转载请注明出处：

参考网址：

tensorflow中，在一个函数内配置完GPU，tf分配了显存，等函数执行完，显存不会释放（貌似torch7中也一样。。。）。第二个参考网址指出：

As for the original problem, currently the Allocator in the GPUDevice belongs to the ProcessState, which is essentially a global singleton. The first session using GPU initializes it, and frees itself when the process shuts down. Even if a second session chooses a different GPUOptions, it would not take effect.

第一个session对GPU初始化后，即便释放了显存，第二个sess使用不同的GPU选项来初始化GPU，也不会起效。

第一个网址Oli Blum指出，use processes and shut them down after the computation才能释放显存。具体代码如下（可以参考第一个网址）：

1 import tensorflow as tf 2 import multiprocessing 3 import numpy as np 4  5 def run_tensorflow(): 6  7     n_input = 10000 8     n_classes = 1000 9 10     # Create model11     def multilayer_perceptron(x, weight):12         # Hidden layer with RELU activation13         layer_1 = tf.matmul(x, weight)14         return layer_115 16     # Store layers weight & bias17     weights = tf.Variable(tf.random_normal([n_input, n_classes]))18 19 20     x = tf.placeholder("float", [None, n_input])21     y = tf.placeholder("float", [None, n_classes])22     pred = multilayer_perceptron(x, weights)23 24     cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))25     optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)26 27     init = tf.global_variables_initializer()28 29     with tf.Session() as sess:30         sess.run(init)31 32         for i in range(100):33             batch_x = np.random.rand(10, 10000)34             batch_y = np.random.rand(10, 1000)35             sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})36 37     print "finished doing stuff with tensorflow!"38 39 40 if __name__ == "__main__":41 42     # option 1: execute code with extra process43     p = multiprocessing.Process(target=run_tensorflow)44     p.start()45     p.join()46 47     # wait until user presses enter key48     raw_input()49 50     # option 2: just execute the function51     run_tensorflow()52 53     # wait until user presses enter key54     raw_input()

使用multiprocessing.Process运行run_tensorflow后，显存会自动释放，但是如果直接执行run_tensorflow，显存不会自动释放。当然，该函数计算量较小，如果显卡太好，可能看不到运行multiprocessing.Process后，显存分配、计算并释放的过程，感觉就像没有运行一样。。。