add doc

doc/paddle/api/paddle/distributed/fleet/Fleet_cn.rst

@@ -11,57 +11,313 @@ Fleet
 
 .. py:method:: init(role_maker=None, is_collective=False)
 
+使用RoleMaker或其他配置初始化fleet。
+
+返回：无。
+
+
+**代码示例1**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+
+**代码示例2**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init(is_collective=True)
+
+**代码示例3**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    role = fleet.PaddleCloudRoleMaker
+    fleet.init(role)
+
 
 .. py:method:: is_first_worker()
 
+返回当前节点是否为第一个`worker`节点
+
+返回：True/False
+
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.is_first_worker()
+
+
 
 .. py:method:: worker_index()
 
+返回当前节点的编号, 每个`worker`节点被分配[0, worker_num-1]内的唯一的编码ID
+
+返回：int
+
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.worker_index()
+
 
 .. py:method:: worker_num()
 
+返回当前全部训练节点的个数
+
+返回：int
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.worker_num()
+
 
 .. py:method:: is_worker()
 
+返回当前节点是否为`worker`节点
+
+返回：True/False
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.is_worker()
+
 
 .. py:method:: worker_endpoints(to_string=False)
 
+返回全部worker节点的ip及端口信息
+
+返回：list/string
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.worker_endpoints()
+
 
 .. py:method:: server_num()
 
+返回当前全部Server节点的个数
+
+返回：int
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.server_num()
+
 
 .. py:method:: server_index()
 
 
+返回当前节点的编号, 每个`server`节点被分配[0, server_num-1]内的唯一的编码ID
+
+返回：int
+
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.server_index()
+
+
 .. py:method:: server_endpoints(to_string=False)
 
 
+返回全部server节点的ip及端口信息
+
+返回：list/string
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.server_endpoints()
+
+
 .. py:method:: is_server()
 
 
+返回当前节点是否为`server`节点
+
+返回：True/False
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.is_server()
+
+
 .. py:method:: barrier_worker()
 
+强制要求所有的worker在此处需要相互等待一次
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.barrier_worker()
+
 
 .. py:method:: init_worker()
 
+worker节点在训练前的初始化, 包括通信模块， 参数同步等
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.init_worker()
+
 
 .. py:method:: init_server(*args, **kwargs)
 
+server节点的初始化, 包括server端参数初始化，模型加载等
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.init_server()
+
 
 .. py:method:: run_server()
 
+server节点的运行, 此命令会将ParameterServer的进程启动并常驻直至训练结束
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.init_server()
+    fleet.run_server()
+
 
 .. py:method:: stop_worker()
 
+停止当前正在运行的worker节点
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    fleet.init()
+    fleet.init_worker()
+    "..."
+    fleet.stop_worker()
+
 
 .. py:method:: save_inference_model(executor, dirname, feeded_var_names, target_vars, main_program=None, export_for_deployment=True)
 
+保存模型及参数用于预估服务
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    import paddle.fluid as fluid
+
+    fleet.init()
+
+    # build net
+    # fleet.distributed_optimizer(...)
+
+    exe = fluid.Executor(fluid.CPUPlace())
+    fleet.save_inference_model(exe, "dirname", ["feednames1"], [acc, loss], fluid.default_main_program())
+
 
 .. py:method:: save_persistables(executor, dirname, main_program=None)
 
 
+保存全量模型参数
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.distributed.fleet as fleet
+    import paddle.fluid as fluid
+
+    fleet.init()
+
+    # build net
+    # fleet.distributed_optimizer(...)
+
+    exe = fluid.Executor(fluid.CPUPlace())
+    fleet.save_persistables(exe, "dirname", fluid.default_main_program())
+
+
 .. py:method:: distributed_optimizer(optimizer, strategy=None)
 
+基于分布式布式并行策略进行模型的拆分及优化。
+
+**代码示例**
+
+.. code-block:: python
+    import paddle.distributed.fleet as fleet
+    role = fleet.role_maker.PaddleCloudRoleMaker(is_collective=True)
+    fleet.init(role)
+    strategy = fleet.DistributedStrategy()
+    optimizer = paddle.optimizer.SGD(learning_rate=0.001)
+    optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+
 
 .. py:method:: distributed_model(model)
 

doc/paddle/api/paddle/distributed/fleet/PaddleCloudRoleMaker_cn.rst

@@ -4,6 +4,7 @@ PaddleCloudRoleMaker
 -------------------------------
 
 .. py:class:: paddle.distributed.fleet.PaddleCloudRoleMaker
+PaddleCloudRoleMaker是基于从环境变量中获取分布式相关信息进行分布式配置初始化的接口.
 
 
 

doc/paddle/api/paddle/distributed/fleet/UserDefinedRoleMaker_cn.rst

@@ -5,6 +5,8 @@ UserDefinedRoleMaker
 
 .. py:class:: paddle.distributed.fleet.UserDefinedRoleMaker
 
+UserDefinedRoleMaker是基于从用户自定义的参数中获取分布式相关信息进行分布式配置初始化的接口
+