diff --git a/doc/fluid/user_guides/howto/training/cluster_quick_start.rst b/doc/fluid/user_guides/howto/training/cluster_quick_start.rst
index a72d18fbeb9afcb337963ea51965deed25685129..236c81e6b527cdc0ca3a7f16b2cf97baaa4de13d 100644
--- a/doc/fluid/user_guides/howto/training/cluster_quick_start.rst
+++ b/doc/fluid/user_guides/howto/training/cluster_quick_start.rst
@@ -1,5 +1,134 @@
 ..  _cluster_quick_start:
 
+从Paddle Fluid 1.5开始，我们推荐使用Fleet API进行分布式训练，关于Fleet API的介绍可以参考 :ref:`fleet_api`
+
+首先，我们假设读者已经学会单机训练，如果还没单机训练的经验，请参考 :ref:`single_training`
+想了解分布式训练，我们可以从单机模拟分布式训练开始，在单台机器上启动多个进程代表多台机器，并进行分布式训练。
+
+为了让读者快速上手，我们采用点击率预估任务作为示例，相关的源码可以参考 xxxx
+
+单机训练代码
+
+.. code:: python
+
+    def train():
+        args = parse_args()
+        if not os.path.isdir(args.model_output_dir):
+            os.mkdir(args.model_output_dir)
+    
+        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')
+
+        loss, auc_var, batch_auc_var = ctr_dnn_model_dataset(dense_input, sparse_input_ids, label,
+                                                             args.embedding_size, args.sparse_feature_dim)
+
+        optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
+        optimizer.minimize(loss)
+
+        exe = fluid.Executor(fluid.CPUPlace())
+        exe.run(fluid.default_startup_program())
+        dataset = fluid.DatasetFactory().create_dataset()
+        dataset.set_use_var([dense_input] + sparse_input_ids + [label])
+        pipe_command = "python criteo_reader.py %d" % args.sparse_feature_dim
+        dataset.set_pipe_command(pipe_command)
+        dataset.set_batch_size(100)
+        thread_num = 10
+        dataset.set_thread(thread_num)
+        whole_filelist = ["raw_data/part-%d" % x for x in range(len(os.listdir("raw_data")))]
+        
+        epochs = 20
+        for i in range(epochs):
+            dataset.set_filelist(whole_filelist[:int(0.8*len(whole_filelist))])
+            exe.train_from_dataset(program=fluid.default_main_program(),
+                                   dataset=dataset,
+                                   fetch_list=[auc_var],
+                                   fetch_info=["auc"],
+                                   debug=False)
+            model_dir = args.model_output_dir + '/epoch' + str(i + 1) + ".model"
+            sys.stderr.write("epoch%d finished" % (i + 1))
+            fluid.io.save_inference_model(model_dir, [dense_input.name] + [x.name for x in sparse_input_ids] + [label.name],                                          [loss, auc_var], exe)
+
+使用FleetAPI进行训练的代码
+
+.. code:: python
+
+    import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+    from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
+
+    def train():
+        args = parse_args()
+        if not os.path.isdir(args.model_output_dir):
+            os.mkdir(args.model_output_dir)
+    
+        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')
+
+        loss, auc_var, batch_auc_var = ctr_dnn_model_dataset(dense_input, sparse_input_ids, label,
+                                                             args.embedding_size, args.sparse_feature_dim)
+
+        role = role_maker.PaddleCloudRoleMaker()
+        exe = fluid.Executor(fluid.CPUPlace())
+        fleet.init(role)
+
+        optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
+        strategy = DistributeTranspilerConfig()
+        strategy.sync_mode = False
+        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 = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_startup_program())
+            dataset = fluid.DatasetFactory().create_dataset()
+            dataset.set_use_var([dense_input] + sparse_input_ids + [label])
+            pipe_command = "python criteo_reader.py %d" % args.sparse_feature_dim
+            dataset.set_pipe_command(pipe_command)
+            dataset.set_batch_size(100)
+            thread_num = 10
+            dataset.set_thread(thread_num)
+            whole_filelist = ["raw_data/part-%d" % x for x in range(len(os.listdir("raw_data")))]
+            
+            epochs = 20
+            for i in range(epochs):
+                dataset.set_filelist(whole_filelist[:int(0.8*len(whole_filelist))])
+                exe.train_from_dataset(program=fluid.default_main_program(),
+                                       dataset=dataset,
+                                       fetch_list=[auc_var],
+                                       fetch_info=["auc"],
+                                       debug=False)
+                
+                if fleet.worker_index() == 0:
+                    model_dir = args.model_output_dir + '/epoch' + str(i + 1) + ".model"
+                    sys.stderr.write("epoch%d finished" % (i + 1))
+                    fluid.io.save_inference_model(model_dir, 
+                        [dense_input.name] + [x.name for x in sparse_input_ids] + [label.name], [loss, auc_var], exe)
+                
+                
+启动命令
+
+.. code:: python
+    
+    python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 dist_train.py
+
+运行日志
+
+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
+
+
+
 分布式训练快速开始
 ==================