Autotuning and Predictive Autotuning

predtuner performs autotuning on program approximation knobs using an error-predictive proxy in place of the original program, to greatly speedup autotuning while getting results comparable in quality. current_version == 0.3.

Installation

predtuner requires python >= 3.6 and pip, preferrably pip >= 20. To install this package from source, at the root directory of this repository, do:

python3 -m pip install -e ./

With the flag -e, any changes to code in this repo is reflected on the installed version automatically. It can be omitted if you don't intend to modify the code in this package.

Getting Started

The documentation page contains a full tutorial. Build the documentation by:

pip install sphinx sphinx_rtd_theme sphinx_autodoc_typehints
cd doc
make html

The documentation page will be created as doc/build/html/index.html. You can open this in the browser and browse to "Getting Started" section.

Model Data for Example / Testing

predtuner contains 10 demo models which are also used in tests.

• Download and extract this file containing all 10 models, for testing purposes.
• The "Getting Started" example on the documentation page only uses VGG16-CIFAR10. If you don't need the other models, get the data for VGG16-CIFAR10 here.

In either case, there should be a model_params/ folder at the root of repo after extraction.