Doing Quantum Mechanics with a Machine Learning Framework: PyTorch and Correlated Gaussian Wavefunctions: Part 1) Introduction
A Quantum Mechanics problem coded up in PyTorch?! Sure! Why not? I’ll explain just enough of the Quantum Mechanics and Mathematics to make the problem and solution (kind of) understandable. The focus is on how easy it is to implement in PyTorch. This first post will give some explanation of the problem and do some testing of a couple of the formulas that will need to be coded up.
Easy Image Bounding Box Annotation with a Simple Mod to VGG Image Annotator
In this post I go through a simple modification to the VGG Image Annotator that adds easy to use buttons for adding labels to image object bounding-boxes. It is very fast way to do what could be a tedious machine learning data preparation task.
PCIe X16 vs X8 with 4 x Titan V GPUs for Machine Learning
One of the questions I get asked frequently is “how much difference does PCIe X16 vs PCIe X8 really make?” Well, I got some testing done using 4 Titan V GPU’s in a machine that will do 4 X16 cards. I ran several jobs with TensorFlow with the GPU’s at both X16 and X8. Read on to see how it went.
Case Study with RentPath
David McFarland, Head of Data Science at RentPath uses Puget Systems DIGITS box for Deep Learning and GPU-accelerated computation
How-To Setup NVIDIA Docker and NGC Registry on your Workstation – Part 4 Accessing the NGC Registry
This post will go through how to get access to the NVIDIA NGC container registry on your workstation. The first 3 posts in this series gave instructions on how to install and configure a base Ubuntu 16.04 workstation system with Docker and NVIDIA-Docker for a usable work-flow. With that taken care of we can get setup to use the many useful docker images in the NGC container registry for your local system.
How-To Setup NVIDIA Docker and NGC Registry on your Workstation – Part 1 Introduction and Base System Setup
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.
NVIDIA Titan V vs Titan Xp Preliminary Machine Learning and Simulation Tests
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.
Docker and NVIDIA-docker on your workstation: Integration with your Desktop
I’ve been doing this series of posts about setting up Docker for your desktop system, so why not literally add containers to your desktop! The way we have Docker configured, containers are the same as other applications you run. In this post I’ll show you how to add icons and menu items to launch containers.
Docker and Nvidia-Docker on your workstation: Common Docker Commands Tutorial
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.
NVIDIA GTX 1080Ti Performance for Machine Learning — as Good as TitanX?
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.