TensorFlow GPU management

In TensorFlow the supported devices are represented as strings. For example:

• /cpu:0: The CPU of your machine
• /gpu:0: The GPU of your machine, if you have one
• /gpu:1: The second GPU of your machine, and so on

The execution flow gives priority when an operation is assigned to a GPU device.

Programming example

To use a GPU in your TensorFlow program, just type the following:

with tf.device("/gpu:0"):

Followed by the setup operations. This line of code will create a new context manager, telling TensorFlow to perform those actions on the GPU.

Let's consider the following example, in which we want to execute the following sum of two matrices, An + Bn.

Define the basic imports:

import numpy as np 
import tensorflow as tf 
import datetime

We can configure a program to find out which devices your operations and tensors are assigned. To realize this, we'll create a session with the following log_device_placement parameter set to True:

log_device_placement = True

Then we fix the n parameter...