Computation using data flow graphs for scalable machine learning
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README.md




ILA-SCNN

This repository contains the source code of our paper: Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks Our implementation extents tensorflow by providing GPU implementations for layers that are able efficiently process large-scale, sparse data. For more details please refer to our paper.

Installation

After cloning this repository:

  1. Follow: Installing TensorFlow to install tensorflow from source code.

  2. build our sparse modules:

bazel build -c opt --config=cuda --strip=never -s //tensorflow/core/user_ops:direct_sparse_conv_kd.so
  1. set environment variables
echo 'export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$HOME/src/ILA-SCNN/bazel-bin/tensorflow/core/user_ops"' >> ~/.bashrc
echo 'export PYTHONPATH="$PYTHONPATH:$HOME/src/ILA-SCNN/tensorflow/core/user_ops"' >> ~/.bashrc
source ~/.bashrc

Examples

We provide the following examples:

  1. Modelnet Application
  2. MNIST Application

The correct data sets will be downloaded automatically when running the scripts.