fix API.spec

test=develop

p2p_role_maker.py

@@ -1,17 +0,0 @@
-
-
-class P2PRoleMakers(object):
-    def __init__(self):
-        from mpi4py import MPI
-        self.comm = MPI.COMM_WORLD
-        self.MPI = MPI
-
-    def get_endpoints(self, port_start):
-        rank = self.comm.Get_rank()
-        size = self.comm.Get_size()
-        import socket
-        local_ip = socket.gethostbyname(socket.gethostname())
-        hostname = socket.gethostname()
-        all_ips = self.comm.allgather(local_ip)
-        all_ports = [str(port_start + rank) for ]
-        return all_ports

paddle/fluid/framework/executor.h

@@ -56,7 +56,6 @@ class Executor {
 
   explicit Executor(const platform::Place& place);
 
-  explicit Executor(Scope* scope, const platform::Place& place);
   /*
    * Close this Executor.
    * Calling this method will send complete messages to all pserver instances.

python/paddle/fluid/async_executor.py

@@ -86,7 +86,7 @@ class AsyncExecutor(object):
             >>> async_executor = fluid.AsyncExecutor(place)
 
         Args:
-            place(Place): CPUPlace or GPUPlace.
+            place(Place): CPUPlace only
             run_mode(str): default is empty string.
         """
         if place is None:

python/paddle/fluid/executor.py

@@ -703,6 +703,9 @@ class Executor(object):
                 exe.infer_from_dataset(program=fluid.default_main_program(),
                                        dataset=dataset)        
         """
+        if self.place == paddle.fluid.CUDAPlace():
+            raise RuntimeError("infer_from_dataset is verified on CPUPlace"
+                               "We will open CUDAPlace in the future")
 
         scope, trainer = self._prepare_trainer(
             program=program,
@@ -776,6 +779,9 @@ class Executor(object):
                                    dataset=dataset)
 
         """
+        if self.place == paddle.fluid.CUDAPlace():
+            raise RuntimeError("train_from_dataset is verified on CPUPlace"
+                               "We will open CUDAPlace in the future")
 
         scope, trainer = self._prepare_trainer(
             program=program,