dmlc / mxnet-deepmark

Benchmark speed and other issues internally, before push to deep-mark

Distributed Machine Learning Common Codebase

DMLC-Core is the backbone library to support all DMLC projects, offers the bricks to build efficient and scalable distributed machine learning libraries.

Developer Channel

What's New

• Note on Parameter Module for Machine Learning

Contents

• Documentation and Tutorials
• Contributing

Known Issues

• RecordIO format is not portable across different processor endians. So it is not possible to save RecordIO file on a x86 machine and then load it on a SPARC machine, because x86 is little endian while SPARC is big endian.

Contributing

Contributing to dmlc-core is welcomed! dmlc-core follows google's C style guide. If you are interested in contributing, take a look at feature wishlist and open a new issue if you like to add something.

• DMLC-Core uses C++11 standard. Ensure that your C++ compiler supports C++11.
• Try to introduce minimum dependency when possible

CheckList before submit code

• Type make lint and fix all the style problems.
• Type make doc and fix all the warnings.

NOTE

deps:

libcurl4-openssl-dev

mxnet-deepmark's Issues

Outstanding items on CuDNN

• CuDNN auto tuning @piiswrong
• CuDNN 3D conv/pool
• CuDNN LSTM
• CuDNN Dropout

c3d

error: Volume convolution is not implmented in mshadow.
how to use the c3d in mxnet?

c3d

error: Volume convolution is not implmented in mshadow.
how to use the c3d in mxnet?