In this post I’ll show you how to setup isolated conda envs for Python without having a base conda install! I’ll cover Linux and Windows including an example to get you started. Read on to learn about the wonderful micromamba project.
How To Use MKL with AMD Ryzen and Threadripper CPU’s (Effectively) for Python Numpy (And Other Applications)
In this post I’m going to show you a simple way to significantly speedup Python numpy compute performance on AMD CPU’s when using Anaconda Python.
AMD Ryzen 3900X vs Intel Xeon 2175W Python numpy – MKL vs OpenBLAS
In this post I’ve done more testing with Ryzen 3900X looking at the effect of BLAS libraries on a simple but computationally demanding problem with Python numpy. The results may surprise you! I start with a little bit of history of Intel vs AMD performance to give you what may be a new perspective on the issue.
PyTorch for Scientific Computing – Quantum Mechanics Example Part 4) Full Code Optimizations — 16000 times faster on a Titan V GPU
This 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
An 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
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.
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.
My 2018 Sys Admin and Dev Resolutions
New 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.
Beginning with Machine Learning and AI
I can’t think of of trending field of scientific research that has ever been better suited for “beginners” than Machine Learning and AI. Even though the field has been around for decades it feels like day one. There is now a perfect convergence of resources to facilitate the learning and doing of Machine Learning.
