The GPU included on the system is a K520 with 4GB of memory and 1,536 cores. This instance is named the g2.2xlarge instance and costs approximately $0.65 per hour. How to install CUDA Toolkit and cuDNN for deep learningĪs I mentioned in an earlier blog post, Amazon offers an EC2 instance that provides access to the GPU for computation purposes. Feel free to spin up an instance of your own and follow along.īy the time you’re finished this tutorial, you’ll have a brand new system ready for deep learning. Specifically, I’ll be using an Amazon EC2 g2.2xlarge machine running Ubuntu 14.04.
In the remainder of this blog post, I’ll demonstrate how to install both the NVIDIA CUDA Toolkit and the cuDNN library for deep learning. Using the cuDNN package, you can increase training speeds by upwards of 44%, with over 6x speedups in Torch and Caffe. The cuDNN library: A GPU-accelerated library of primitives for deep neural networks.This toolkit includes a compiler specifically designed for NVIDIA GPUs and associated math libraries + optimization routines. The NVIDIA CUDA Toolkit: A development environment for building GPU-accelerated applications.If you already have an NVIDIA supported GPU, then the next logical step is to install two important libraries: And the more GPUs you have, the better off you are. If you’re serious about doing any type of deep learning, you should be utilizing your GPU rather than your CPU. Click here to download the source code to this post