Wednesday, July 5, 2017

Getting Torch running on Fedora 25

In this blog post we will setup the Torch AI framework so that it can be used on Fedora. This builds on the previous blog post which shows you how to setup a CUDA development environment for Fedora.

Torch is a Deep Learning AI framework that is written in LUA. This makes it very fast because there is little between the script and the pure C code that is performing the work. Both Facebook and Twitter are major contributors to this and have probably derived their in-house version from the open source version.

The first thing I would do is setup an account just for AI. The reason I suggest this is because we are going to be installing a bunch of software without rpm. All of this will be going into the home directory. So, if one day you want to delete it all, its as simple as deleting the account and home directory. Assuming you made the account and logged into it...

$ git clone ~/torch --recursive
$ cd torch/
$ export CMAKE_CXX_FLAGS="-std=c++03"
$ ./

The Torch community say that they only support Torch built this way. I have tried to package Torch in rpm and it simply does not work. I get some strange errors related to math. There are probably compile options that fix this but I'm done with hunting this down. It's easier to use their method from an account just for this. But I digress...

After about 25 minutes, the build asks "Do you want to automatically prepend the Torch install location to PATH and LD_LIBRARY_PATH in your /home/ai/.bashrc? (yes/no)"

I typed "yes" to have it update ~/.bashrc. I logged out and back in. Test to see if the GPU based Torch is working:

luajit -lcutorch
luajit -lcunn

This should produce errors if its not working. To exit the shell, type:


At this point only one last thing is needed. We may want to play with machine vision at some point so get the camera module. And a lot of models seem to be trained using the Caffe Deep Learning framework. This means we need load it from that format so let's grab the loadcaffe module.

During the build of Torch, you got a copy of luarocks which is a package manager for LUA modules. We can use this to pull in the modules so that Torch can use them.

$ luarocks install camera
$ luarocks install loadcaffe

If you run the webcam from another account that is not your login account, then you need to go into /etc/group and find the video group and add the ai account as a supplemental group.

Quick Art Test
OK. Now lets see if Torch is working right. There is a famous project that can take a picture and transfer the artistic style of a work of art onto your picture. Its really quite astonishing to see. Let's use that as our test for Torch.

The project page is here:

To download it:

$ git clone

Now download the caffe models:

$ cd neural-style/models
$ sh ./
$ cd ..

We need a picture and a work of art. I have a picture of a circuit board:

Let's see if we can make art from it. The boxiness of the circuit kind of suggests cubism to me. There is a web site called wikiart that curates a collection of art by style and genre. Let's grab a cubist style painting and see how well it works.

$ wget
$ mv portrait-de-jacques-nayral-1911.jpg cubist.jpg

To render the art:

$ th neural_style.lua -backend cudnn -style_image cubist.jpg -content_image circuit.jpg -output_image art.jpg

Using a 1050Ti GPU, it takes about 4 minutes and this is the results:

One thing you have to pay attention to is that if the picture is too big, you will run out of GPU memory. The video card only has so much working memory. You can use any image editing tool to re-scale the picture. The number of pixels is what matters rather than the size of the file. Something in the 512 - 1080 pixel range usually fits in a 4Gb video card.

At some point we may come back to Torch to do some experimenting on security data. But I find it to be fun to play around with the art programs written for it. If you like this, look around. There are a number of apps written for Torch. The main point, though, is to show how to leverage the CUDA development environment we previously setup to get one of the main Deep Learning frameworks installed and running on a modern Fedora system.

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