P2P peer-to-peer on NVIDIA RTX 2080Ti vs GTX 1080Ti GPUs

There 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!

RTX 2080Ti with NVLINK – TensorFlow Performance (Includes Comparison with GTX 1080Ti, RTX 2070, 2080, 2080Ti and Titan V)

More 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

NVLINK 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

Are 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**.

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.

Why You Should Consider PyTorch (includes Install and a few examples)

PyTorch 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.

Install TensorFlow with GPU Support on Windows 10 (without a full CUDA install)

In 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.

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.

Microsoft Build 2018 — impressions

I 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.