I am using low level Tensorflow API's for my model training. When I say low level it means I'm defining the
tf.Session() object of the graph and evaluate graph with in this session.
I would like to distribute the model training using
I am able to use
mirroredstrategy() on tensorflow sequential API's using the example shared by tensorflow in their document.
But I am facing difficulty in executing tf low level code using mirror strategy.
I tried to use
distribute.MirrorStrategy() and below are the results of resource utilization:
 GeForce RTX 2080 Ti | 48'C, 40 % | 10771 / 11019 MB | vipin(10763M) gdm(4M)
 GeForce RTX 2080 Ti | 37'C, 0 % | 10376 / 11014 MB | vipin(10327M) gdm(36M) gdm(8M)
Even though model used the memory of both the GPU's, but still GPU1 utilization is 0.
I am not sure about the issue. Even not sure if tensorflow support this.
Please clear my doubts and if possible share the sample code as well.