Collected experimental results in the CK format from the ReQuEST@ASPLOS'18 tournament on reproducible SW/HW co-design of Pareto-efficient deep learning for the following submission:
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README.md

README.md

compatibility License: CC BY 4.0

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.

References

Installation

Minimal CK installation

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

Install this CK repository and dependencies

$ ck pull repo:ck-request-asplos18-results-iot-farm

List available experiments

$ ck ls ck-request-asplos18-results-iot-farm:experiment:*

Replay 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.

Start dashboard to visualize/compare results

$ ck dashboard request.asplos18 --results=ck-request-asplos18-results-iot-farm

See the live scoreboard

Link

Further discussions