Home

Awesome

SLIDE

The SLIDE package contains the source code for reproducing the main experiments in this paper.

For Optimized Code on CPUs (with AVX, BFloat and other memory optimization) from the newer paper please refer here

Dataset

The Datasets can be downloaded in Amazon-670K. Note that the data is sorted by labels so please shuffle at least the validation/testing data.

TensorFlow Baselines

We suggest directly get TensorFlow docker image to install TensorFlow-GPU. For TensorFlow-CPU compiled with AVX2, we recommend using this precompiled build.

Also there is a TensorFlow docker image specifically built for CPUs with AVX-512 instructions, to get it use:

docker pull clearlinux/stacks-dlrs_2-mkl    

config.py controls the parameters of TensorFlow training like learning rate. example_full_softmax.py, example_sampled_softmax.py are example files for Amazon-670K dataset with full softmax and sampled softmax respectively.

Run

python python_examples/example_full_softmax.py
python python_examples/example_sampled_softmax.py

Running SLIDE

Dependencies

Notes:

Commands

Change the paths in ./SLIDE/Config_amz.csv appropriately.

git clone https://github.com/sarthakpati/HashingDeepLearning.git
cd HashingDeepLearning
mkdir bin
cd bin
cmake ..
make
./runme ../SLIDE/Config_amz.csv