Puget Systems HPC Blog
How to install CUDA 9.2 on Ubuntu 18.04
Written on June 15, 2018 by Dr Donald KinghornIf you are wanting to use Ubuntu 18.04 and also want a CUDA install this post should help you get that working.
The Best Way To Install Ubuntu 18.04 with NVIDIA Drivers and any Desktop Flavor
Written on June 8, 2018 by Dr Donald KinghornIn this post I'll be going over details of Installing Ubuntu 18.04 including the NVIDIA display driver and, any one of the available desktop environments. I'll do this starting from a base server install. I'll go over a few possible pitfalls and end with a short discussion on the new netplan configuration tool for Ubuntu networking.
Install TensorFlow with GPU Support on Windows 10 (without a full CUDA install)
Written on June 4, 2018 by Dr Donald KinghornIn this post I'll walk you through the best way I have found so far to get a good TensorFlow work environment on Windows 10 including GPU acceleration. I'll go through how to install just the needed libraries (DLL's) from CUDA 9.0 and cuDNN 7.0 to support TensorFlow 1.8. I'll also go through setting up Anaconda Python and create an environment for TensorFlow and how to make that available for use with Jupyter notebook. As a "non-trivial" example of using this setup we'll go through training LeNet-5 with Keras using TensorFlow with GPU acceleration. We'll get a setup that is 18 times faster than using the CPU alone.
Install TensorFlow with GPU Support the Easy Way on Ubuntu 18.04 (without installing CUDA)
Written on May 25, 2018 by Dr Donald KinghornTensorFlow is a very important Machine/Deep Learning framework and Ubuntu Linux is a great workstation platform for this type of work. If you are wanting to setup a workstation using Ubuntu 18.04 with CUDA GPU acceleration support for TensorFlow then this guide will hopefully help you get your machine learning environment up and running without a lot of trouble. And, you don't have to do a CUDA install!
PCIe X16 vs X8 with 4 x Titan V GPUs for Machine Learning
Written on May 21, 2018 by Dr Donald KinghornOne 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.
Microsoft Build 2018 -- impressions
Written on May 11, 2018 by Dr Donald KinghornI attended the Microsoft Build 2018 developers conference this week and really enjoyed it. I wanted to share my "big picture" feelings about it and some of the things that stood out to me. I'm not going to give you a "reporters" view or repeat press-release items. This is just my personal impression of the conference.
Multi-GPU scaling with Titan V and TensorFlow on a 4 GPU Workstation
Written on May 4, 2018 by Dr Donald KinghornI have been qualifying a 4 GPU workstation for Machine Learning and HPC use. The last confirmation testing I wanted to do was running it with TensorFlow benchmarks on 4 NVIDIA Titan V GPU's. I have that systems up and running and the multi-GPU scaling looks very good.
GPU Memory Size and Deep Learning Performance (batch size) 12GB vs 32GB -- 1080Ti vs Titan V vs GV100
Written on April 27, 2018 by Dr Donald KinghornBatch size is an important hyper-parameter for Deep Learning model training. When using GPU accelerated frameworks for your models the amount of memory available on the GPU is a limiting factor. In this post I look at the effect of setting the batch size for a few CNN's running with TensorFlow on 1080Ti and Titan V with 12GB memory, and GV100 with 32GB memory.
NVIDIA Titan V plus Tensor-cores Considerations and Testing of FP16 for Deep Learning
Written on April 20, 2018 by Dr Donald KinghornTensor-cores are one of the compelling new features of the NVIDIA Volta architecture. In this post I discuss the some thought on mixed precision and FP16 related to Tensor-cores. I have some performance results for large convolution neural network training that makes a good argument for trying to use them. Performance looks very good.
Build TensorFlow-GPU with CUDA 9.1 MKL and Anaconda Python 3.6 using a Docker Container
Written on April 12, 2018 by Dr Donald KinghornBuilding TensorFlow from source is challenging but the end result can be a version tailored to your needs. This post will provide step-by-step instructions for building TensorFlow 1.7 linked with Anaconda3 Python, CUDA 9.1, cuDNN7.1, and Intel MKL-ML. I do the build in a docker container and show how the container is generated from a Dockerfile.
Build TensorFlow-CPU with MKL and Anaconda Python 3.6 using a Docker Container
Written on April 6, 2018 by Dr Donald KinghornIn this post I go through how to use Docker to create a container with all of the libraries and tools needed to compile TensorFlow 1.7. The build will include links to Intel MKL-ML (Intel's math kernel library plus extensions for Machine Learning) and optimizations for AVX512.
GTC 2018 Impressions
Written on April 2, 2018 by Dr Donald KinghornNVIDIA's Graphics Technology Conference (GTC) is probably my all-time favorite conference. It's an interesting blend of "Scientific Research meeting" and Trade-Show. It's put on by a hardware vendor but still feels like a scientific meeting. It's not just a "Kool-Aid" fest! In this post I go present some of my thoughts about this years conference.
TensorFlow Installation CPU version
Written on March 23, 2018 by Dr Donald KinghornTensorFlow is a very powerful numerical computing framework. However, like any large research level program it can be challenging to install and configure. In this post I'll try to give some guidance on relatively easy ways to get started with TensorFlow. I'll only look at relatively simple "CPU only" Installs with "standard" Python and Anaconda Python in this post. (I also have a quick test with Intel Python.)
TensorFlow Introduction What is TensorFlow
Written on March 16, 2018 by Dr Donald KinghornTensorFlow is on it's way to becoming the "standard" framework for machine learning. There are many reasons for that, and, it is not just for machine learning! In this post I'll give a descriptive introduction to TensorFlow. This is the first post in a series on how to work with TensorFlow. Hopefully after reading thsi you will have a better understanding of the What? and Why? of TensorFlow.
NAMD Performance on Xeon-Scalable 8180 and 8 GTX 1080Ti GPUs
Written on March 9, 2018 by Dr Donald KinghornThis post will look at the molecular dynamics program, NAMD. NAMD has good GPU acceleration but is heavily dependent on CPU performance as well. It achieves best performance when there is a proper balance between CPU and GPU. The system under test has 2 Xeon 8180 28-core CPU's. That's the current top of the line Intel processor. We'll see how many GPU's we can add to those Xeon 8180 CPU's to get optimal CPU/GPU compute balance with NAMD.
TensorFlow Scaling on 8 1080Ti GPUs - Billion Words Benchmark with LSTM on a Docker Workstation Configuration
Written on March 2, 2018 by Dr Donald KinghornIn this post I present some Multi-GPU scaling tests running TensorFlow on a very nice system with 8 1080Ti GPU's. I use the Docker Workstation setup that I have recently written about. The job I ran for this testing was the "Billion Words Benchmark" using an LSTM model. Results were very good and better than expected.
How-To Setup NVIDIA Docker and NGC Registry on your Workstation - Part 5 Docker Performance and Resource Tuning
Written on February 23, 2018 by Dr Donald KinghornThis should be the last post in this series dealing with the Docker setup for accessing the NVIDIA NCG Docker registry on your workstation. There are a couple of configuration tuning changes that you may want to make. These will improve performance and ensure that you have proper system "user limit" resources to handle large application and job runs with docker.
How-To Setup NVIDIA Docker and NGC Registry on your Workstation - Part 4 Accessing the NGC Registry
Written on February 16, 2018 by Dr Donald KinghornThis 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 3 Setup User-Namespaces
Written on February 9, 2018 by Dr Donald KinghornIn 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.
How-To Setup NVIDIA Docker and NGC Registry on your Workstation - Part 2 Docker and NVIDIA-Docker-v2
Written on February 2, 2018 by Dr Donald KinghornThis 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.
How-To Setup NVIDIA Docker and NGC Registry on your Workstation - Part 1 Introduction and Base System Setup
Written on January 26, 2018 by Dr Donald KinghornOne 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.
The Best Way To Install Ubuntu 16.04 with NVIDIA Drivers and CUDA
Written on January 19, 2018 by Dr Donald KinghornIn 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.
Intel CPU flaw kernel patch effects - GPU compute Tensorflow Caffe and LMDB database creation
Written on January 10, 2018 by Dr Donald KinghornThe 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.
My 2018 Sys Admin and Dev Resolutions
Written on January 4, 2018 by Dr Donald KinghornNew Years resolutions are notorious for being overly ambitious, vague, and quickly forgotten.But, I'm not going to let that stop me from making some! In order to keep myself from forgetting what I resolve to do I'm going to write them down in public! These are my resolutions for when I'm wearing my System Administrator and Developer hats.
Don's Computing Technology Predictions for 2018
Written on December 28, 2017 by Dr Donald KinghornI've been exposed to enough computing "teasers" in 2017 that I feel I can stick my neck out a little and make some predictions for 2018. Some of these are pretty wild i.e. unlikely but I want to put them out there anyway.