In this post I’ll go through setting up Docker to use User-Namespaces. This is a very important step to achieving a comfortable docker work-flow on a personal Workstation. I will show you how to configure Docker so that instead of files and processes being owned by root they will be owned by your personal user account. This will make using Docker containers on your system safer and feel much the same as a “normally” installed application.
This post will build on top of the base systems setup described in Part1. We will go through installing,configuring and testing Docker and NVIDIA-Docker version 2.
One of my New Years resolutions was to adopt a Docker based workflow. I had also promised in my recent post on testing the Titan V that I would do a series of How-To’s on setting up docker and ultimately configuring and using the excellent NVIDIA NGC docker registry. This is the fist post of that series and covers the base system setup, motivation and references.
In this post I’ll be going over details of Installing Ubuntu 16.04 including the NVIDIA display driver and, optionally, NVIDIA CUDA. I have found the method presented here to be the most likely to succeed no matter what hardware configuration you are installing onto.
The Intel CPU flaw and the Meltdown and Spectre security exploits are causing a lot of concern. There is a possibility of application slowdown from the kernel patches to mitigate the exploits. This slowdown concern is a concern for GPU accelerated application because of the systems calls they require for moving data between CPU and GPU memory space. I did some testing on a couple of large Tensorflow and Caffe machine learning jobs along with the creation of a LMDA database from 1.3 million images.
NIVIDA announced availability of the the Titan V card Friday December 8th. We had a couple in hand for testing on Monday December 11th, nice! I ran through many of the machine learning and simulation testing problems that I have done on Titan cards in the past. Results are not the near doubling in performance of past generations… but read on.
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.
You got your new wonderful NVIDIA Pascal GPU … maybe a GTX 1080, 1070, or Titan X(P) … And, you want to setup a CUDA environment for some dev work or maybe try some “machine learning” code with your new card. What are you going to do? At the time of this writing CUDA 8 is still in RC and the deb and rpm packages have drivers that don’t work with Pascal. I’ll walk through the tricks you need to do a manual setup of CUDA 7.5 and 8.0 on top of Ubuntu 16.04 or 14.04 that will work with the new Pascal based GPU’s
I have a Titan Black, Titan X (Maxwell) and a new Titan X (Pascal) in a system for a quick CUDA performance test. Install is on Ubuntu 16.04 with CUDA 8.0rc. We’ll look at nbody from the CUDA samples code and NAMD Molecular Dynamics. It is stunning to see how much the CUDA performance has increased on these wonderful GPU’s in just 3 years.
Just got a NVIDIA GTX 1080 for testing. I hacked up an install with Ubuntu 16.04 and CUDA 7.5 along with a beta display driver that works! First run after compiling the cuda samples nbody gave 5816 GFLOP/s! A GTX 980 on the same system does 2572 GFLOP/s. However, it’s not all good news …
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