CUDA is the API layer provided by NVIDIA, using the parallel nature of the GPU. When this is installed, drivers for the hardware are also installed. First, download the CUDA library from the NVIDIA-portal: https://developer.nvidia.com/cuda-downloads.

Go through the instructions on the page, download the driver, and follow the installation instructions. Here is the screenshot of Ubuntu CUDA and the installation instructions:

These commands would have installed the cuda-drivers and the other CUDA APIs required.

The CUDNN library provides primitives for deep learning algorithms. Since this package is provided by NVIDIA, it is highly optimized for their hardware and runs faster. Several standard routines for deep learning are provided in this package. These packages are used by famous deep learning libraries such as tensorflow, caffe, and so on. In the next section, instructions are provided for installing CUDNN. You can download CUDNN from the NVIDIA portal at https://developer.nvidia.com/rdp/cudnn-download.

Copy the relevant files to the CUDA folders, making them faster to run on GPUs. We will not use CUDA and CUDNN libraries directly. Tensorflow uses these to work on GP-GPU with optimized routines.

Install tensorflow using pip3 for the CPU using the following command:

sudo pip3 install tensorflow  

If you are using GPU hardware and have installed CUDA and CUDNN, install the GPU version of the tensorflow with the following command:

sudo pip3 install tensorflow-gpu

Now the tensorflow is installed and ready for use. We will try out a couple of examples to understand how TensorFlow works.

We will do an example using TensorFlow directly in the Python shell. In this example, we will print Hello, TensorFlow using TensorFlow.

         python3

        >>> import tensorflow as tf

        >>> hello = tf.constant('Hello, TensorFlow!')

        >>> session = tf.Session()

The session can be run with the variable hello as the parameter.

        >>> print(session.run(hello))

It should print the following:

Hello, TensorFlow!

Let us look at one more example to understand how the session and graph work.

Here is another simple example of how TensorFlow is used to add two numbers.

        import tensorflow as tf

The preceding import will be necessary for all the latter examples. It is assumed that the reader has imported the library for all the examples. A placeholder can be defined in the following manner. The placeholders are not loaded when assigned. Here, a variable is defined as a placeholder with a type of float32. A placeholder is an empty declaration and can take values when a session is run.

        x = tf.placeholder(tf.float32)
        y = tf.placeholder(tf.float32)

        z = x + y

        session = tf.Session()

        values = {x: 5.0, y: 4.0}

        result = session.run([z], values)
        print(result)

This program should print [9.0] as the result of the addition.

It's understandable that this is not the best way to add two numbers. This example is to understand how tensors and operations are defined in TensorFlow. Imagine how difficult it will be to use a trillion numbers and add them. TensorFlow enables that scale with ease with the same APIs. In the next section, we will see how to install and use TensorBoard and TensorFlow serving.

TensorBoard is a suite of visualization tools for training deep learning-based models with TensorFlow. The following data can be visualized in TensorBoard:

Tensorboard is installed along with TensorFlow. Go to the python3 prompt and type the following command, similar to the previous example, to start using Tensorboard:

x = tf.placeholder(tf.float32, name='x')
y = tf.placeholder(tf.float32, name='y')
z = tf.add(x, y, name='sum')

Note that an argument name has been provided as an extra parameter to placeholders and operations. These are names that can be seen when we visualize the graph. Now we can write the graph to a specific folder with the following command in TensorBoard:

session = tf.Session()
summary_writer = tf.summary.FileWriter('/tmp/1', session.graph)

This command writes the graph to disk to a particular folder given in the argument. Now Tensorboard can be invoked with the following command:

tensorboard --logdir=/tmp/1

Any directory can be passed as an argument for the logdir option where the files are stored. Go to a browser and paste the following URL to start the visualization to access the TensorBoard:

http://localhost:6006/

The browser should display something like this:

The TensorBoard visualization in the browser window

The graph of addition is displayed with the names given for the placeholders. When we click on them, we can see all the particulars of the tensor for that operation on the right side. Make yourself familiar with the tabs and options. There are several parts in this window. We will learn about them in different chapters. TensorBoard is one the best distinguishing tools in TensorFlow, which makes it better than any other deep learning framework.

TensorFlow Serving is a tool in TensorFlow developed for deployment environments that are flexible, providing high latency and throughput environments. Any deep learning model trained with TensorFlow can be deployed with serving. Install the Serving by running the following command:

sudo apt-get install tensorflow-model-server

Step-by-step instructions on how to use serving will be described in Chapter 3, Image Retrieval. Note that the Serving is easy to install only in Ubuntu; for other OSes, please refer to https://www.tensorflow.org/serving/setup. The following figure illustrates how TensorFlow Serving and TensorFlow interact in production environments:

Many models can be produced by the training process, and Serving takes care of switching them seamlessly without any downtime. TensorFlow Serving is not required for all the following chapters, except for Chapter 3, Image Retrieval and Chapter 10, Deployment.