（原）tensorflow中函数执行完毕，显存不自动释放

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转载请注明出处：

http://www.cnblogs.com/darkknightzh/p/7608916.html

参考网址：

https://stackoverflow.com/questions/39758094/clearing-tensorflow-gpu-memory-after-model-execution

https://github.com/tensorflow/tensorflow/issues/1727#issuecomment-285815312s

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）：

import tensorflow as tf
import multiprocessing
import numpy as np def run_tensorflow(): n_input = 10000
n_classes = 1000 # Create model
def multilayer_perceptron(x, weight):
# Hidden layer with RELU activation
layer_1 = tf.matmul(x, weight)
return layer_1 # Store layers weight & bias
weights = tf.Variable(tf.random_normal([n_input, n_classes])) x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
pred = multilayer_perceptron(x, weights) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) init = tf.global_variables_initializer() with tf.Session() as sess:
sess.run(init) for i in range(100):
batch_x = np.random.rand(10, 10000)
batch_y = np.random.rand(10, 1000)
sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) print "finished doing stuff with tensorflow!" if __name__ == "__main__": # option 1: execute code with extra process
p = multiprocessing.Process(target=run_tensorflow)
p.start()
p.join() # wait until user presses enter key
raw_input() # option 2: just execute the function
run_tensorflow() # wait until user presses enter key
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。。。