Update cluster_quick_start.rst

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

-..  _cluster_quick_start:
+分布式训练快速开始
+==================
 
-从Paddle Fluid 1.5.1 https://github.com/PaddlePaddle/Paddle/releases/tag/v1.5.1 开始，官方推荐使用Fleet API进行分布式训练，关于Fleet API的介绍可以参考 https://github.com/PaddlePaddle/Fleet。
+使用Fleet API进行分布式训练
+---------------------------
+
+从Paddle Fluid `Release
+1.5.1 <https://github.com/PaddlePaddle/Paddle/releases/tag/v1.5.1>`__
+开始，官方推荐使用Fleet API进行分布式训练，关于Fleet API的介绍可以参考
+`Fleet Design Doc <https://github.com/PaddlePaddle/Fleet>`__
 
 前置条件
-成功安装Paddle Fluid
-学会最基本的单机训练方法
+~~~~~~~~
+
+-  成功安装Paddle Fluid
+-  学会最基本的单机训练方法
 
-本文使用一个简单的示例，点击率预估任务，来说明如何使用Fleet API进行分布式训练的配置方法，并利用单机环境模拟分布式环境给出运行示例。
-https://github.com/PaddlePaddle/Fleet/tree/develop/examples/ctr
+本文使用一个简单的示例，点击率预估任务，来说明如何使用Fleet
+API进行分布式训练的配置方法，并利用单机环境模拟分布式环境给出运行示例。示例的源码来自\ `CTR
+with
+Fleet <https://github.com/PaddlePaddle/Fleet/tree/develop/examples/ctr>`__
 
 为了方便学习，这里给出的示例是单机与多机混合的代码，用户可以通过不同的启动命令进行单机或多机任务的启动。
 
 .. code:: python
 
-    from __future__ import print_function
-
-    from args import parse_args
-    import os
-    import paddle.fluid as fluid
-    import sys
-    from network_conf import ctr_dnn_model_dataset
-    import paddle.fluid.incubate.fleet.base.role_maker as role_maker
-    from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
-    from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig
-
-    dense_feature_dim = 13
-    sparse_feature_dim = 10000001
-    batch_size = 100
-    thread_num = 10
-    embedding_size = 10
-
-    args = parse_args()
-
-    def main_function(is_local):
-        dense_input = fluid.layers.data(
-            name="dense_input", shape=[dense_feature_dim], dtype='float32')
-        sparse_input_ids = [
-            fluid.layers.data(name="C" + str(i), shape=[1], lod_level=1,
-                              dtype="int64") for i in range(1, 27)]
-        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-
-        dataset = fluid.DatasetFactory().create_dataset()
-        dataset.set_use_var([dense_input] + sparse_input_ids + [label])
-        pipe_command = "python criteo_reader.py %d" % sparse_feature_dim
-        dataset.set_pipe_command(pipe_command)
-        dataset.set_batch_size(batch_size)
-        dataset.set_thread(thread_num)
-        whole_filelist = ["raw_data/part-%d" % x 
-                          for x in range(len(os.listdir("raw_data")))]
-        dataset.set_filelist(whole_filelist)
-        loss, auc_var, batch_auc_var = ctr_dnn_model_dataset(
-            dense_input, sparse_input_ids, label, embedding_size,
-            sparse_feature_dim)
-
-        exe = fluid.Executor(fluid.CPUPlace())
-
-        def train_loop(epoch=20):
-            for i in range(epoch):
-                exe.train_from_dataset(program=fluid.default_main_program(),
-                                       dataset=dataset,
-                                       fetch_list=[auc_var],
-                                       fetch_info=["auc"],
-                                       debug=False)
-
-        def local_train(optimizer):
-            optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
-            optimizer.minimize(loss)
-            exe.run(fluid.default_startup_program())
-            train_loop()
-
-
-        def dist_train(optimizer):
-            role = role_maker.PaddleCloudRoleMaker()
-            fleet.init(role)
-            strategy = DistributeTranspilerConfig()
-            strategy.sync_mode = False
-            optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
-            optimizer = fleet.distributed_optimizer(optimizer, strategy)
-            optimizer.minimize(loss)
-            if fleet.is_server():
-                fleet.init_server()
-                fleet.run_server()
-            elif fleet.is_worker():
-                fleet.init_worker()
+        from __future__ import print_function
+        from args import parse_args
+        import os
+        import paddle.fluid as fluid
+        import sys
+        from network_conf import ctr_dnn_model_dataset
+        import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+        from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
+        from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig
+        dense_feature_dim = 13
+        sparse_feature_dim = 10000001
+        batch_size = 100
+        thread_num = 10
+        embedding_size = 10
+        args = parse_args()
+        def main_function(is_local):
+            dense_input = fluid.layers.data(
+                name="dense_input", shape=[dense_feature_dim], dtype='float32')
+            sparse_input_ids = [
+                fluid.layers.data(name="C" + str(i), shape=[1], lod_level=1,
+                                  dtype="int64") for i in range(1, 27)]
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+            dataset = fluid.DatasetFactory().create_dataset()
+            dataset.set_use_var([dense_input] + sparse_input_ids + [label])
+            pipe_command = "python criteo_reader.py %d" % sparse_feature_dim
+            dataset.set_pipe_command(pipe_command)
+            dataset.set_batch_size(batch_size)
+            dataset.set_thread(thread_num)
+            whole_filelist = ["raw_data/part-%d" % x 
+                              for x in range(len(os.listdir("raw_data")))]
+            dataset.set_filelist(whole_filelist)
+            loss, auc_var, batch_auc_var = ctr_dnn_model_dataset(
+                dense_input, sparse_input_ids, label, embedding_size,
+                sparse_feature_dim)
+            exe = fluid.Executor(fluid.CPUPlace())
+            def train_loop(epoch=20):
+                for i in range(epoch):
+                    exe.train_from_dataset(program=fluid.default_main_program(),
+                                           dataset=dataset,
+                                           fetch_list=[auc_var],
+                                           fetch_info=["auc"],
+                                           debug=False)
+            def local_train(optimizer):
+                optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
+                optimizer.minimize(loss)
                 exe.run(fluid.default_startup_program())
                 train_loop()
+            def dist_train(optimizer):
+                role = role_maker.PaddleCloudRoleMaker()
+                fleet.init(role)
+                strategy = DistributeTranspilerConfig()
+                strategy.sync_mode = False
+                optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
+                optimizer = fleet.distributed_optimizer(optimizer, strategy)
+                optimizer.minimize(loss)
+                if fleet.is_server():
+                    fleet.init_server()
+                    fleet.run_server()
+                elif fleet.is_worker():
+                    fleet.init_worker()
+                    exe.run(fluid.default_startup_program())
+                    train_loop()
+            if is_local:
+                local_train(optimizer)
+            else:
+                dist_train(optimizer)
+        if __name__ == '__main__':
+            main_function(args.is_local)
 
-        if is_local:
-            local_train(optimizer)
-        else:
-            dist_train(optimizer)
+单机训练启动命令
 
-    if __name__ == '__main__':
-        main_function(args.is_local)
+.. code:: python
 
+        python train.py --is_local 1
 
-单机训练启动命令
-.. code:: python
-    
-    python train.py --is_local 1
+在单机模拟多机训练的启动命令，这里我们用到了paddle内置的一个启动器launch\_ps，用户可以指定worker和server的数量进行参数服务器任务的启动
 
-在单机模拟多机训练的启动命令，这里我们用到了paddle内置的一个启动器launch_ps，用户可以指定worker和server的数量进行参数服务器任务的启动
 .. code:: python
-    
-    python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 train.py
 
-运行日志
+        python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 train.py
 
-如何进行多机分布式训练
-请参考百度云运行分布式任务的示例
+任务运行的日志在工作目录的logs目录下可以查看，当您能够使用单机模拟分布式训练，可以进行真正的多机分布式训练。我们建议用户直接参\ `百度云运行分布式任务的示例 <https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/user_guides/howto/training/deploy_ctr_on_baidu_cloud_cn.html>`__