This repository contains raw experimental results in the CK format for the image classification workflow from the ReQuEST tournament at ASPLOS'18 on reproducible SW/HW co-design of deep learning (speed, accuracy, energy, costs). The live ReQuEST scoreboard shows a subset of these results.
The minimal installation requires:
- Python 2.7 or 3.3+ (limitation is mainly due to unitests)
- Git command line client.
You can install CK in your local user space as follows:
$ git clone http://github.com/ctuning/ck
$ export PATH=$PWD/ck/bin:$PATH
$ export PYTHONPATH=$PWD/ck:$PYTHONPATH
You can also install CK via PIP with sudo to avoid setting up environment variables yourself:
$ sudo pip install ck
$ ck pull repo:ck-request-asplos18-results-iot-farm
$ ck ls ck-request-asplos18-results-iot-farm:experiment:*
$ ck replay experiment:{name from above list}
Note that CK will try to automatically rebuild experimental setup by detecting already installed software dependencies and installing missing ones using shared CK packages.
If you want to have a software setup as close to the original one as possible, install packages before running replay as described in the ReadMe of the related CK workflow.
$ ck dashboard request.asplos18 --results=ck-request-asplos18-results-iot-farm