我是分布式tensorflow的新手，我正在寻找一个在CPU上执行同步培训的好例子。
我已经尝试了Distributed Tensorflow Example，它可以通过1个参数服务器（1台带1个CPU的机器）和3个工作器（每个工作器= 1个带1个CPU的机器）成功执行异步培训。但是，当涉及到同步训练时，我无法正确运行它，尽管我已按照教程进行操作 SyncReplicasOptimizer(V1.0 and V2.0)。
我已将正式的SyncReplicasOptimizer代码插入到工作异步训练示例中，但训练过程仍然是异步的。我的详细代码如下。任何与同步训练相关的代码都在******的范围内。
import tensorflow as tf
import sys
import time
# cluster specification ----------------------------------------------------------------------
parameter_servers = ["xx1.edu:2222"]
workers = ["xx2.edu:2222", "xx3.edu:2222", "xx4.edu:2222"]
cluster = tf.train.ClusterSpec({"ps":parameter_servers, "worker":workers})
# input flags
tf.app.flags.DEFINE_string("job_name", "", "Either 'ps' or 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")
FLAGS = tf.app.flags.FLAGS
# start a server for a specific task
server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
# Parameters ----------------------------------------------------------------------
N = 3 # number of replicas
learning_rate = 0.001
training_epochs = int(21/N)
batch_size = 100
# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
n_hidden_1 = 256 # 1st layer number of features
n_hidden_2 = 256 # 2nd layer number of features
n_classes = 10 # MNIST total classes (0-9 digits)
if FLAGS.job_name == "ps":
server.join()
print("--- Parameter Server Ready ---")
elif FLAGS.job_name == "worker":
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Between-graph replication
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
# count the number of updates
global_step = tf.get_variable('global_step', [],
initializer = tf.constant_initializer(0),
trainable = False,
dtype = tf.int32)
# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
# Create model
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Hidden layer with RELU activation
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
# Output layer with linear activation
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# Construct model
pred = multilayer_perceptron(x, weights, biases)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
# ************************* SyncReplicasOpt Version 1.0 *****************************************************
''' This optimizer collects gradients from all replicas, "summing" them,
then applying them to the variables in one shot, after which replicas can fetch the new variables and continue. '''
# Create any optimizer to update the variables, say a simple SGD
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
# Wrap the optimizer with sync_replicas_optimizer with N replicas: at each step the optimizer collects N gradients before applying to variables.
opt = tf.train.SyncReplicasOptimizer(opt, replicas_to_aggregate=N,
replica_id=FLAGS.task_index, total_num_replicas=N)
# Now you can call `minimize()` or `compute_gradients()` and `apply_gradients()` normally
train = opt.minimize(cost, global_step=global_step)
# You can now call get_init_tokens_op() and get_chief_queue_runner().
# Note that get_init_tokens_op() must be called before creating session
# because it modifies the graph.
init_token_op = opt.get_init_tokens_op()
chief_queue_runner = opt.get_chief_queue_runner()
# **************************************************************************************
# Test model
correct = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, "float"))
# Initializing the variables
init_op = tf.initialize_all_variables()
print("---Variables initialized---")
# **************************************************************************************
is_chief = (FLAGS.task_index == 0)
# Create a "supervisor", which oversees the training process.
sv = tf.train.Supervisor(is_chief=is_chief,
logdir="/tmp/train_logs",
init_op=init_op,
global_step=global_step,
save_model_secs=600)
# **************************************************************************************
with sv.prepare_or_wait_for_session(server.target) as sess:
# **************************************************************************************
# After the session is created by the Supervisor and before the main while loop:
if is_chief:
sv.start_queue_runners(sess, [chief_queue_runner])
# Insert initial tokens to the queue.
sess.run(init_token_op)
# **************************************************************************************
# Statistics
net_train_t = 0
# Training
for epoch in range(training_epochs):
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
# ======== net training time ========
begin_t = time.time()
sess.run(train, feed_dict={x: batch_x, y: batch_y})
end_t = time.time()
net_train_t += (end_t - begin_t)
# ===================================
# Calculate training accuracy
# acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels})
# print("Epoch:", '%04d' % (epoch+1), " Train Accuracy =", acc)
print("Epoch:", '%04d' % (epoch+1))
print("Training Finished!")
print("Net Training Time: ", net_train_t, "second")
# Testing
print("Testing Accuracy = ", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
sv.stop()
print("done")
我的代码有什么问题？或者我可以有一个很好的榜样吗？
答案 0 :(得分：0)
我不确定您是否对在后端使用MPI的用户透明分布式张量流感兴趣。我们最近使用MaTEx开发了一个这样的版本：https://github.com/matex-org/matex。
因此，对于分布式TensorFlow，您不需要编写SyncReplicaOptimizer代码，因为所有更改都是从用户中提取的。
希望这有帮助。
答案 1 :(得分：0)
一个问题是您需要在minimize方法中指定aggregation_method才能使其同步运行，
train = opt.minimize(cost, global_step=global_step, aggregation_method=tf.AggregationMethod.ADD_N)