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

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.

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

• CMake v3.0 and above
• C++11 Compliant compiler
• Linux: Ubuntu 16.04 and newer
• Transparent Huge Pages must be enabled.
  • SLIDE requires approximately 900 2MB pages, and 10 1GB pages: (Instructions)

• For simplicity, please refer to the our Docker image with all environments installed. To replicate the experiment without setting Hugepages, please download Amazon-670K in path /home/code/HashingDeepLearning/dataset/Amazon

• Also, note that only Skylake or newer architectures support Hugepages. For older Haswell processors, we need to remove the flag -mavx512f from the OPT_FLAGS line in Makefile. You can also revert to the commit 2d10d46b5f6f1eda5d19f27038a596446fc17cee to ignore the HugePages optimization and still use SLIDE (which could lead to a 30% slower performance).

• This version builds all dependencies (which currently are ZLIB and CNPY).

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