Hello,
I've been packaging up LuaJIT and Torch packages for even easier install using Continuum.IO's "conda" tool (
http://conda.pydata.org/).
The goal is to turn all of the LuaJIT numerical libraries into reliable, fast, architecture-independent, one-line installs.
Here's how you can, from scratch, get all the packages you need to train a basic neural net on the CPU.
First, install anaconda, if you don't have it (you can check by typing "conda help", and if nothing useful comes up, then try the lines below)
# Install Anaconda on Linux:
bash Miniconda-latest-Linux-x86_64.sh -b -p $HOME/anaconda
# Install Anaconda on OS X
bash Miniconda-latest-MacOSX-x86_64.sh -b -p $HOME/anaconda
# Then, to install all the packages you need for training a neural network on the CPU, type:
conda install -c alexbw lua-nn lua-trepl lua-optim # That's it!
I'm just at the beginning stages of this, so please let me know if there's hiccups, and I'll try to work through it.
You can look at all the build recipes here:
And if you want to submit a pull request for a new package (please do!), here's a walk-through explanation of how these recipes work: