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
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.pyFor 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