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.
Does PCIe X16 give better performance than X8 for training models with Caffe when using cuDNN? Yes, but not by much!
OK, Intel Core-i7 7th gen Kabylake is out. Of course the first thing I want to do is drop a Core i7 7700K in a new Z270 based motherboard, install Linux and run a Linpack benchmark. You know, GFLOP/s and all that. We installed Ubuntu 1610 with a recent release of Intel MKL and fired up a few Linpack job runs. Read on for the not-so dramatic results.
NVIDIA’s Pascal GPU’s have twice the computational performance of the last generation. A great use for this compute capability is for training deep neural networks. We have tested NVIDIA DIGITS 4 with Caffe on 1 to 4 Titan X and GTX 1070 cards. Training was for classification of a million image data set from ImageNet. Read on to see how it went.
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