How to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA) UPDATED!
Written on April 26, 2019 by Dr Donald KinghornThis post is the needed update to a post I wrote nearly a year ago (June 2018) with essentially the same title. This time I have presented more details in an effort to prevent many of the "gotchas" that some people had with the old guide. This is a detailed guide for getting the latest TensorFlow working with GPU acceleration without needing to do a CUDA install.
How To Install CUDA 10.1 on Ubuntu 19.04
Written on April 5, 2019 by Dr Donald KinghornUbuntu 19.04 will be released soon so I decided to see if CUDA 10.1 could be installed on it. Yes, it can and it seems to work fine. In this post I walk through the install and show that docker and nvidia-docker also work. I ran TensorFlow 2.0- alpha on Ubuntu 19.04 beta.
TensorFlow Performance with 1-4 GPUs -- RTX Titan, 2080Ti, 2080, 2070, GTX 1660Ti, 1070, 1080Ti, and Titan V
Written on March 14, 2019 by Dr Donald KinghornI have updated my TensorFlow performance testing. This post contains up-to-date versions of all of my testing software and includes results for 1 to 4 RTX and GTX GPU's. It gives a good comparative overview of most of the GPU's that are useful in a workstation intended for machine learning and AI development work.
Intel Xeon W-3175X and i9 9990XE Linpack and NAMD on Ubuntu 18.04
Written on February 28, 2019 by Dr Donald KinghornThere are 2 recent Intel processors that are really strange, the Xeon W-3175X 28-core, and the Core i9 9990XE overclocked 14-core. I was able to get a little time in on the these processors. I ran a couple of numerical compute performance tests with the Intel MKL Linpack benchmark and NAMD. I used the same system image that I had used recently to look at 3 Intel 8-core processors so I will include those results here as well. **There will be results for W-3175, 9990XE, 9800X, W-2145, and 9900K**.
RTX Titan TensorFlow performance with 1-2 GPUs (Comparison with GTX 1080Ti, RTX 2070, 2080, 2080Ti, and Titan V)
Written on January 30, 2019 by Dr Donald KinghornI've done some testing with 2 NVIDIA RTX Titan GPU's running machine learning jobs with TensorFlow. The RTX Titan is a great card but there is good news and bad news.
Numerical Computing Performance of 3 Intel 8-core CPUs - i9 9900K vs i7 9800X vs Xeon 2145W
Written on January 25, 2019 by Dr Donald KinghornIn this post I'll take a brief look at the numerical computing performance of three very capable 8-core processors -- i9 9900K, i9 9800X and Xeon 2145W All three are great CPU's but there are some significant differences that can cause confusion. I'll discuss these differences and see how the processors stack up when running Linpack and NAMD molecular dynamics simulations.
P2P peer-to-peer on NVIDIA RTX 2080Ti vs GTX 1080Ti GPUs
Written on January 11, 2019 by Dr Donald KinghornThere has been some concern about Peer-to-Peer (P2P) on the NVIDIA RTX Turing GPU's. P2P is not available over PCIe as it has been in past cards. It is available with very good performance when using NVLINK with 2 cards. I did some testing to see how the performance compared between the GTX 1080Ti and RTX 2080Ti. There were some interesting results!
AMD Threadripper and (1-4) NVIDIA 2080Ti and 2070 for NAMD Molecular Dynamics
Written on December 14, 2018 by Dr Donald KinghornIn my recent testing with the AMD Threadripper 2990WX is was impressed by the CPU based performance with the molecular dynamics program NAMD. NAMD makes a good benchmark for looking at CPU/GPU performance since it requires a balance and is usually limited by CPU. After some discussions I decided it would be good to look at multi-GPU performance with NAMD on Threadripper.
AMD Threadripper 2990WX 32-core vs Intel Xeon-W 2175 14-core - Linpack NAMD and Kernel Build Time
Written on December 6, 2018 by Dr Donald KinghornI recently wrote a post about building and running AMD Threadripper 2990WX with HPL Linpack - a "How-To". Most of the time I had with the processor went into getting that to work. However, I did run a few other test jobs that I thought the 2990WX would do well with. I compared that against my personal workstation with a Xeon-W 2175. In this post I share those test runs with you. It's not thorough testing by any means but it was interesting and I was surprised a couple of times with the results.
How to Run an Optimized HPL Linpack Benchmark on AMD Ryzen Threadripper -- 2990WX 32-core Performance
Written on November 30, 2018 by Dr Donald KinghornThe AMD Ryzen Threadripper 2990WX with 32 cores is an intriguing processor. I've been asked about performance for numerical computing and decided to find out how well it would do with my favorite benchmark the "High Performance Linpack" benchmark. This is used to rank Supercomputers on the Top500 list. It is not always simple to run this test since it can require building a few libraries from source. This includes the all important BLAS library which AMD has optimized in their BLIS package. I give you a complete How-To guide for getting this running to see what the 2990WX is capable of.
RTX 2080Ti with NVLINK - TensorFlow Performance (Includes Comparison with GTX 1080Ti, RTX 2070, 2080, 2080Ti and Titan V)
Written on October 26, 2018 by Dr Donald KinghornMore Machine Learning testing with TensorFlow on the NVIDIA RTX GPU's. This post adds dual RTX 2080 Ti with NVLINK and the RTX 2070 along with the other testing I've recently done. Performance in TensorFlow with 2 RTX 2080 Ti's is very good! Also, the NVLINK bridge with 2 RTX 2080 Ti's gives a bidirectional bandwidth of nearly 100 GB/sec!
NVLINK on RTX 2080 TensorFlow and Peer-to-Peer Performance with Linux
Written on October 16, 2018 by Dr Donald KinghornNVLINK is one of the more interesting features of NVIDIA's new RTX GPU's. In this post I'll take a look at the performance of NVLINK between 2 RTX 2080 GPU's along with a comparison against single GPU I've recently done. The testing will be a simple look at the raw peer-to-peer data transfer performance and a couple of TensorFlow job runs with and without NVLINK.
NVIDIA RTX 2080 Ti vs 2080 vs 1080 Ti vs Titan V, TensorFlow Performance with CUDA 10.0
Written on October 3, 2018 by Dr Donald KinghornAre the NVIDIA RTX 2080 and 2080Ti good for machine learning? Yes, they are great! The RTX 2080 Ti rivals the Titan V for performance with TensorFlow. The RTX 2080 seems to perform as well as the GTX 1080 Ti (although the RTX 2080 only has 8GB of memory). I've done some testing using **TensorFlow 1.10** built against **CUDA 10.0** running on **Ubuntu 18.04** with the **NVIDIA 410.48 driver**.
How To Install CUDA 10 (together with 9.2) on Ubuntu 18.04 with support for NVIDIA 20XX Turing GPUs
Written on September 27, 2018 by Dr Donald KinghornNVIDIA recently released version 10.0 of CUDA. This is an upgrade from the 9.x series and has support for the new Turing GPU architecture. This CUDA version has full support for Ubuntu 18.4 as well as 16.04 and 14.04. The CUDA 10.0 release is bundled with the new 410.x display driver for Linux which will be needed for the 20xx Turing GPU's. If you are doing development work with CUDA or running packages that require you to have the CUDA toolkit installed then you will probably want to upgrade to this. I'll go though how to do the install of CUDA 10.0 either by itself or along with an existing CUDA 9.2 install.
PyTorch for Scientific Computing - Quantum Mechanics Example Part 4) Full Code Optimizations -- 16000 times faster on a Titan V GPU
Written on September 14, 2018 by Dr Donald KinghornThis is the 16000 times speedup code optimizations for the scientific computing with PyTorch Quantum Mechanics example. The following quote says a lot, "The big magic is that on the Titan V GPU, with batched tensor algorithms, those million terms are all computed in the same time it would take to compute 1!!!"
PyTorch for Scientific Computing - Quantum Mechanics Example Part 3) Code Optimizations - Batched Matrix Operations, Cholesky Decomposition and Inverse
Written on August 31, 2018 by Dr Donald KinghornAn amazing result in this testing is that "batched" code ran in constant time on the GPU. That means that doing the Cholesky decomposition on 1 million matrices took the same amount of time as it did with 10 matrices! In this post we start looking at performance optimization for the Quantum Mechanics problem/code presented in the first 2 posts. This is the start of the promise to make the code over 15,000 times faster! I still find the speedup hard to believe but it turns out little things can make a big difference.
PyTorch for Scientific Computing - Quantum Mechanics Example Part 2) Program Before Code Optimizations
Written on August 16, 2018 by Dr Donald KinghornThis is the second post on using Pytorch for Scientific computing. I'm doing an example from Quantum Mechanics. In this post we go through the formulas that need to coded and write them up in PyTorch and give everything a test.
Doing Quantum Mechanics with a Machine Learning Framework: PyTorch and Correlated Gaussian Wavefunctions: Part 1) Introduction
Written on July 31, 2018 by Dr Donald KinghornA 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.
NAMD Custom Build for Better Performance on your Modern GPU Accelerated Workstation -- Ubuntu 16.04, 18.04, CentOS 7
Written on July 20, 2018 by Dr Donald KinghornIn this post I will be compiling NAMD from source for good performance on modern GPU accelerated Workstation hardware. Doing a custom NAMD build from source code gives a moderate but significant boost in performance. This can be important considering that large simulations over many time-steps can run for days or weeks. I wanted to do some custom NAMD builds to ensure that that modern Workstation hardware was being well utilized. I include some results for the STMV benchmark showing the custom build performance boost. I've included some results using NVIDIA 1080Ti and Titan V GPU's as well as an "experimental" build using an Ubuntu 18.04 base.
Why You Should Consider PyTorch (includes Install and a few examples)
Written on July 13, 2018 by Dr Donald KinghornPyTorch is a relatively new ML/AI framework. It combines some great features of other packages and has a very "Pythonic" feel. It has excellent and easy to use CUDA GPU acceleration. It is fun to use and easy to learn. read on for some reasons you might want to consider trying it. I've got some unique example code you might find interesting too.
Easy Image Bounding Box Annotation with a Simple Mod to VGG Image Annotator
Written on June 29, 2018 by Dr Donald KinghornIn 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.
The Best Way to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA)
Written on June 21, 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. YOU WILL NOT HAVE TO INSTALL CUDA! 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.
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.