After everything we've just been through together, you'll be pleased to see how straightforward installing TensorFlow and Keras now is.

Let's start with installing TensorFlow

The installation of TensorFlow can be done using the following code:

pip install --upgrade tensorflow-gpu 

Before we install Keras, let's test our TensorFlow installation. To do this, I'll be using some sample code from the TensorfFow website and the IPython interpreter.

Start the IPython interpreter by typing IPython at the bash prompt. Once IPython has started, let's attempt to import TensorFlow. The output would look like the following:

In [1]: import tensorflow as tf
In [2]: 

If importing TensorFlow results in an error, troubleshoot the steps you have followed so far. Most often when TensorFlow cannot be imported, the CUDA or cuDNN might not be installed correctly.

Now that we've successfully installed TensorFlow, we will run a tiny bit of code in IPython that will verify we can run computations on the GPU:

a = tf.constant([1.0,</span> 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)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))

If everything goes as we hope, we will see lots of indications that our GPU is being used. I have included some output here and highlighted the evidence to draw your attention to it. Your output will likely be different based on hardware, but you should see similar evidence the one shown here:

/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0, compute capability: 3.7
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
: I tensorflow/core/common_runtime/placer.cc:874] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:GPU:0
 b: (Const): /job:localhost/replica:0/task:0/device:GPU:0
: I tensorflow/core/common_runtime/placer.cc:874] b: (Const)/job:localhost/replica:0/task:0/device:GPU:0
 a: (Const): /job:localhost/replica:0/task:0/device:GPU:0
: I tensorflow/core/common_runtime/placer.cc:874] a: (Const)/job:localhost/replica:0/task:0/device:GPU:0
 [[ 22. 28.]
 [ 49. 64.]]

In the preceding output, we can see that tensors a and b, as well as the matrix multiplication operation, were assigned the the GPU. If there was a problem with accessing the GPU, the output might look as follows:

I tensorflow/core/common_runtime/placer.cc:874] b_1: (Const)/job:localhost/replica:0/task:0/device:CPU:0
a_1: (Const): /job:localhost/replica:0/task:0/device:CPU:0
I tensorflow/core/common_runtime/placer.cc:874] a_1: (Const)/job:localhost/replica:0/task:0/device:CPU:0

Here we can see the tensors b_1 and a_1 were assigned to the CPU rather than the GPU. If this happens there is a problem with your installation of TensorFlow, CUDA, or cuDNN.

If you've made it this far, you have a working installation of TensorFlow. The only remaining task is to install Keras.

The installation of Keras can be done with the help of the following code:

pip install keras

And that's it! Now we're ready to build deep neural networks in Keras and TensorFlow.