GithubHelp home page GithubHelp logo

pwais / hashingdeeplearning Goto Github PK

View Code? Open in Web Editor NEW

This project forked from keroro824/hashingdeeplearning

0.0 2.0 0.0 22.49 MB

Codebase for "SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems"

License: MIT License

C++ 78.66% C 0.91% Python 14.91% Makefile 1.15% CMake 4.37%

hashingdeeplearning's Introduction

SLIDE

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

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

  • 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)

Notes:

  • 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).

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

hashingdeeplearning's People

Contributors

iitkgpanshu avatar jcfarwe avatar keroro824 avatar ottovonxu avatar rahulunair avatar sarthakpati avatar tharun24 avatar wrathematics avatar xman avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.