cac 1.0.0

Content-Aware Computing library

What's CAC

This repository contains a library of Content-Aware Computing (CAC) by Fujitsu.
CAC is a software technology that aims at easy, high-speed, lightweight, and accurate deep learning processing.

Contents

1. Gradient-Skip

Gradient-Skip is an approach for CNNs to skip backward calculations for layers that enouch converged.
This reduces calculations in backward and communications of gradient.
You can use Gradient-Skip by simply replacing the optimizer with our SGD.

Python Source

Example

2. Automatic Pruner

Automatic Pruner is a pruning tool for neural networks, which can determine the pruning rate of each layer automatically.

Python Source

Example

3. Synchronous-Relaxation

Relaxed Synchronization technique removes slow processes from the group of distributed training and prevent limiting overall training speed due to slow processes.

Python Source

Example

Requirements

Python 3.6 or later

CUDA 10 or later

PyTorch 1.6 or later

Apex

Quick Start

Linux

git clone https://github.com/FujitsuLaboratories/CAC.git
cd CAC
python setup.py install

CAC will be registered in PyPI at a later date and will be able to pip install

pip install -v --disable-pip-version-check --no-cache-dir ./