View source for Using Caffe On The Lab Computers

[http://caffe.berkeleyvision.org/ Caffe], the deep learning framework, is supported on the CS department's [[:Category:Linux Clients|Linux clients]], but there are a few things to be aware of.

== Installation Location ==

Caffe is installed in <tt>/usr/local/caffe</tt> .  That installation only includes the binaries and Python modules.  Notably, it does not include the reference models or utility scripts.

If you want to use the Caffe-distributed models, you'll need to clone the Caffe repository yourself and use the contents of the <tt>models</tt> directory:

<pre>
git clone https://github.com/BVLC/caffe.git
</pre>

=== Python Module Location ===

The appropriate directory is already part of the system's Python path, so if you're using the Caffe Python module, you can just use <code>import caffe</code> without having to tell Python where to find that module.

== GPU Support ==

GPU acceleration (via CUDA) is supported on our lab machines (ugrad1 through ugrad24 on the ugraduate network; masters1 through masters16 on the grad/research network).

GPU acceleration is '''not''' supported on our compute systems (ugradx, gradx, and gradz); on those systems, you will only be able to use Caffe in CPU mode.

== Supported Python Versions ==

Caffe will only work with Python 2.7.  That's the default version on our systems, so if you're just running <code>python</code>, you should be fine.  If you run <code>python3</code>, the Caffe module will not be available.  (Caffe uses Google's protocol buffers to store its data and, as of May 2016, we unfortunately don't support the protobuf module for Python 3.)

[[Category:Software]]