- #Ubuntu 18.04 cuda how to#
- #Ubuntu 18.04 cuda software#
- #Ubuntu 18.04 cuda code#
- #Ubuntu 18.04 cuda series#
#Ubuntu 18.04 cuda how to#
I detailed how to get it loaded into your AWS account and how to boot it up in this previous post. That’s why I have built a custom Amazon Machine Instance (AMI) pre-configured and pre-installed for the community to accompany my book. In fact, there are quite a few steps and potential for things to go sour. You’ll soon find out below that configuring a GPU machine isn’t a cakewalk. How hard is it to configure Ubuntu with GPU support for deep learning? As I say, “fundamentals before funds” - meaning, get acclimated with modern deep learning fundamentals and concepts before you bite off more than you can chew with expensive hardware and cloud bills. Most modern laptop CPUs will do just fine with the small experiments presented in the early chapters in my book.
It is important to point out that you don’t need access to an expensive GPU machine to get started with Deep Learning. 16GB didn’t cut it for me on my EC2 instance. I also recommend that you have about 32GB of space on your OS drive/partition. The older instances, g2.2xlarge and g2.8xlarge are not compatible with the version of CUDA and cuDNN in this tutorial. Note: For those utilizing AWS’s EC2, I recommend you select the p2.xlarge, p2.8xlarge, or p2.16xlarge machines for compatibility with these instructions (depending on your use case scenario and budget).
#Ubuntu 18.04 cuda software#
Longer term if you are working on deep learning experiments daily, then it would be wise to have one on hand for cost savings purposes (assuming you’re willing to keep the hardware and software updated regularly). If you are just doing a couple of experiments then using a cloud service provider such as Amazon, Google, or FloydHub for a time-based usage charge is the way to go. Alternatively, you may build one, buy one ( as I did), or rent one in the cloud (as I still do today).
It is common to share high-end GPU machines at universities and companies. Be sure to check out this developer page. It is worth getting familiar with their lineup of products (hardware and software) so you know what you’re paying for if you’re using an instance in the cloud or building a machine yourself. 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.
#Ubuntu 18.04 cuda series#
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.
#Ubuntu 18.04 cuda code#
Click here to download the source code to this post
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