Setup 

Contents

Setup
- Installation
  - Ubuntu with sudo privileges
  - Docker
- Code Overview

Installation 

Ubuntu with sudo privileges 

Recursively clone the goldeneye repository.

git clone --recurse-submodules git@github.com:ma3mool/goldeneye.git

Download ninja-build which is needed for qtorch.
sudo apt install ninja-build
Download the other project dependencies. Please make sure you are inside the goldeneye folder when applying this command.
pip install -r requirements.txt
Setup environment variable (replace with the directory where the imagenet dataset is downloaded).
ML_DATASETS=/dir/to/imagenet/

Docker 

Recursively clone the goldeneye repository.

git clone --recurse-submodules git@github.com:ma3mool/goldeneye.git

Pull the goldeneye docker image and rename it to simply the next steps

docker pull goldeneyetool/goldeneye:latest
docker image tag goldeneyetool/goldeneye goldeneye

Within the goldeneye folder, run the shell on the pulled docker image. Make sure to replace [/path/to/imagenet] with the actual path to your downloaded imagenet dataset.

cd goldeneye
docker run -ti
    --mount type=bind,source=`pwd`/src/,target=/src
    --mount type=bind,source=`pwd`/val/,target=/val
    --mount type=bind,source=`pwd`/scripts/,target=/scripts
    --mount type=bind,source=[/path/to/imagenet],target=/datasets/imagenet
    goldeneye

Code Overview 

Scripts Folder (scripts)

The scripts folder includes wrappers around the goldeneye framework to simplify its use.

Source Folder (src)

The src folder contains all of the goldeneye core logic such as number system implementation and error injection routines.

Validation Folder (val)

The val folder is used for unit-testing the code. You can run it using pytest to check that the installation process was successful.