GithubHelp home page GithubHelp logo

Compiling NNET examle about mshadow HOT 2 CLOSED

dmlc avatar dmlc commented on May 9, 2024
Compiling NNET examle

from mshadow.

Comments (2)

siemanko avatar siemanko commented on May 9, 2024

ok adding:

template class NNet<cpu>;
template class NNet<gpu>;

Under the definition of NNet class in nnet_ps resolves this issue for me. I think my compiler is not very happy with instantiation of nested templated classes...

Also when I run the code on 4 cpus I barely get any speedup (only about 30% faster than single CPU) - is that expected here? I know that Hogwild code normally scales linearly, but this is not hogwild is it?

Thank you,
Szymon

from mshadow.

tqchen avatar tqchen commented on May 9, 2024

Yes, I think it is normal. This was mainly because the synchronization cost and it is not pure hogwild. When you are running multiple GPUs, you could not freely write to a shared memory region. The demo is mainly for demonstration purpose of mshadow-ps

You will find great speedup for larger problems and real neuralnet that you work on in cxxnet

from mshadow.

Related Issues (20)

Recommend Projects

  • React photo React

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

  • Vue.js photo Vue.js

    🖖 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. 📊📈🎉

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 ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.