Update cluster_quick_start.rst

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

@@ -7,120 +7,100 @@
 
 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
+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
 
-    def train():
+    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()
-        if not os.path.isdir(args.model_output_dir):
-            os.mkdir(args.model_output_dir)
 
+    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')
-
-        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)
+            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")
 
-        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
+        pipe_command = "python criteo_reader.py %d" % sparse_feature_dim
         dataset.set_pipe_command(pipe_command)
-        dataset.set_batch_size(100)
-        thread_num = 10
+        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")))]
+        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)
 
-        epochs = 20
-        for i in range(epochs):
-            dataset.set_filelist(whole_filelist[:int(0.8*len(whole_filelist))])
+        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)
-            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')
+        def local_train(optimizer):
+            optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
+            optimizer.minimize(loss)
+            exe.run(fluid.default_startup_program())
+            train_loop()
 
-        loss, auc_var, batch_auc_var = ctr_dnn_model_dataset(dense_input, sparse_input_ids, label,
-                                                             args.embedding_size, args.sparse_feature_dim)
 
+        def dist_train(optimizer):
             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 = 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 = 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")))]
+                train_loop()
 
-            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 is_local:
+            local_train(optimizer)
+        else:
+            dist_train(optimizer)
 
-                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)
+    if __name__ == '__main__':
+        main_function(args.is_local)
   
+单机训练启动命令
+.. code:: python
     
-启动命令
+    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
 .. code:: python
     
-    python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 dist_train.py
+    python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 train.py
 
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