Verifying if GPU is actually used in Keras/Tensorflow, not just verified as present

Question

I\'ve just built a deep learning rig (AMD 12 core threadripper; GeForce RTX 2080 ti; 64Gb RAM). I originally wanted to install CUDnn and CUDA on Ubuntu 19.0, but the install

Answer 1

You can see the following details here.
Based on the documentation:

If a TensorFlow operation has both CPU and GPU implementations, 
by default, the GPU devices will be given priority when the operation is assigned to a device.
For example, tf.matmul has both CPU and GPU kernels. 
On a system with devices CPU:0 and GPU:0, the GPU:0 device will be selected to run tf.matmul unless you explicitly request running it on another device.

Logging device placement

tf.debugging.set_log_device_placement(True)

# Create some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)

Example Result
Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)

For Manual Device placement

tf.debugging.set_log_device_placement(True)

# Place tensors on the CPU
with tf.device('/GPU:0'):
  a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])

c = tf.matmul(a, b)
print(c)

Example Result: 
Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)