Caffe

Introduction

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.

Installed version

The currently installed version is 1.0. The full module name is Caffe/1.0-intel-2016b-CUDA-8.0.61-Python-2.7.12.

Using Caffe with the HPC cluster

If you want to use Caffe, you will have to load its corresponding module first. You can do that with the following command:

module load Caffe

This takes a few seconds because Caffe has quite a few dependencies. In total, 72 modules will be loaded including Caffe itself. Python 2.7.12 will be loaded, its not necessary to load it separately. Its currently unknown if other, newer versions of Python would work too.

Important Note: If you want to work with Caffe you have to request a GPU in your jobscript. Otherwise you will end up with an error like this:

(38 vs 0) No CUDA capable device found

Requesting a GPU is easy and can be done by adding the following line to your jobscript:

#SBATCH --gres=gpu:1

This will request on GPU for your job. Since we have nodes with multiple GPUs, you can request up to 2 GPUs for your job. This request is per node, so its not possible to request serveral nodes with different amounts of GPUs.

Documentation

Serveral different documentations can be found on the official homepage of Caffe.