Update fleet_api_howto_cn.rst

test=document_preview

doc/fluid/user_guides/howto/training/fleet_api_howto_cn.rst

-.. _fleet_api_howto_cn:
-
 使用FleetAPI进行分布式训练
 ==========================
 
@@ -16,9 +14,10 @@ Fleet API快速上手示例
 下面会针对Fleet
 API最常见的两种使用场景，用一个模型做示例，目的是让用户有快速上手体验的模板。快速上手的示例源代码可以在\ `Fleet
 Quick
-Start <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/quick-start>`__\ 找到。
+Start <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/quick-start>`__
+找到。
 
-假设我们定义MLP网络如下：
+-  假设我们定义MLP网络如下：
 
 .. code:: python
 
@@ -32,7 +31,7 @@ Start <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/quick-start>`
       avg_cost = fluid.layers.mean(x=cost)
       return avg_cost
 
-定义一个在内存生成数据的Reader如下：
+-  定义一个在内存生成数据的Reader如下：
 
 .. code:: python
 
@@ -42,32 +41,24 @@ Start <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/quick-start>`
         return {"x": np.random.random(size=(128, 32)).astype('float32'),
                 "y": np.random.randint(2, size=(128, 1)).astype('int64')}
 
-单机Trainer定义
->>>>>>>>>>>>>>>
+-  单机Trainer定义
 
-.. code:: python
+   import paddle.fluid as fluid from nets import mlp from utils import
+   gen\_data
 
-    import paddle.fluid as fluid
-    from nets import mlp
-    from utils import gen_data
+   input\_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
+   input\_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
 
-    input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
-    input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
-
-    cost = mlp(input_x, input_y)
-    optimizer = fluid.optimizer.SGD(learning_rate=0.01)
-    optimizer.minimize(cost)
+   cost = mlp(input\_x, input\_y) optimizer =
+   fluid.optimizer.SGD(learning\_rate=0.01) optimizer.minimize(cost)
    place = fluid.CUDAPlace(0)
 
    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    step = 1001
-    for i in range(step):
-      cost_val = exe.run(feed=gen_data(), fetch_list=[cost.name])
-      print("step%d cost=%f" % (i, cost_val[0]))
+   exe.run(fluid.default\_startup\_program()) step = 1001 for i in
+   range(step): cost\_val = exe.run(feed=gen\_data(),
+   fetch\_list=[cost.name]) print("step%d cost=%f" % (i, cost\_val[0]))
 
-Parameter Server训练方法
->>>>>>>>>>>>>>>
+-  Parameter Server训练方法
 
 参数服务器方法对于大规模数据，简单模型的并行训练非常适用，我们基于单机模型的定义给出其实用Parameter
 Server进行训练的示例如下：
@@ -107,8 +98,7 @@ Server进行训练的示例如下：
         print("worker_index: %d, step%d cost = %f" %
              (fleet.worker_index(), i, cost_val[0]))
 
-Collective训练方法
->>>>>>>>>>>>>>>
+-  Collective训练方法
 
 collective
 training通常在GPU多机多卡训练中使用，一般在复杂模型的训练中比较常见，我们基于上面的单机模型定义给出使用Collective方法进行分布式训练的示例如下：
@@ -147,23 +137,23 @@ training通常在GPU多机多卡训练中使用，一般在复杂模型的训练
 更多使用示例
 ------------
 
-`点击率预估 <>`__
+`点击率预估 <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/ctr>`__
 
-`语义匹配 <>`__
+`语义匹配 <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/semantic_matching>`__
 
-`向量学习 <>`__
+`向量学习 <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/word2vec>`__
 
-`基于Resnet50的图像分类 <>`__
+`基于Resnet50的图像分类 <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/resnet50>`__
 
-`基于Transformer的机器翻译 <>`__
+`基于Transformer的机器翻译 <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/transformer>`__
 
-`基于Bert的语义表示学习 <>`__
+`基于Bert的语义表示学习 <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/bert>`__
 
 Fleet API相关的接口说明
 -----------------------
 
 Fleet API接口
->>>>>>>>>>>>>>>
+>>>>>>>>>>>>
 
 -  init(role\_maker=None)
 -  fleet初始化，需要在使用fleet其他接口前先调用，用于定义多机的环境配置
@@ -187,7 +177,7 @@ Fleet API接口
 -  分布式优化算法装饰器，用户可带入单机optimizer，并配置分布式训练策略，返回一个分布式的optimizer
 
 RoleMaker
->>>>>>>>>>>>>>>
+>>>>>>>>>>>>
 
 -  MPISymetricRoleMaker
 
@@ -195,19 +185,15 @@ RoleMaker
 
 -  示例：
 
-   .. code:: python
+   \`\`\`python from
+   paddle.fluid.incubate.fleet.parameter\_server.distribute\_transpiler
+   import fleet from paddle.fluid.incubate.fleet.base import role\_maker
 
-       from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
-       from paddle.fluid.incubate.fleet.base import role_maker
-
-       role = role_maker.MPISymetricRoleMaker()
-       fleet.init(role)
+role = role\_maker.MPISymetricRoleMaker() fleet.init(role) \`\`\`
 
 -  启动方法：
 
-   .. code:: shell
-
-       mpirun -np 2 python trainer.py
+   ``python    mpirun -np 2 python trainer.py``
 
 -  PaddleCloudRoleMaker
 
@@ -215,35 +201,27 @@ RoleMaker
 
 -  Parameter Server训练示例：
 
-   .. code:: python
+   \`\`\`python from
+   paddle.fluid.incubate.fleet.parameter\_server.distribute\_transpiler
+   import fleet from paddle.fluid.incubate.fleet.base import role\_maker
 
-       from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
-       from paddle.fluid.incubate.fleet.base import role_maker
-
-       role = role_maker.PaddleCloudRoleMaker()
-       fleet.init(role)
+role = role\_maker.PaddleCloudRoleMaker() fleet.init(role) \`\`\`
 
 -  启动方法：
 
-   .. code:: python
-
-       python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 trainer.py
+   ``python    python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 trainer.py``
 
 -  Collective训练示例：
 
-   .. code:: python
+   \`\`\`python from paddle.fluid.incubate.fleet.collective import fleet
+   from paddle.fluid.incubate.fleet.base import role\_maker
 
-       from paddle.fluid.incubate.fleet.collective import fleet
-       from paddle.fluid.incubate.fleet.base import role_maker
-
-       role = role_maker.PaddleCloudRoleMaker(is_collective=True)
-       fleet.init(role)
+role = role\_maker.PaddleCloudRoleMaker(is\_collective=True)
+fleet.init(role) \`\`\`
 
 -  启动方法：
 
-   .. code:: python
-
-       python -m paddle.distributed.launch trainer.py
+   ``python   python -m paddle.distributed.launch trainer.py``
 
 -  UserDefinedRoleMaker
 
@@ -251,21 +229,18 @@ RoleMaker
 
 -  示例：
 
-   .. code:: python
+   \`\`\`python from
+   paddle.fluid.incubate.fleet.parameter\_server.distribute\_transpiler
+   import fleet from paddle.fluid.incubate.fleet.base import role\_maker
 
-       from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
-       from paddle.fluid.incubate.fleet.base import role_maker
-
-       role = role_maker.UserDefinedRoleMaker(
-                   current_id=int(os.getenv("CURRENT_ID")),
-                   role=role_maker.Role.WORKER if bool(int(os.getenv("IS_WORKER"))) 
-                                                                                   else role_maker.Role.SERVER,
-                   worker_num=int(os.getenv("WORKER_NUM")),
-                   server_endpoints=pserver_endpoints)
-       fleet.init(role)
+role = role\_maker.UserDefinedRoleMaker(
+current\_id=int(os.getenv("CURRENT\_ID")), role=role\_maker.Role.WORKER
+if bool(int(os.getenv("IS\_WORKER"))) else role\_maker.Role.SERVER,
+worker\_num=int(os.getenv("WORKER\_NUM")),
+server\_endpoints=pserver\_endpoints) fleet.init(role) \`\`\`
 
 Strategy
->>>>>>>>>>>>>>>
+>>>>>>>>>>>>
 
 -  Parameter Server Training
 -  Sync\_mode
@@ -274,7 +249,7 @@ Strategy
 -  ReduceGrad
 
 Fleet Mode
->>>>>>>>>>>>>>>
+>>>>>>>>>>>>
 
 -  Parameter Server Training