RTX3090 TensorFlow, NAMD and HPCG Performance on Linux (Preliminary)
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.
RTX3080 TensorFlow and NAMD Performance on Linux (Preliminary)
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!
Note: JupyterHub with JupyterLab Install using Conda
This is a quick note about setting up a JupyterHub server and JupyterLab using conda with Anaconda Python.
Note: How To Install JupyterHub on a Local Server
This note describes installing and configuring JupyterHub and JupyterLab on a “bare-metal” server.
How To Install Docker and NVIDIA-Docker on Ubuntu 19.04
Being able to get Docker and the NVIDIA-Docker runtime working on Ubuntu 19.04 makes this new and (currently) mostly unsupported Linux distribution a lot more useful. In this post I’ll go through the steps that I used to get everything working nicely.
How To Install CUDA 10.1 on Ubuntu 19.04
Ubuntu 19.04 will be released soon so I decided to see if CUDA 10.1 could be installed on it. Yes, it can and it seems to work fine. In this post I walk through the install and show that docker and nvidia-docker also work. I ran TensorFlow 2.0- alpha on Ubuntu 19.04 beta.
RTX Titan TensorFlow performance with 1-2 GPUs (Comparison with GTX 1080Ti, RTX 2070, 2080, 2080Ti, and Titan V)
I’ve done some testing with 2 NVIDIA RTX Titan GPU’s running machine learning jobs with TensorFlow. The RTX Titan is a great card but there is good news and bad news.
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**.