Assigning a single GPU on a multi-GPU system

If you have more than one GPU in your system, the GPU with the lowest ID will be selected by default. If you would like to run on a different GPU, you will need to specify the preference explicitly.

For example, we can try to change the GPU assignation in the previous code:

with tf.device('/gpu:1'): 
    a = tf.placeholder(tf.float32, [10000, 10000]) 
    b = tf.placeholder(tf.float32, [10000, 10000]) 
    c1.append(matpow(a, n)) 
    c1.append(matpow(b, n))

In this way, we are telling GPU to execute the kernel function.

If the device we have specified does not exist (as in my case), you will get the following error message on the console (or terminal):

InvalidArgumentError : 
InvalidArgumentError (see above for traceback): Cannot assign a device to node 'Placeholder_1': Could not satisfy explicit device specification '/device:GPU:1' because...