Distiller Installation

These instructions will help get Distiller up and running on your local machine.

You may also want to refer to these resources:

• Image classification dataset installation instructions
• Jupyter installation instructions

Notes: - Distiller has only been tested on Ubuntu 16.04 LTS, and with Python 3.5. - If you are not using a GPU, you might need to make small adjustments to the code.

Clone Distiller

Clone the Distiller code repository from github:

$ git clone https://github.com/IntelLabs/distiller.git

The rest of the documentation that follows, assumes that you have cloned your repository to a directory called distiller.

Create a Python virtual environment

We recommend using a Python virtual environment, but that of course, is up to you. There's nothing special about using Distiller in a virtual environment, but we provide some instructions, for completeness.
Before creating the virtual environment, make sure you are located in directory distiller. After creating the environment, you should see a directory called distiller/env.

Using virtualenv

If you don't have virtualenv installed, you can find the installation instructions here.

To create the environment, execute:

$ python3 -m virtualenv env

This creates a subdirectory named env where the python virtual environment is stored, and configures the current shell to use it as the default python environment.

Using venv

If you prefer to use venv, then begin by installing it:

$ sudo apt-get install python3-venv

Then create the environment:

$ python3 -m venv env

As with virtualenv, this creates a directory called distiller/env.

Activate the environment

The environment activation and deactivation commands for venv and virtualenv are the same.
!NOTE: Make sure to activate the environment, before proceeding with the installation of the dependency packages:

$ source env/bin/activate

Install the package

Finally, install the Distiller package and its dependencies using pip3:

$ cd distiller
$ pip3 install -e .

This installs Distiller in "development mode", meaning any changes made in the code are reflected in the environment without re-running the install command (so no need to re-install after pulling changes from the Git repository).

PyTorch is included in the requirements.txt file, and will currently download PyTorch version 1.0.1 for CUDA 9.0. This is the setup we've used for testing Distiller.