Can some one explain logging device placement in tensorflow tutorial ? - tensorflow

Question

Link for the tf tutorial
# Creates a graph.
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print sess.run(c)
In above example cpu:0 has assigned to the execution process. With the log_device_placement true. So this is solution for the above code that the have mentioned
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/cpu:0
a: /job:localhost/replica:0/task:0/cpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22. 28.]
[ 49. 64.]]
Now here place holders a, b and the matmul operation c which runs inside a session is inside the device log cpu:o but in the log device description why only MatMul has been executed in gpu:0 ?

Answer 1

That seems like a bug in the documentation, the MatMul operation will be placed on CPU in this case.
Indeed, running the code sample does show this:
import tensorflow as tf
# Creates a graph.
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print sess.run(c)
# And to prove that GPUs exist and can be used
with tf.device('/gpu:0'):
d = tf.random_normal([])
print sess.run(d)
Will show the following placements:
MatMul: (MatMul): /job:localhost/replica:0/task:0/cpu:0
b: (Const): /job:localhost/replica:0/task:0/cpu:0
a: (Const): /job:localhost/replica:0/task:0/cpu:0
random_normal/RandomStandardNormal: (RandomStandardNormal): /job:localhost/replica:0/task:0/gpu:0
I think the documentation bug is that the c = tf.matmul(a, b) statement was supposed to be outside the with tf.device('/cpu:0') scope.