How To Install and Use TensorFlow on Ubuntu 16.04

Updated on March 23, 2022
How To Install and Use TensorFlow on Ubuntu 16.04
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Ubuntu 16.04


TensorFlow is an open-source machine learning software built by Google to train neural networks. TensorFlow’s neural networks are expressed in the form of stateful dataflow graphs. Each node in the graph represents the operations performed by neural networks on multi-dimensional arrays. These multi-dimensional arrays are commonly known as “tensors”, hence the name TensorFlow.

TensorFlow is a deep learning software system. TensorFlow works well for information retrieval, as demonstrated by Google in how they do search ranking in their machine-learning artificial intelligence system, RankBrain. TensorFlow can perform image recognition, as shown in Google’s Inception, as well as human language audio recognition. It’s also useful in solving other problems not specific to machine learning, such as partial differential equations.

The TensorFlow architecture allows for deployment on multiple CPUs or GPUs within a desktop, server or mobile device. There are also extensions for integration with CUDA, a parallel computing platform from Nvidia. This gives users who are deploying on a GPU direct access to the virtual instruction set and other elements of the GPU that are necessary for parallel computational tasks.

In this tutorial, you’ll install TensorFlow’s “CPU support only” version. This installation is ideal for people looking to install and use TensorFlow, but who don’t have an Nvidia graphics card or don’t need to run performance-critical applications.

You can install TensorFlow several ways. Each method has a different use case and development environment:

  • Python and Virtualenv: In this approach, you install TensorFlow and all of the packages required to use TensorFlow in a Python virtual environment. This isolates your TensorFlow environment from other Python programs on the same machine.
  • Native pip: In this method, you install TensorFlow on your system globally. This is recommended for people who want to make TensorFlow available to everyone on a multi-user system. This method of installation does not isolate TensorFlow in a contained environment and may interfere with other Python installations or libraries.
  • Docker: Docker is a container runtime environment and completely isolates its contents from preexisting packages on your system. In this method, you use a Docker container that contains TensorFlow and all of its dependencies. This method is ideal for incorporating TensorFlow into a larger application architecture already using Docker. However, the size of the Docker image will be quite large.

In this tutorial, you’ll install TensorFlow in a Python virtual environment with virtualenv. This approach isolates the TensorFlow installation and gets things up and running quickly. Once you complete the installation, you’ll validate your installation by running a short TensorFlow program and then use TensorFlow to perform image recognition.


Before you begin this tutorial, you’ll need the following:

Step 1 — Installing TensorFlow

In this step we are going to create a virtual environment and install TensorFlow.

First, create a project directory called tf-demo:

  1. mkdir ~/tf-demo

Navigate to your newly created tf-demo directory:

  1. cd ~/tf-demo

Then create a new virtual environment called tensorflow-dev. Run the following command to create the environment:

  1. python3 -m venv tensorflow-dev

This creates a new tensorflow-dev directory which will contain all of the packages that you install while this environment is activated. It also includes pip and a standalone version of Python.

Now activate your virtual environment:

  1. source tensorflow-dev/bin/activate

Once activated, you will see something similar to this in your terminal:

(tensorflow-dev)username@hostname:~/tf-demo $

Now you can install TensorFlow in your virtual environment.

Run the following command to install and upgrade to the newest version of TensorFlow available in PyPi:

  1. pip3 install --upgrade tensorflow

TensorFlow will install:

Collecting tensorflow Downloading tensorflow-1.4.0-cp36-cp36m-macosx_10_11_x86_64.whl (39.3MB) 100% |████████████████████████████████| 39.3MB 35kB/s ... Successfully installed bleach-1.5.0 enum34-1.1.6 html5lib-0.9999999 markdown-2.6.9 numpy-1.13.3 protobuf-3.5.0.post1 setuptools-38.2.3 six-1.11.0 tensorflow-1.4.0 tensorflow-tensorboard-0.4.0rc3 werkzeug-0.12.2 wheel-0.30.0

If you’d like to deactivate your virtual environment at any time, the command is:

  1. deactivate

To reactivate the environment later, navigate to your project directory and run source tensorflow-dev/bin/activate.

Now, that you have installed TensorFlow, let’s make sure the TensorFlow installation works.

Step 2 — Validating Installation

To validate the installation of TensorFlow, we are going to run a simple program in TensorFlow as a non-root user. We will use the canonical beginner’s example of “Hello, world!” as a form of validation. Rather than creating a Python file, we’ll create this program using Python’s interactive console.

To write the program, start up your Python interpreter:

  1. python

You will see the following prompt appear in your terminal


This is the prompt for the Python interpreter, and it indicates that it’s ready for you to start entering some Python statements.

First, type this line to import the TensorFlow package and make it available as the local variable tf. Press ENTER after typing in the line of code:

  1. import tensorflow as tf

Next, add this line of code to set the message “Hello, world!”:

  1. hello = tf.constant("Hello, world!")

Then create a new TensorFlow session and assign it to the variable sess:

  1. sess = tf.Session()

Note: Depending on your environment, you might see this output:

2017-06-18 16:22:45.956946: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-06-18 16:22:45.957158: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2017-06-18 16:22:45.957282: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2017-06-18 16:22:45.957404: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2017-06-18 16:22:45.957527: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

This tells you that that you have an instruction set that has the potential to be optimized for better performance with TensorFlow. If you see this, you can safely ignore it and continue.

Finally, enter this line of code to print out the result of running the hello TensorFlow session you’ve constructed in your previous lines of code:

  1. print(sess.run(hello))

You’ll see this output in your console:

Hello, world!

This indicates that everything is working and that you can start using TensorFlow to do something more interesting.

Exit the Python interactive console by pressing CTRL+D.

Now let’s use TensorFlow’s image recognition API to get more familiar with TensorFlow.

Step 3 — Using TensorFlow for Image Recognition

Now that TensorFlow is installed and you’ve validated it by running a simple program, let’s look at TensorFlow’s image recognition capabilities.

In order to classify an image you need to train a model. Then you need to write some code to use the model. To learn more about these concepts, you can take a look at An Introduction to Machine Learning.

TensorFlow provides a repository of models and examples, including code and a trained model for classifying images.

Use Git to clone the TensorFlow models repository from GitHub into your project directory:

  1. git clone https://github.com/tensorflow/models.git

You will see the following output as Git checks out the repository into a new folder called models:

Cloning into 'models'... remote: Counting objects: 8785, done. remote: Total 8785 (delta 0), reused 0 (delta 0), pack-reused 8785 Receiving objects: 100% (8785/8785), 203.16 MiB | 24.16 MiB/s, done. Resolving deltas: 100% (4942/4942), done. Checking connectivity... done.

Switch to the models/tutorials/image/imagenet directory:

  1. cd models/tutorials/image/imagenet

This directory contains the classify_image.py file which uses TensorFlow to recognize images. This program downloads a trained model from tensorflow.org on its first run. Downloading this model requires that you have 200MB of free space available on disk.

In this example, we will classify a pre-supplied image of a Panda. Execute this command to run the image classifier program:

  1. python classify_image.py

You’ll see output similar to this:

giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.89107) indri, indris, Indri indri, Indri brevicaudatus (score = 0.00779) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00296) custard apple (score = 0.00147) earthstar (score = 0.00117)

You have classified your first image using the image recognition capabilities of TensorFlow.

If you’d like to use another image, you can do this by adding the -- image_file argument to your python3 classify_image.py command. For the argument, you’d pass in the absolute path of the image file.


You’ve installed TensorFlow in a Python virtual environment and validated that TensorFlow works by running a couple of examples. You now possess tools that make it possible for you to explore additional topics including Convolutional Neural Networks and Word Embeddings.

TensorFlow’s programmer’s guide is a great resource and reference for TensorFlow development. You can also explore Kaggle, a competitive environment for practical application of machine learning concepts that pit you against other machine learning, data science, and statistics enthusiasts. They have an excellent wiki where you can see and share solutions, some of which are on the cutting edge of statistical and machine learning techniques.

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