![]() NVIDIA is the market leader in deep learning hardware, and quite frankly the primary option I recommend if you are getting in this space. Graphics Processing Units are great at deep learning for their parallel processing architecture - in fact, these days there are many GPUs built specifically for deep learning - they are put to use outside the domain of computer gaming. If you’ve reached this point, you are likely serious about deep learning and want to train your neural networks with a GPU. Let’s go ahead and get started! Setting up Ubuntu 16.04 + CUDA + GPU for deep learning with Python If you have an NVIDIA CUDA compatible GPU, you can use this tutorial to configure your deep learning development to train and execute neural networks on your optimized GPU hardware. Configuring macOS for deep learning with Python (releasing on Friday).Setting up Ubuntu 16.04 + CUDA + GPU for deep learning with Python (this post).Configuring Ubuntu for deep learning with Python (for a CPU only environment).Pre-configured Amazon AWS deep learning AMI with Python.Your deep learning + Python Ubuntu virtual machine.Links to related tutorials can be found here: Today, we will configure Ubuntu + NVIDIA GPU + CUDA with everything you need to be successful when training your own deep learning networks on your GPU. Welcome back! This is the fourth post in the deep learning development environment configuration series which accompany my new book, Deep Learning for Computer Vision with Python. Click here to download the source code to this post
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