Implement Book chapter 02 in distributed framework (#7629)

python/paddle/v2/fluid/tests/book_distribute/notest_recognize_digits_mlp_dist.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+from __future__ import print_function
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import os
+
+BATCH_SIZE = 128
+PASS_NUM = 100
+
+images = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
+# TODO(aroraabhinav) Add regularization and error clipping after
+# Issue 7432(https://github.com/PaddlePaddle/Paddle/issues/7432) is resolved.
+hidden1 = fluid.layers.fc(input=images, size=128, act='relu')
+hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
+predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
+
+label = fluid.layers.data(name='y', shape=[1], dtype='int64')
+
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+avg_cost = fluid.layers.mean(x=cost)
+
+optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
+optimize_ops, params_grads = optimizer.minimize(avg_cost)
+
+accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=8192),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+
+t = fluid.DistributeTranspiler()
+# all parameter server endpoints list for spliting parameters
+pserver_endpoints = os.getenv("PSERVERS")
+# server endpoint for current node
+current_endpoint = os.getenv("SERVER_ENDPOINT")
+# run as trainer or parameter server
+training_role = os.getenv("TRAINING_ROLE",
+                          "TRAINER")  # get the training role: trainer/pserver
+t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+if training_role == "PSERVER":
+    if not current_endpoint:
+        print("need env SERVER_ENDPOINT")
+        exit(1)
+    pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
+    exe.run(fluid.default_startup_program())
+    exe.run(pserver_prog)
+elif training_role == "TRAINER":
+    trainer_prog = t.get_trainer_program()
+    feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
+    exe.run(fluid.default_startup_program())
+
+    for pass_id in range(PASS_NUM):
+        accuracy.reset(exe)
+        batch_id = 0
+        for data in train_reader():
+            loss, acc = exe.run(trainer_prog,
+                                feed=feeder.feed(data),
+                                fetch_list=[avg_cost] + accuracy.metrics)
+            pass_acc = accuracy.eval(exe)
+            if batch_id % 100 == 0:
+                print("batch_id %d, loss: %f, acc: %f" %
+                      (batch_id, loss, pass_acc))
+            batch_id += 1
+
+        pass_acc = accuracy.eval(exe)
+        print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
+else:
+    print("environment var TRAINER_ROLE should be TRAINER os PSERVER")