In this note I'll go through creating self-signed SSL certificates and adding them to a JupyterHub configuration running on a LAN or VPN. This will allow encrypted access to the server using https in a browser.
This is a quick note about setting up a JupyterHub server and JupyterLab using conda with Anaconda Python.
On March 19, 2020 I did a webinar titled, "AMD Threadripper 3rd Gen HPC Parallel Performance and Scaling ++(Xeon 3265W and EPYC 7742)" The "++(Xeon 3265W and EPYC 7742)" part of that title was added after we had scheduled the webinar. It made the presentation a lot more interesting than the original Threadripper only title! This is a follow up post with the charts and plots of testing results presented in that webinar.
Is 32-cores enough? I had some testing time again on an AMD Threadripper 32-core 3970x and thought it would be interesting to compare that to the 64-core 3990x. In this post I take a comparative look at parallel performance and scaling for HPL Linpack, Python numpy and the NAMD molecular dynamics program.
64 cores is a lot of cores! How well will parallel applications scale on that many cores? The answer, of course, is, it depends on the application. In this post I look at Amdhal's Law parallel scaling for HPL Linpack, Python numpy and the NAMD molecular dynamics program.
64 cores! The latest AMD Threadripper is out, the 3990x 64-core. I've spent the last couple of days running benchmarks and have some results showing raw numerical compute performance using my standard CPU testing applications HPL Linpack and the molecular dynamics program NAMD. The 3990x is a great processor with exceptional performance. Especially for NAMD! (There were some difficulties and disappointments during the testing and I report those here too.)
It's the end of the 2010's and start of 2020's. Time to reflect ...
The Super Computing conference annual US counterpart is always a great meeting. It's a chance to see the trend and get sentiment for the highest performance end of computing. I have written up a few observations and provided a few interesting links for SC19.
How To Use MKL with AMD Ryzen and Threadripper CPU's (Effectively) for Python Numpy (And Other Applications)Written on November 27, 2019 by Dr Donald Kinghorn
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 Threadripper 3970x 32-core! ...The, third new AMD processor I've had the pleasure of trying recently. I'm running it through the same double precision floating point performance tests as the recently tested Ryzen processors, Linpack and NAMD.
The, much anticipated, AMD Ryzen 3950x 16-core processor is out! As always the first thing I wanted know was the double precision floating point performance. My two favorite applications for a "first look" at a new CPU are Linpack and NAMD.
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.
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.
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.
I was able to spend a little time with an AMD Ryzen 3900X. Of course the first thing I wanted know was the double precision floating point performance. My two favorite applications for a "first look" at a new processor are Linpack and NAMD. The Ryzen 3900X is a pretty impressive processor!
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!
Install TensorFlow 2 beta1 (GPU) on Windows 10 and Linux with Anaconda Python (no CUDA install needed)Written on June 26, 2019 by Dr Donald Kinghorn
TensorFlow 2.0.0-beta1 is available now and ready for testing. What if you want to try it but don't want to mess with doing an NVIDIA CUDA install on your system. The official TensorFlow install documentations has you do that, but it's really not necessary.
Being able to run Jupyter Notebooks on remote systems adds tremendously to the versatility of your workflow. In this post I will show a simple way to do this by taking advantage of some nifty features of secure shell (ssh). What I'll do is mostly OS independent but I am putting an emphasis on Windows 10 since many people are not familiar with tools like ssh on that OS.
This post is a setup guide and introduction to ssh client and server on Windows 10. Microsoft has a native OpenSSH client AND server on Windows. They are standard (and in stable versions) on Windows 10 since the 1809 "October Update". This guide should helpful to both Windows and Linux users who want better interoperability.
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.
This post is the needed update to a post I wrote nearly a year ago (June 2018) with essentially the same title. This time I have presented more details in an effort to prevent many of the "gotchas" that some people had with the old guide. This is a detailed guide for getting the latest TensorFlow working with GPU acceleration without needing to do a CUDA install.