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Read this article at https://www.pugetsystems.com/guides/2181
Dr Donald Kinghorn (Scientific Computing Advisor)

Self Contained Executable Containers Using Enroot Bundles

Written on July 14, 2021 by Dr Donald Kinghorn

NVIDIA Enroot has a unique feature that will let you easily create an executable, self-contained, single-file package with a container image AND the runtime to start it up! This allows creation of a container package that will run itself on a system with or without Enroot installed on it! "Enroot Bundles".


Read this article at https://www.pugetsystems.com/guides/2170
Dr Donald Kinghorn (Scientific Computing Advisor)

NVIDIA 3080Ti Compute Performance ML/AI HPC

Written on June 18, 2021 by Dr Donald Kinghorn

For computing tasks like Machine Learning and some Scientific computing the RTX3080TI is an alternative to the RTX3090 when the 12GB of GDDR6X is sufficient. (Compared to the 24GB available of the RTX3090). 12GB is in line with former NVIDIA GPUs that were "work horses" for ML/AI like the wonderful 2080Ti.


Read this article at https://www.pugetsystems.com/guides/2142
Dr Donald Kinghorn (Scientific Computing Advisor)

Run "Docker" Containers with NVIDIA Enroot

Written on May 11, 2021 by Dr Donald Kinghorn

Enroot is a simple and modern way to run "docker" or OCI containers. It provides an unprivileged user "sandbox" that integrates easily with a "normal" end user workflow. I like it for running development environments and especially for running NVIDIA NGC containers. In this post I'll go through steps for installing enroot and some simple usage examples including running NVIDIA NGC containers.


Read this article at https://www.pugetsystems.com/guides/1983
Dr Donald Kinghorn (Scientific Computing Advisor)

Quad RTX3090 GPU Power Limiting with Systemd and Nvidia-smi

Written on November 24, 2020 by Dr Donald Kinghorn

This is a follow up post to "Quad RTX3090 GPU Wattage Limited "MaxQ" TensorFlow Performance". This post will show you a way to have GPU power limits set automatically at boot by using a simple script and a systemd service Unit file.


Read this article at https://www.pugetsystems.com/guides/1974
Dr Donald Kinghorn (Scientific Computing Advisor)

Quad RTX3090 GPU Wattage Limited "MaxQ" TensorFlow Performance

Written on November 13, 2020 by Dr Donald Kinghorn

Can you run 4 RTX3090's in a system under heavy compute load? Yes, by using nvidia-smi I was able to reduce the power limit on 4 GPUs from 350W to 280W and achieve over 95% of maximum performance. The total power load "at the wall" was reasonable for a single power supply and a modest US residential 110V, 15A power line.


Read this article at https://www.pugetsystems.com/guides/1958
Dr Donald Kinghorn (Scientific Computing Advisor)

RTX3070 (and RTX3090 refresh) TensorFlow and NAMD Performance on Linux (Preliminary)

Written on October 29, 2020 by Dr Donald Kinghorn

The GeForce RTX3070 has been released. The RTX3070 is loaded with 8GB of memory making it less suited for compute task than the 3080 and 3090 GPUs. we have some preliminary results for TensorFlow, NAMD and HPCG.


Read this article at https://www.pugetsystems.com/guides/1902
Dr Donald Kinghorn (Scientific Computing Advisor)

RTX3090 TensorFlow, NAMD and HPCG Performance on Linux (Preliminary)

Written on September 24, 2020 by Dr Donald Kinghorn

The second new NVIDIA RTX30 series card, the GeForce RTX3090 has been released. The RTX3090 is loaded with 24GB of memory making it a good replacement for the RTX Titan... at significantly less cost! The performance for Machine Learning and Molecular Dynamics on the RTX3090 is quite good, as expected.


Read this article at https://www.pugetsystems.com/guides/1885
Dr Donald Kinghorn (Scientific Computing Advisor)

RTX3080 TensorFlow and NAMD Performance on Linux (Preliminary)

Written on September 17, 2020 by Dr Donald Kinghorn

The much anticipated NVIDIA GeForce RTX3080 has been released. How good is it with TensorFlow for machine learning? How about molecular dynamics with NAMD? I've got some preliminary numbers for you!


Read this article at https://www.pugetsystems.com/guides/1828
Dr Donald Kinghorn (Scientific Computing Advisor)

Note: How To Install JupyterLab Extensions (Globally for a JupyterHub Server)

Written on July 15, 2020 by Dr Donald Kinghorn

The current JupyterHub version 2.5.1 does not allow user installed extension for JupyterLab when it is being served from JupyterHub. This should be remedied in version 3. However, even when this is "fixed" it is still useful to be able to install extensions globally for all users on a multi-user system. This note will show you how.


Read this article at https://www.pugetsystems.com/guides/1551
Dr Donald Kinghorn (Scientific Computing Advisor)

2 x RTX2070 Super with NVLINK TensorFlow Performance Comparison

Written on August 14, 2019 by Dr Donald Kinghorn

This is a short post showing a performance comparison with the RTX2070 Super and several GPU configurations from recent testing. The comparison is with TensorFlow running a ResNet-50 and Big-LSTM benchmark.


Read this article at https://www.pugetsystems.com/guides/1207
Dr Donald Kinghorn (Scientific Computing Advisor)

Doing Quantum Mechanics with a Machine Learning Framework: PyTorch and Correlated Gaussian Wavefunctions: Part 1) Introduction

Written on July 31, 2018 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1193
Dr Donald Kinghorn (Scientific Computing Advisor)

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

Written on July 13, 2018 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1191
Dr Donald Kinghorn (Scientific Computing Advisor)

Easy Image Bounding Box Annotation with a Simple Mod to VGG Image Annotator

Written on June 29, 2018 by Dr Donald Kinghorn

In 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.


Read this article at https://www.pugetsystems.com/guides/1187
Dr Donald Kinghorn (Scientific Computing Advisor)

The Best Way to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA)

Written on June 21, 2018 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1172
Dr Donald Kinghorn (Scientific Computing Advisor)

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

Written on June 4, 2018 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1170
Dr Donald Kinghorn (Scientific Computing Advisor)

Install TensorFlow with GPU Support the Easy Way on Ubuntu 18.04 (without installing CUDA)

Written on May 25, 2018 by Dr Donald Kinghorn

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!


Read this article at https://www.pugetsystems.com/guides/1032
Dr Donald Kinghorn (Scientific Computing Advisor)

Beginning with Machine Learning and AI

Written on September 14, 2017 by Dr Donald Kinghorn

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.


Read this article at https://www.pugetsystems.com/guides/1007
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Multinomial (Multiclass) Logistic Regression

Written on August 18, 2017 by Dr Donald Kinghorn

The post will implement Multinomial Logistic Regression. The multiclass approach used will be one-vs-rest. The Jupyter notebook contains a full collection of Python functions for the implementation. An example problem done showing image classification using the MNIST digits dataset.


Read this article at https://www.pugetsystems.com/guides/1003
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Logistic Regression Examples-1

Written on August 14, 2017 by Dr Donald Kinghorn

This post will be mostly Python code with implementation and examples of the Logistic Regression theory we have been discussing in the last few posts. Examples include fitting to 2 feature data using an arbitrary order multinomial model and a simple 2 class image classification problem using the MNIST digits data.


Read this article at https://www.pugetsystems.com/guides/996
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Logistic Regression Implementation

Written on August 5, 2017 by Dr Donald Kinghorn

In this post I'll discuss evaluating the "goodness of fit" for a Logistic Regression model and do an implementation of the formulas in Python using numpy. We'll look at an example to check the validity of the code.


Read this article at https://www.pugetsystems.com/guides/994
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Logistic and Linear Regression Regularization

Written on July 31, 2017 by Dr Donald Kinghorn

In this post I will look at "Regularization" in order to address an important problem that is common with implementations, namely over-fitting. We'll go through for logistic regression and linear regression. After getting the equations for regularization worked out we'll look at an example in Python showing how this can be used for a badly over-fit linear regression model.


Read this article at https://www.pugetsystems.com/guides/988
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Logistic Regression Theory

Written on July 21, 2017 by Dr Donald Kinghorn

Logistic regression is a widely used Machine Learning method for binary classification. It is also a good stepping stone for understanding Neural Networks. In this post I will present the theory behind it including a derivation of the Logistic Regression Cost Function gradient.


Read this article at https://www.pugetsystems.com/guides/978
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Linear Regression Part 6

Written on July 15, 2017 by Dr Donald Kinghorn

This will be the last post in the Linear Regression series. We will look at the problems of over or under fitting data along with non-linear feature variables.


Read this article at https://www.pugetsystems.com/guides/974
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Linear Regression Part 5

Written on July 4, 2017 by Dr Donald Kinghorn

In this post I will present the matrix/vector form of the Linear Regression problem and derive the "exact" solution for the parameters.


Read this article at https://www.pugetsystems.com/guides/968
Dr Donald Kinghorn (Scientific Computing Advisor)

Machine Learning and Data Science: Linear Regression Part 4

Written on June 15, 2017 by Dr Donald Kinghorn

In this post I'll be working up, analyzing, visualizing, and doing Gradient Descent for Linear Regression. It's a Jupyter notebook with all the code for plots and functions in Python available on my github account.