This 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
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.
NAMD Custom Build for Better Performance on your Modern GPU Accelerated Workstation — Ubuntu 16.04, 18.04, CentOS 7
In 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)
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.
The Best Way to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA)
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. 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
If you are wanting to use Ubuntu 18.04 and also want a CUDA install this post should help you get that working.
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.
Install TensorFlow with GPU Support the Easy Way on Ubuntu 18.04 (without installing CUDA)
TensorFlow 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!
Build TensorFlow-GPU with CUDA 9.1 MKL and Anaconda Python 3.6 using a Docker Container
Building 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.
The Best Way To Install Ubuntu 16.04 with NVIDIA Drivers and CUDA
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.