TensorFlow CPU切换到GPU，CPU/GPU互换

TensorFlow CPU切换到GPU，CPU/GPU互换

with tf.Session() as ses:
    with tf.device("/gpu:1"):
        matrix1=tf.constant([[3.,3.]])
        matrix2=tf.constant([[2.],[2.]])
        product=tf.matmul(matrix1,matrix2)

方法二：
<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf sess = tf.Session(config=tf.ConfigProto(device_count={<span class="hljs-string">'gpu'</span>:<span class="hljs-number">0</span>}))

<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf <span class="hljs-keyword">import</span> keras.backend.tensorflow_backend <span class="hljs-keyword">as</span> KTF KTF.set_session(tf.Session(config=tf.ConfigProto(device_count={<span class="hljs-string">'gpu'</span>:<span class="hljs-number">0</span>})))

方法三：使用CUDA_VISIBLE_DEVICES命令行参数，代码如下：

CUDA_VISIBLE_DEVICES="" python3 train.py

Environment Variable Syntax      Results

CUDA_VISIBLE_DEVICES=1           Only device 1 will be seen
CUDA_VISIBLE_DEVICES=0,1         Devices 0 and 1 will be visible
CUDA_VISIBLE_DEVICES="0,1"       Same as above, quotation marks are optional
CUDA_VISIBLE_DEVICES=0,2,3       Devices 0, 2, 3 will be visible; device 1 is masked
CUDA_VISIBLE_DEVICES=""          No GPU will be visible

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