Tensorflow on O2

Due to recent developments in deep learning and related topics, tensorflow and its components have been widely requested by the user community. Due to the nature of the package however, we have decided it is best for the user to manage their own installation to ensure that they can quickly modify or upgrade tensorflow to their own needs without waiting for Research Computing to handle version changes. This page therefore serves to provide basic instructions on how to install tensorflow without elevated privileges, into a local directory that is owned by the user.

Basic Installation

Early on, tensorflow was quite difficult to install into a shared computing environment such as O2. However, the developers have since made it far friendlier to set up for the average user that is not computing locally. All that needs to be done is to invoke pip to complete the installation.

Tensorflow is compatible with both Python 3 as of the writing of this document. In order to install it, it is strongly recommended to set up a virtual environment.

First, request an interactive session:

If you are planning to use Tensorflow immediately after installing it inside of the interactive session, it may be wise to increase the memory requirement when submitting the request.

Once you're on a compute node, load the prerequisite module: