Pfthb

I recently built my first TensorFlow model (converted from hand-coded python). I'm using tensorflow-gpu, but I only want to use GPU for backprop during training. For everything else I want to use CPU. I've seen this article showing how to force CPU use on a system that will use GPU by default. However, you have to specify every single operation where you want to force CPU use. Instead I'd like to do the opposite. I'd like to default to CPU use, but then specify GPU just for the backprop that I do during training. Is there a way to do that?

Update

Looks like things are just going to run slower over tensorflow because of how my model and scenario are built at present. I tried using a different environment that just uses regular (non-gpu) tensorflow, and it still runs significantly slower than hand-coded python. The reason for this, I suspect, is it's a reinforcement learning model that plays checkers (see below) and makes one single forward prop "prediction" at a time as it plays against a computer opponent. At the time I designed the architecture, that made sense. But it's not very efficient to do predictions one at a time, and less so with whatever overhead there is for tensorflow.

So, now I'm thinking that I'm going to need to change the game playing architecture to play, say, a thousand games simultaneously and run a thousand forward prop moves in a batch. But, man, changing the architecture now is going to be tricky at best.

TensorFlow lets you control device placement with the tf.device context manager.

So for example to run some code on the CPU do

with tf.device('cpu:0'):
 <your code goes here>

Similarly to force GPU usage.

Instead of always running your forward pass on the CPU though you're better off making two graphs: a forward-only cpu-only graph to be used when rolling out the policy and a gpu-only forward-and-backward graph to be used when training.