These instructions will help get Distiller up and running on your local machine.
You may also want to refer to these resources:
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 the Distiller code repository from github:
$ git clone https://github.com/NervanaSystems/distiller.git
The rest of the documentation that follows, assumes that you have cloned your repository to a directory called
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
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.
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
Activate the environment
The environment activation and deactivation commands for
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
$ 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.