Setting Up TensorFlow and Jupyter (for the Tutorials)

The easiest way (I found) to setup TensorFlow on your system is by using a Docker install. Its advantages are that it just works out of the box, all dependencies are there and you can start building deep models within minutes! Warning: it does not allow you to accelerate the learning on a GPU though!!

So here are the steps required (on Mac):

  • Wait to download the image :)
  • You should be good to go!
    • You can test that by running python and type in the following;
    • >>> import tensorflow as tf
      >>> hello = tf.constant('Hello, TensorFlow!')
      >>> sess = tf.Session()
      >>> print(
    • To access the Jupyter notebook you need to forward the ports, you can do this by:
docker run -it -p 8888:8888

Happy hacking!


What is TensorFlow?

TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs around machine learning. More info:

What is Docker?

Docker is an open platform for developers and sysadmins to build, ship, and run distributed applications, whether on laptops, data center VMs, or the cloud. More info:

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One thought on “Setting Up TensorFlow and Jupyter (for the Tutorials)”

  1. How do I start the jupyter notebook such that I can use the notebook from the host machine? Ideally I would like to use docker to launch the container and start jupyter in a single command.

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