Hardware Recommendations
Machine Learning & AI System Requirements There are many types of Machine Learning and Artificial Intelligence applications – from traditional regression models, non-neural network classifiers, and statistical models that are represented by capabilities in Python SciKitLearn and the R language, up to Deep Learning models using frameworks like PyTorch and TensorFlow. Within these different types
Machine Learning / AI
Our Machine Learning / AI workstation configurations are single CPU, multi-GPU, and optimized for model training with NVIDIA GPU acceleration. These are great platforms for working with frameworks like TensorFlow, PyTorch, MXNet, etc.
UPDATE v0.2 NVIDIA GPU Powerlimit Setup
This is just a short post to announce a more usable version of the NVIDIA GPU powerlimit setup script that I released a few months ago. This update to version 0.2 uses an interactive mode to set GPU powerlimits and optionally setup a systemd unit file to set these limits on subsequent reboots.
NVIDIA GPU Powerlimit Systemd Setup Script
In this post I am referencing a Bash shell script I recently put together for setting up automatic NVIDIA GPU power-limit lowering at system boot. This allows a reliable way to configure and maintain multi-GPU systems for stable operation under heavy load.
Self Contained Executable Containers Using Enroot Bundles
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”.
Run “Docker” Containers with NVIDIA Enroot
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.
Quad RTX3090 GPU Power Limiting with Systemd and Nvidia-smi
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.
Workstation Setup for Docker with the New NVIDIA Container Toolkit (nvidia-docker2 is deprecated)
It’s time for a “Docker with NVIDIA GPU support” update. This post will guide you through a useful Workstation setup (including User-name-spaces and performance tuning) with the new versions of Docker and the NVIDIA GPU container toolkit.
NVIDIA Docker2 with OpenGL and X Display Output
Docker is a great Workstation tool. It is mostly used for command-line application or servers but, … What if you want to run an application in a container, AND, use an X Window GUI with it? What if you are doing development work with CUDA and are including OpenGL graphic visualization along with it? You CAN do that!
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!