerec.core.utils import envs
 import paddle.fluid.incubate.data_generator as dg
 
@@ -28,12 +24,7 @@ except ImportError:
 class Reader(dg.MultiSlotDataGenerator):
     def __init__(self, config):
         dg.MultiSlotDataGenerator.__init__(self)
-
-        if os.path.isfile(config):
-            with open(config, 'r') as rb:
-                _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
-        else:
-            raise ValueError("reader config only support yaml")
+        _config = envs.load_yaml(config)
 
     def init(self):
         self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
diff --git a/models/rank/nfm/data/get_slot_data.py b/models/rank/nfm/data/get_slot_data.py
index 5d77ec0cd71b22331b96d8e4ac7d75c5b9c29c8a..b9a4adc3feff53499f4ff190d4a321bc58cf2033 100644
--- a/models/rank/nfm/data/get_slot_data.py
+++ b/models/rank/nfm/data/get_slot_data.py
@@ -12,10 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import yaml
-import os
-
-from paddlerec.core.reader import ReaderBase
 from paddlerec.core.utils import envs
 import paddle.fluid.incubate.data_generator as dg
 try:
@@ -27,12 +23,7 @@ except ImportError:
 class Reader(dg.MultiSlotDataGenerator):
     def __init__(self, config):
         dg.MultiSlotDataGenerator.__init__(self)
-
-        if os.path.isfile(config):
-            with open(config, 'r') as rb:
-                _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
-        else:
-            raise ValueError("reader config only support yaml")
+        _config = envs.load_yaml(config)
 
     def init(self):
         self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
diff --git a/run.py b/run.py
index 23a55d581714cf9796d047ad1d70bc63a47afb13..881abcc6ae6bc8022d76b9672a9fabb424ebcebf 100755
--- a/run.py
+++ b/run.py
@@ -18,11 +18,9 @@ import sys
 import argparse
 import tempfile
 
-import yaml
 import copy
 from paddlerec.core.factory import TrainerFactory
 from paddlerec.core.utils import envs
-from paddlerec.core.utils import validation
 from paddlerec.core.utils import util
 from paddlerec.core.utils import validation
 
@@ -96,35 +94,61 @@ def get_all_inters_from_yaml(file, filters):
     return ret
 
 
-def get_engine(args):
+def get_modes(running_config):
+    if not isinstance(running_config, dict):
+        raise ValueError("get_modes arguments must be [dict]")
+
+    modes = running_config.get("mode")
+    if not modes:
+        raise ValueError("yaml mast have config: mode")
+
+    if isinstance(modes, str):
+        modes = [modes]
+
+    return modes
+
+
+def get_engine(args, running_config, mode):
     transpiler = get_transpiler()
-    with open(args.model, 'r') as rb:
-        _envs = yaml.load(rb.read(), Loader=yaml.FullLoader)
-    run_extras = get_all_inters_from_yaml(args.model, ["train.", "runner."])
 
-    engine = run_extras.get("train.engine", None)
-    if engine is None:
-        engine = run_extras.get("runner." + _envs["mode"] + ".class", None)
+    engine_class = ".".join(["runner", mode, "class"])
+    engine_device = ".".join(["runner", mode, "device"])
+    device_gpu_choices = ".".join(["runner", mode, "device", "selected_gpus"])
+
+    engine = running_config.get(engine_class, None)
     if engine is None:
-        engine = "train"
+        raise ValueError("not find {} in yaml, please check".format(
+            mode, engine_class))
+    device = running_config.get(engine_device, None)
+
+    engine = engine.upper()
+    device = device.upper()
+
+    if device is None:
+        print("not find device be specified in yaml, set CPU as default")
+        device = "CPU"
+
+    if device == "GPU":
+        selected_gpus = running_config.get(device_gpu_choices, None)
+
+        if selected_gpus is None:
+            print(
+                "not find selected_gpus be specified in yaml, set `0` as default"
+            )
+            selected_gpus = ["0"]
+        else:
+            print("selected_gpus {} will be specified for running".format(
+                selected_gpus))
 
-    device = run_extras.get("runner." + _envs["mode"] + ".device", "CPU")
-    if device.upper() == "GPU":
-        selected_gpus = run_extras.get(
-            "runner." + _envs["mode"] + ".selected_gpus", "0")
         selected_gpus_num = len(selected_gpus.split(","))
         if selected_gpus_num > 1:
             engine = "LOCAL_CLUSTER"
 
-    engine = engine.upper()
     if engine not in engine_choices:
-        raise ValueError("runner.class can not be chosen in {}".format(
-            engine_choices))
-
-    print("engines: \n{}".format(engines))
+        raise ValueError("{} can not be chosen in {}".format(engine_class,
+                                                             engine_choices))
 
     run_engine = engines[transpiler].get(engine, None)
-
     return run_engine
 
 
@@ -146,12 +170,7 @@ def set_runtime_envs(cluster_envs, engine_yaml):
     if cluster_envs is None:
         cluster_envs = {}
 
-    engine_extras = get_inters_from_yaml(engine_yaml, "train.trainer.")
-    if "train.trainer.threads" in engine_extras and "CPU_NUM" in cluster_envs:
-        cluster_envs["CPU_NUM"] = engine_extras["train.trainer.threads"]
-
     envs.set_runtime_environs(cluster_envs)
-    envs.set_runtime_environs(engine_extras)
 
     need_print = {}
     for k, v in os.environ.items():
@@ -162,29 +181,31 @@ def set_runtime_envs(cluster_envs, engine_yaml):
 
 
 def single_train_engine(args):
-    _envs = envs.load_yaml(args.model)
-    run_extras = get_all_inters_from_yaml(args.model, ["train.", "runner."])
-    trainer_class = run_extras.get(
-        "runner." + _envs["mode"] + ".trainer_class", None)
+    run_extras = get_all_inters_from_yaml(args.model, ["runner."])
+    mode = envs.get_runtime_environ("mode")
+    trainer_class = ".".join(["runner", mode, "trainer_class"])
+    fleet_class = ".".join(["runner", mode, "fleet_mode"])
+    device_class = ".".join(["runner", mode, "device"])
+    selected_gpus_class = ".".join(["runner", mode, "selected_gpus"])
+
+    trainer = run_extras.get(trainer_class, "GeneralTrainer")
+    fleet_mode = run_extras.get(fleet_class, "ps")
+    device = run_extras.get(device_class, "cpu")
+    selected_gpus = run_extras.get(selected_gpus_class, "0")
+    executor_mode = "train"
 
-    if trainer_class:
-        trainer = trainer_class
-    else:
-        trainer = "GeneralTrainer"
+    single_envs = {}
 
-    executor_mode = "train"
-    fleet_mode = run_extras.get("runner." + _envs["mode"] + ".fleet_mode",
-                                "ps")
-    device = run_extras.get("runner." + _envs["mode"] + ".device", "cpu")
-    selected_gpus = run_extras.get(
-        "runner." + _envs["mode"] + ".selected_gpus", "0")
-    selected_gpus_num = len(selected_gpus.split(","))
     if device.upper() == "GPU":
-        assert selected_gpus_num == 1, "Single Mode Only Support One GPU, Set Local Cluster Mode to use Multi-GPUS"
+        selected_gpus_num = len(selected_gpus.split(","))
+        if selected_gpus_num != 1:
+            raise ValueError(
+                "Single Mode Only Support One GPU, Set Local Cluster Mode to use Multi-GPUS"
+            )
+
+        single_envs["selsected_gpus"] = selected_gpus
+        single_envs["FLAGS_selected_gpus"] = selected_gpus
 
-    single_envs = {}
-    single_envs["selsected_gpus"] = selected_gpus
-    single_envs["FLAGS_selected_gpus"] = selected_gpus
     single_envs["train.trainer.trainer"] = trainer
     single_envs["fleet_mode"] = fleet_mode
     single_envs["train.trainer.executor_mode"] = executor_mode
@@ -199,29 +220,32 @@ def single_train_engine(args):
 
 def single_infer_engine(args):
     _envs = envs.load_yaml(args.model)
-    run_extras = get_all_inters_from_yaml(args.model, ["train.", "runner."])
-    trainer_class = run_extras.get(
-        "runner." + _envs["mode"] + ".trainer_class", None)
-
-    if trainer_class:
-        trainer = trainer_class
-    else:
-        trainer = "GeneralTrainer"
-
+    run_extras = get_all_inters_from_yaml(args.model, ["runner."])
+
+    mode = envs.get_runtime_environ("mode")
+    trainer_class = ".".join(["runner", mode, "trainer_class"])
+    fleet_class = ".".join(["runner", mode, "fleet_mode"])
+    device_class = ".".join(["runner", mode, "device"])
+    selected_gpus_class = ".".join(["runner", mode, "selected_gpus"])
+
+    trainer = run_extras.get(trainer_class, "GeneralTrainer")
+    fleet_mode = run_extras.get(fleet_class, "ps")
+    device = run_extras.get(device_class, "cpu")
+    selected_gpus = run_extras.get(selected_gpus_class, "0")
     executor_mode = "infer"
-    fleet_mode = run_extras.get("runner." + _envs["mode"] + ".fleet_mode",
-                                "ps")
 
-    device = run_extras.get("runner." + _envs["mode"] + ".device", "cpu")
-    selected_gpus = run_extras.get(
-        "runner." + _envs["mode"] + ".selected_gpus", "0")
-    selected_gpus_num = len(selected_gpus.split(","))
+    single_envs = {}
+
     if device.upper() == "GPU":
-        assert selected_gpus_num == 1, "Single Mode Only Support One GPU, Set Local Cluster Mode to use Multi-GPUS"
+        selected_gpus_num = len(selected_gpus.split(","))
+        if selected_gpus_num != 1:
+            raise ValueError(
+                "Single Mode Only Support One GPU, Set Local Cluster Mode to use Multi-GPUS"
+            )
+
+        single_envs["selsected_gpus"] = selected_gpus
+        single_envs["FLAGS_selected_gpus"] = selected_gpus
 
-    single_envs = {}
-    single_envs["selected_gpus"] = selected_gpus
-    single_envs["FLAGS_selected_gpus"] = selected_gpus
     single_envs["train.trainer.trainer"] = trainer
     single_envs["train.trainer.executor_mode"] = executor_mode
     single_envs["fleet_mode"] = fleet_mode
@@ -235,18 +259,6 @@ def single_infer_engine(args):
 
 
 def cluster_engine(args):
-    def update_workspace(cluster_envs):
-        workspace = cluster_envs.get("engine_workspace", None)
-
-        if not workspace:
-            return
-        path = envs.path_adapter(workspace)
-        for name, value in cluster_envs.items():
-            if isinstance(value, str):
-                value = value.replace("{workspace}", path)
-                value = envs.windows_path_converter(value)
-                cluster_envs[name] = value
-
     def master():
         role = "MASTER"
         from paddlerec.core.engine.cluster.cluster import ClusterEngine
@@ -255,8 +267,6 @@ def cluster_engine(args):
         flattens["engine_role"] = role
         flattens["engine_run_config"] = args.model
         flattens["engine_temp_path"] = tempfile.mkdtemp()
-        update_workspace(flattens)
-
         envs.set_runtime_environs(flattens)
         print(envs.pretty_print_envs(flattens, ("Submit Envs", "Value")))
 
@@ -424,7 +434,7 @@ def local_mpi_engine(args):
 
 def get_abs_model(model):
     if model.startswith("paddlerec."):
-        dir = envs.path_adapter(model)
+        dir = envs.paddlerec_adapter(model)
         path = os.path.join(dir, "config.yaml")
     else:
         if not os.path.isfile(model):
@@ -442,11 +452,17 @@ if __name__ == "__main__":
     envs.set_runtime_environs({"PACKAGE_BASE": abs_dir})
 
     args = parser.parse_args()
-    model_name = args.model.split('.')[-1]
     args.model = get_abs_model(args.model)
+
     if not validation.yaml_validation(args.model):
         sys.exit(-1)
+
     engine_registry()
-    which_engine = get_engine(args)
-    engine = which_engine(args)
-    engine.run()
+    running_config = get_all_inters_from_yaml(args.model, ["mode", "runner."])
+    modes = get_modes(running_config)
+
+    for mode in modes:
+        envs.set_runtime_environs({"mode": mode})
+        which_engine = get_engine(args, running_config, mode)
+        engine = which_engine(args)
+        engine.run()
diff --git a/setup.py b/setup.py
index b3e3a362333a9edf571477f92cd8f66eebd4dedc..5720ccccbe9b0ef8376094091a3515d78454a53a 100644
--- a/setup.py
+++ b/setup.py
@@ -34,27 +34,28 @@ about["__url__"] = "https://github.com/PaddlePaddle/PaddleRec"
 readme = ""
 
 
-def run_cmd(command):
-    assert command is not None and isinstance(command, str)
-    return os.popen(command).read().strip()
-
-
 def build(dirname):
     package_dir = os.path.dirname(os.path.abspath(__file__))
-    run_cmd("cp -r {}/* {}".format(package_dir, dirname))
-    run_cmd("mkdir {}".format(os.path.join(dirname, "paddlerec")))
-    run_cmd("mv {} {}".format(
-        os.path.join(dirname, "core"), os.path.join(dirname, "paddlerec")))
-    run_cmd("mv {} {}".format(
-        os.path.join(dirname, "doc"), os.path.join(dirname, "paddlerec")))
-    run_cmd("mv {} {}".format(
-        os.path.join(dirname, "models"), os.path.join(dirname, "paddlerec")))
-    run_cmd("mv {} {}".format(
-        os.path.join(dirname, "tests"), os.path.join(dirname, "paddlerec")))
-    run_cmd("mv {} {}".format(
-        os.path.join(dirname, "tools"), os.path.join(dirname, "paddlerec")))
-    run_cmd("mv {} {}".format(
-        os.path.join(dirname, "*.py"), os.path.join(dirname, "paddlerec")))
+    shutil.copytree(
+        package_dir, dirname, ignore=shutil.ignore_patterns(".git"))
+    os.mkdir(os.path.join(dirname, "paddlerec"))
+    shutil.move(
+        os.path.join(dirname, "core"), os.path.join(dirname, "paddlerec"))
+    shutil.move(
+        os.path.join(dirname, "doc"), os.path.join(dirname, "paddlerec"))
+    shutil.move(
+        os.path.join(dirname, "models"), os.path.join(dirname, "paddlerec"))
+    shutil.move(
+        os.path.join(dirname, "tests"), os.path.join(dirname, "paddlerec"))
+    shutil.move(
+        os.path.join(dirname, "tools"), os.path.join(dirname, "paddlerec"))
+
+    for f in os.listdir(dirname):
+        if os.path.isdir(f):
+            continue
+        if os.path.splitext(f)[1] == ".py":
+            shutil.move(
+                os.path.join(dirname, f), os.path.join(dirname, "paddlerec"))
 
     packages = find_packages(dirname, include=('paddlerec.*'))
     package_dir = {'': dirname}
@@ -90,7 +91,7 @@ def build(dirname):
         zip_safe=False)
 
 
-dirname = tempfile.mkdtemp()
+dirname = tempfile.mktemp()
 build(dirname)
 shutil.rmtree(dirname)