fix API spec about infer_from_dataset

test=develop

python/paddle/fluid/dataset.py

@@ -220,9 +220,11 @@ class InMemoryDataset(DatasetBase):
     def global_shuffle(self, fleet=None):
         """
         Global shuffle.
-        If you run distributed, you should pass fleet instead of None.
+        Global shuffle can be used only in distributed mode. i.e. multiple
+        processes on single machine or multiple machines training together.
+        If you run in distributed mode, you should pass fleet instead of None.
 
-        Example:
+        Examples:
             >>> import paddle.fluid as fluid
             >>> import paddle.fluid.incubate.fleet.parameter_server as fleet
             >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")

python/paddle/fluid/device_worker.py

@@ -11,7 +11,6 @@
 # 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.
-import sys
 
 __all__ = ['DeviceWorker', 'Hogwild', 'DownpourSGD']
 
@@ -117,7 +116,7 @@ class DownpourSGD(DeviceWorker):
         program_id = str(id(self.program_))
         if self.program_ == None:
             print("program of current device worker is not configured")
-            sys.exit(-1)
+            exit(-1)
         opt_info = self.program_._fleet_opt
         program_configs = opt_info["program_configs"]
         downpour = trainer_desc.downpour_param

python/paddle/fluid/distributed/node.py

@@ -112,30 +112,6 @@ class DownpourServer(Server):
             fea_dim += reduce(lambda x, y: x * y, param.shape, 1)
         table.accessor.fea_dim = fea_dim
 
-    def add_data_norm_table(self, table_id, learning_rate, param_var, grad_var):
-        """
-        Args:
-            table_id(int): id of sparse params table
-            learning_rate(float): the learning rate used to update parameters. \
-                Can be a float value
-            param_var(list): all dense param. it is a list.
-            grad_var(list): all dense grad parm it is a list.
-        Returns:
-            return None 
-        """
-        table = self.server_.downpour_server_param.downpour_table_param.add()
-        table.table_id = table_id
-        table.table_class = "DownpourDenseTable"
-        table.type = pslib.PS_DENSE_TABLE
-        table.accessor.accessor_class = "DownpourDenseValueAccessor"
-        table.accessor.dense_sgd_param.name = "summary"
-        table.accessor.dense_sgd_param.summary.summary_decay_rate = 0.999999
-        fea_dim = 0
-        for param in filter(lambda x: x.name.find("embedding") == -1,
-                            param_var):
-            fea_dim += reduce(lambda x, y: x * y, param.shape, 1)
-        table.accessor.fea_dim = fea_dim
-
     def get_desc(self):
         """
         Return downpour server program_desc

python/paddle/fluid/executor.py

@@ -676,16 +676,16 @@ class Executor(object):
                if not provided, then default_main_program (not compiled) will be used.
             dataset(paddle.fluid.Dataset): dataset created outside this function,
                a user should provide a well-defined dataset before calling this function.
-               Please check the document of Dataset if needed.
+               Please check the document of Dataset if needed. default is None
             scope(Scope): the scope used to run this program, you can switch it to different scope
                for each run. default is global_scope
             thread(int): number of thread a user wants to run in this function. The actual number
-               of thread will be min(Dataset.thread_num, thread)
-            debug(bool): whether a user wants to run infer_from_dataset
+               of thread will be min(Dataset.thread_num, thread) if thread > 0, default is 0
+            debug(bool): whether a user wants to run infer_from_dataset, default is False
             fetch_list(Variable List): fetch variable list, each variable
-                                       will be printed during training
-            fetch_info(String List): print information for each variable
-            print_period(int): the number of mini-batches for each print
+                                       will be printed during training, default is None
+            fetch_info(String List): print information for each variable, default is None
+            print_period(int): the number of mini-batches for each print, default is 100
 
         Returns:
             None
@@ -693,6 +693,7 @@ class Executor(object):
         Examples:
 
             .. code-block:: python
+
                 import paddle.fluid as fluid
                 place = fluid.CPUPlace()
                 exe = fluid.Executor(place)
@@ -707,6 +708,9 @@ class Executor(object):
                                        dataset=dataset)        
 
         """
+        if dataset == None:
+            raise RuntimeError("dataset is needed and should be initialized")
+
         if self.place == paddle.fluid.CUDAPlace():
             raise RuntimeError("infer_from_dataset is verified on CPUPlace"
                                "We will open CUDAPlace in the future")
@@ -788,6 +792,9 @@ class Executor(object):
                                      dataset=dataset)
 
         """
+        if dataset == None:
+            raise RuntimeError("dataset is need and should be initialized")
+
         if self.place == paddle.fluid.CUDAPlace():
             raise RuntimeError("train_from_dataset is verified on CPUPlace"
                                "We will open CUDAPlace in the future")