在一套标准的系统上通常有多个计算设备. TensorFlow 支持 CPU 和 GPU 这两种设备. 我们用指定字符串strings 来标识这些设备. 比如:
"/cpu:0": 机器中的 CPU"/gpu:0": 机器中的 GPU, 如果你有一个的话."/gpu:1": 机器中的第二个 GPU, 以此类推...如果一个 TensorFlow 的 operation 中兼有 CPU 和 GPU 的实现, 当这个算子被指派设备时, GPU 有优先权. 比如matmul中 CPU和 GPU kernel 函数都存在. 那么在cpu:0 和gpu:0 中, matmul operation 会被指派给 gpu:0 .
为了获取你的 operations 和 Tensor 被指派到哪个设备上运行, 用 log_device_placement 新建一个session, 并设置为True.
# 新建一个 graph. a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # 新建session with log_device_placement并设置为True. sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # 运行这个 op. print sess.run(c)你应该能看见以下输出:
Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus id: 0000:05:00.0 b: /job:localhost/replica:0/task:0/gpu:0 a: /job:localhost/replica:0/task:0/gpu:0 MatMul: /job:localhost/replica:0/task:0/gpu:0 [[ 22. 28.] [ 49. 64.]]如果你不想使用系统来为 operation 指派设备, 而是手工指派设备, 你可以用 with tf.device创建一个设备环境, 这个环境下的 operation 都统一运行在环境指定的设备上.
# 新建一个graph. with tf.device('/cpu:0'): a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # 新建session with log_device_placement并设置为True. sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # 运行这个op. print sess.run(c)你会发现现在 a 和 b 操作都被指派给了 cpu:0.
Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus id: 0000:05:00.0 b: /job:localhost/replica:0/task:0/cpu:0 a: /job:localhost/replica:0/task:0/cpu:0 MatMul: /job:localhost/replica:0/task:0/gpu:0 [[ 22. 28.] [ 49. 64.]]如果你的系统里有多个 GPU, 那么 ID 最小的 GPU 会默认使用. 如果你想用别的 GPU, 可以用下面的方法显式的声明你的偏好:
# 新建一个 graph. with tf.device('/gpu:2'): a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # 新建 session with log_device_placement 并设置为 True. sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # 运行这个 op. print sess.run(c)如果你指定的设备不存在, 你会收到 InvalidArgumentError 错误提示:
InvalidArgumentError: Invalid argument: Cannot assign a device to node 'b': Could not satisfy explicit device specification '/gpu:2' [[Node: b = Const[dtype=DT_FLOAT, value=Tensor<type: float shape: [3,2] values: 1 2 3...>, _device="/gpu:2"]()]]为了避免出现你指定的设备不存在这种情况, 你可以在创建的 session 里把参数 allow_soft_placement 设置为True, 这样 tensorFlow 会自动选择一个存在并且支持的设备来运行 operation.
# 新建一个 graph. with tf.device('/gpu:2'): a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # 新建 session with log_device_placement 并设置为 True. sess = tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=True)) # 运行这个 op. print sess.run(c)如果你想让 TensorFlow 在多个 GPU 上运行, 你可以建立 multi-tower 结构, 在这个结构里每个 tower 分别被指配给不同的 GPU 运行. 比如:
# 新建一个 graph. c = [] for d in ['/gpu:2', '/gpu:3']: with tf.device(d): a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3]) b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2]) c.append(tf.matmul(a, b)) with tf.device('/cpu:0'): sum = tf.add_n(c) # 新建session with log_device_placement并设置为True. sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # 运行这个op. print sess.run(sum)你会看到如下输出:
Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K20m, pci bus id: 0000:02:00.0 /job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: Tesla K20m, pci bus id: 0000:03:00.0 /job:localhost/replica:0/task:0/gpu:2 -> device: 2, name: Tesla K20m, pci bus id: 0000:83:00.0 /job:localhost/replica:0/task:0/gpu:3 -> device: 3, name: Tesla K20m, pci bus id: 0000:84:00.0 Const_3: /job:localhost/replica:0/task:0/gpu:3 Const_2: /job:localhost/replica:0/task:0/gpu:3 MatMul_1: /job:localhost/replica:0/task:0/gpu:3 Const_1: /job:localhost/replica:0/task:0/gpu:2 Const: /job:localhost/replica:0/task:0/gpu:2 MatMul: /job:localhost/replica:0/task:0/gpu:2 AddN: /job:localhost/replica:0/task:0/cpu:0 [[ 44. 56.] [ 98. 128.]]cifar10 tutorial 这个例子很好的演示了怎样用GPU集群训练.
==============programe with GPU==================
下面是带GPU的Minist程序代码:
# encoding=utf8 from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_string('data_dir', '/tmp/data/', 'Directory for storing data') global mnist mnist = input_data.read_data_sets('/home/yuan/testMinist', one_hot=True) # mnist = input_data.read_data_sets('/home/yuan/Xia', one_hot=True) #远程下载MNIST数据,建议先下载好并保存在MNIST_data目录下 def DownloadData(): global mnist #mnist = input_data.read_data_sets('/home/yuan/testMinist', one_hot=True) #编码格式:one-hot #print(mnist.train.images.shape, mnist.train.labels.shape) #print(mnist.test.images.shape, mnist.test.labels.shape) #print(mnist.validation.images.shape, mnist.validation.labels.shape) def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def Train(): with tf.device('/cpu:0'): x = tf.placeholder("float", shape=[None, 784]) #构建占位符,代表输入的图像,None表示样本的数量可以是任意的 W = tf.Variable(tf.zeros([784,10])) #构建一个变量,代表训练目标weights,初始化为0 b = tf.Variable(tf.zeros([10])) y_ = tf.placeholder("float", shape=[None, 10]) config = tf.ConfigProto() config.gpu_options.allow_growth = True gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) #sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) #sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) #sess = tf.InteractiveSession(config=tf.ConfigProto(gpu_options=gpu_options)) sess = tf.InteractiveSession(config=config) #sess = tf.Session(config=config) with tf.device('/gpu:0'): #with tf.device('/cpu:0'): #sess = tf.InteractiveSession() #Step 1 #定义算法公式Softmax Regression # = tf.placeholder("float", shape=[None, 784]) #构建占位符,代表输入的图像,None表示样本的数量可以是任意的 # = tf.Variable(tf.zeros([784,10])) #构建一个变量,代表训练目标weights,初始化为0 #b = tf.Variable(tf.zeros([10])) #构建一个变量,代表训练目标biases,初始化为0 #y = tf.nn.softmax(tf.matmul(x, W) + b) #构建了一个softmax的模型:y = softmax(Wx + b),y指样本标签的预测值 W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) # Now image size is reduced to 7*7 W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) #Step 2 #定义损失函数,选定优化器,并指定优化器优化损失函数 #y_ = tf.placeholder(tf.float32, [None, 10]) # 构建占位符,代表样本标签的真实值 #y_ = tf.placeholder("float", shape=[None, 10]) # 交叉熵损失函数 with tf.device('/cpu:0'): cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) #y = tf.matmul(x, W) + b #cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) # 使用梯度下降法(0.01的学习率)来最小化这个交叉熵损失函数 #train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #Step 3 #使用随机梯度下降训练数据 #tf.global_variables_initializer().run() #when the comple enviroment is python3, changing global_variables_initializer #tf.initialize_all_variables().run() #when the comple enviroment is python2, changing initialize_all_variables() #for i in range(10000): #迭代次数为1000 # batch_xs, batch_ys = mnist.train.next_batch(100) #使用minibatch的训练数据,一个batch的大小为100 # sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) #用训练数据替代占位符来执行训练 #Step 4 #在测试集上对准确率进行评测 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) #tf.argmax()返回的是某一维度上其数据最大所在的索引值,在这里即代表预测值和真值 #accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) #用平均值来统计测试准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #用平均值来统计测试准确率 #sess.run(tf.initialize_all_variables()) # when compile in python2,changing this. sess.run(tf.global_variables_initializer()) # when compile in python3,changing this. #print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) #打印测试信息 for i in range(2000): #迭代次数为1000 #batch_xs, batch_ys = mnist.train.next_batch(50) #使用minibatch的训练数据,一个batch的大小为100 batch = mnist.train.next_batch(50) #sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) #用训练数据替代占位符来执行训练 if i0 == 0: # this step is conducted when the condition needs train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) print ("step %d, training accuracy %.3f" %(i, train_accuracy)) # the output the training accuracy when the condition needs train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) # this step is used to get an trained model #sess.close() print ("Training finished") print ("test accuracy %.3f" % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) if __name__ == '__main__': DownloadData(); Train();
==============programe without GPU, only using CPU==================
下面是带CPU的Minist程序代码:#!/usr/bin/python # -*- coding: utf-8 -*- import input_data mnist = input_data.read_data_sets('/home/yuan/testMinist', one_hot=True) import tensorflow as tf import sys #from tensorflow.examples.tutorials.mnist import input_data from tensorflow.examples.tutorials.mnist import input_data #print("step",mnist) #print("step",mnist.train) #print("step",mnist) def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) sess = tf.InteractiveSession() x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10]) W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) # Now image size is reduced to 7*7 W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess.run(tf.initialize_all_variables()) for i in range(2000): # the iterate steps for array batch = mnist.train.next_batch(50) #print("the format shape is:",batch[0].shape) #print("the format size is:",batch[0].size) #print("the format 2 is:",batch[0]) #print("the format 2 is:",batch[0].length) #print("the format 2 is:",batch.size) if i0 == 0: # this step is conducted when the condition needs train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) print "step %d, training accuracy %.3f"%(i, train_accuracy) # the output the training accuracy when the condition needs train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) # this step is used to get an trained model print "Training finished" print "test accuracy %.3f" % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})