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配置服务器常用代码

import os

gpu_no = '0' # or '1'

os.environ["CUDA_VISIBLE_DEVICES"] = gpu_no

# os.environ["LD_LIBRARY_PATH"]='/usr/local/cuda-8.0/lib64:'+os.environ["LD_LIBRARY_PATH"]
# print(os.environ["LD_LIBRARY_PATH"])

# 定义TensorFlow配置
config=tf.ConfigProto()

# 配置GPU内存分配方式，按需增长，很关键
config.gpu_options.allow_growth = True

# 配置可使用的显存比例
config.gpu_options.per_process_gpu_memory_fraction = 0.3



with tf.Session(config=config) as sess:
    sess.run(init)
    for epoch in range(11):
        for batch in range(n_batch):
            batch_xs,batch_ys =  mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
    #保存模型
    saver.save(sess,'net/my_net.ckpt')