NVIDIA has released the Titan Xp which is an update to the Titan X Pascal (they both use the Pascal GPU core). They also recently released the GTX1080Ti which proved to be every bit as good at the Titan X Pascal but at a much lower price. The new Titan Xp does offer better performance and is currently their fastest GeForce card. How much faster? I decided to find out by running a large Deep Learning image classification job to see how it performs for GPU accelerated Machine Learning.
Docker can be complex but for use on single-user-workstation you can get a lot done with just a few commands. This post will go through some commands to manage your images and containers. We will also go through the process of building a docker image for CUDA development that includes OpenGl support.
A few weeks ago I wrote a blog post titled Should You Learn to Program with Python. If you read that and decided the answer is yes then this post is for you.
You can use graphical application with Docker and NVIDIA-Docker by attaching your X-Window server socket to a container. And, it can be done in a relatively safe and secure way. I will take advantage of the Docker security and usability enhancements from the configuration with User-Namespaces that we setup in the previous post and show you how to run a CUDA application with OpenGL output support.
How good is the NVIDIA GTX 1080Ti for CUDA accelerated Machine Learning workloads? About the same as the TitanX! I ran a Deep Neural Network training calculation on a million image dataset using both the new GTX 1080Ti and a Titan X Pascal GPU and got very similar runtimes.
In this third post on Docker and Nvidia-Docker we will configure “kernel user namespaces” for use with Docker. This will increase the security and usability of Docker on your desktop. This is a relatively new feature in Docker and is a key component for the viability of “Docker on your workstation”.
The short answer to that question is, yes. If you want to know why you would want to do that then read on.
In this second post on Docker and Nvidia-Docker we will do an install and setup on an Ubuntu 16.04 workstation.
Docker containers together with the NVIDIA-docker can provide relief from dependency and configuration difficulties when setting up GPU accelerated machine learning environments on a workstation. In this post I will discuss motivation for considering this.
NVIDIA has released the Quadro GP100 bringing Tesla P100 Pascal performance to your desktop. This new card gives you the compute performance of the NVIDIA Tesla P100 together with Quadro display capability. That means full double precision floating point capability of the P100 and NVLINK for multiple cards.
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