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dmlc.github.io's Introduction

Distributed Machine Learning Common Codebase

Build Status Documentation Status GitHub license

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 Join the chat at https://gitter.im/dmlc/dmlc-core

What's New

Contents

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

dmlc.github.io's People

Contributors

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dmlc.github.io's Issues

Site is poisoned

If you go dmlc.github.io, you will be redirected to dmlc.ml which is not working.

When I went to dmlc.github.io first time, I was redirected to the fishing site (I cannot reproduce this behavior)

RNN Shakespeare example giving error

This code in the tutorial leads to the error:

model <- mx.lstm(X.train, X.val, 
                 ctx=mx.gpu(),
                 num.round=num.round, 
                 update.period=update.period,
                 num.lstm.layer=num.lstm.layer, 
                 seq.len=seq.len,
                 num.hidden=num.hidden, 
                 num.embed=num.embed, 
                 num.label=vocab,
                 batch.size=batch.size, 
                 input.size=vocab,
                 initializer=mx.init.uniform(0.1), 
                 learning.rate=learning.rate,
                 wd=wd,
                 clip_gradient=clip_gradient)

Error: Error in check.data(train.data, batch.size, TRUE) : could not find function "check.data"
Is this function from an unspecific package?

Additionally, there are several missing files referenced in the tutorial:
rnn_model.R, rnn.R, lstm.R, etc

best,

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