When I started using GPUs for deep learning my deep learning skills improved quickly. When you can run experiments of algorithms and algorithms with different parameters and gain rapid feedback you can just learn much more quickly. At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2nd place in the Crowdflower competition where the task was to predict weather labels from given tweets (sunny, raining etc.).
After this success I was tempted to use multiple GPUs in order to train deep learning algorithms even faster. I also took interest in learning very large models which do not fit into a single GPU. I thus wanted to build a little GPU cluster and explore the possibilities to speed up deep learning with multiple nodes with multiple GPUs. At the same time I was offered to do contract work as a data base developer through my old employer. This gave me opportunity to get the money to build the GPU cluster I thought of.
[Read more…] about How To Build and Use a Multi GPU System for Deep Learning