2. Prerequisites

In order to use IPUs through Gcore Cloud, you must be familiar with developing machine-learning applications using industry-standard frameworks such as TensorFlow or PyTorch. You must also have a knowledge of programming languages such as Python or C++.

The Graphcore Developer page has links to a variety of resources to assist you getting started programming for the IPU. A good place to start is the Fundamentals of the IPU and Poplar video, which introduces the IPU architecture and programming model.

2.1. Linux and Python commands

You will also need to have some familiarity with the Linux command line and some common Linux tools. Some of the tasks that you may need to perform are:

• Navigating the file system using commands such as ls and cd, and shortcuts such as ~.

• Using login files such as .profile and .bashrc to configure your environment (for example, setting paths and performing actions that need to be done on every login).

• Understanding file ownership and permissions for users and groups.

• Changing file permissions to allow sharing between multiple users in your organisation (commands: chmod and chown).

• Logging in with ssh, configuring ssh and using scp to copy files to and from the vPod.

• Using cp and rsync to copy public examples and data to your user workspace.

• Creating a Python “virtual environment” with the virtualenv command; this keeps the Python packages that you install for an application or framework separate. This can avoid problems such as conflicting versions and dependencies.