add comments in io.py

python/paddle/fluid/io.py

@@ -840,6 +840,12 @@ def save_checkpoint(executor,
         max_num_checkpoints(int): The max number of total number of existing 
             checkpoints.
             Default: 3
+        lookup_table(string|None): the lookup table name, when use distribute
+            lookup table, we can get lookup table name by DistributeTranspiler.
+            table_name 
+        ps_endpoint_list(list|None): the parameter server ip:port list.  
+            when use distribute lookup table, we can get ps_endpoint_list by 
+            distribute arguments.
 
     Returns:
         None
@@ -856,15 +862,21 @@ def save_checkpoint(executor,
             prog = fluid.default_main_program()
             trainer_args = {"epoch_id": 200,
                             "step_id": 20} # just an example
+            table_name = "share_w"
+            ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
+
             fluid.io.save_checkpoint(executor=exe,
                                      checkpoint_dir=path,
                                      trainer_id=0,
                                      trainer_args=trainer_args,
                                      main_program=prog,
-                                     max_num_checkpoints=3)
+                                     max_num_checkpoints=3,
+                                     lookup_table=table_name,
+                                     ps_endpoint_list = ps_endpoints)
     """
     if checkpoint_dir is None:
         raise ValueError("'checkpoint_dir' should not be None")
+    assert checkpoint_dir
 
     if trainer_args:
         assert isinstance(trainer_args, dict)
@@ -881,6 +893,7 @@ def save_checkpoint(executor,
 
     if is_chief:
         save_persist_vars_without_grad(executor, cur_dir, main_program)
+
     if is_chief and lookup_table and ps_endpoint_list:
         save_pserver_vars_by_notify(executor, cur_dir, lookup_table,
                                     ps_endpoint_list)
@@ -1020,6 +1033,31 @@ def load_persist_vars_without_grad(executor,
 
 
 def load_lookup_table_vars(executor, dirname, program, pserver_id, table_name):
+    """
+    The parameter server will load lookup table's local file in 
+    selectedrows variable.
+
+    Args:
+        executor(Executor): The executor to run for loading persistable variables
+        dirname(str): The directory path
+        main_program(Program): Find the variable named table_name in main_program
+        pserver_id(int): the serial number in pserver_endpoints list
+        table_name(str): lookup table name
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            dirname = "./checkpoints/checkpoint_9/__model__"
+            prog = fluid.default_main_program()
+            pserver_id = 1
+            table_name = "share_w"
+            fluid.io.load_lookup_table_vars(executor=exe,
+                    dirname=dirname, program=prog, pserver_id=pserver_id,
+                    table_name=table_name)
+    """
 
     for var in program.list_vars():
         if var.name == table_name:
@@ -1092,6 +1130,35 @@ def save_persist_vars_without_grad(executor, dirname, program):
 def save_pserver_vars_by_notify(executor, dirname, lookup_table,
                                 ps_endpoint_list):
     """
+    This function will send checkpoint notify message from Trainer 0
+    to all the pservers.
+    The checkpoint notify message contains lookup table name, 
+    the absolute path on pserver to save lookup_table.
+
+    Args:
+        executor(Executor): The executor to run for send checkpoint notify.
+        dirname(str): The folder where to save checkpoints.
+        lookup_table(string): the lookup table name, when use distribute
+            lookup table, we can get lookup table name by DistributeTranspiler.
+            table_name 
+        ps_endpoint_list(list): the parameter server ip:port list.  
+            when use distribute lookup table, we can get ps_endpoint_list by 
+            distribute arguments.
+    Return:
+        None
+    
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            param_path = "./my_paddle_model"
+            prog = fluid.default_main_program()
+            table_name = "share_w"
+            ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
+
+            fluid.io.save_pserver_vars_by_notify(executor=exe,
+                    dirname=param_path, lookup_table=table_name, 
+                    ps_endpoint_list=ps_endpoints)
     """
     cur_dir = _get_lookuptable_dir(dirname)
 
@@ -1121,6 +1188,29 @@ def save_trainer_args(dirname, trainer_id, trainer_args):
 
 
 def load_trainer_args(checkpoint_dir, serial, trainer_id, trainer_args):
+    """
+    trainer will load some args from it's  independent directory, 
+    such as epoch_id and step_id.
+
+    Args:
+        checkpoint_dir(str): The folder where all checkpoints are.
+        serial(int): The serial of checkpoint you would like to load.
+        trainer_id(int): current trainer id.
+        trainer_args(list): list about load trainer args
+    Return:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            param_path = "./checkpoint/"
+            serial = 7
+            trainer_id = 2
+            trainer_args = ["epoch_id", "step_id"]
+
+            fluid.io.load_trainer_args(checkpoint_dir=param_path, serial=serial,
+            trainer_id=trainer_id, trainer_args=trainer_args)
+    """
     assert isinstance(trainer_args, list)
 
     cur_dir = _get_serial_dir(checkpoint_dir, serial)
@@ -1141,7 +1231,7 @@ def _is_checkpoint_var(var):
     the checkpoint will not save or load all the variables.
     var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
 
-    : param var
+    : param var(Variable)
     """
     if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
             var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \