Recently we have noticed an issue when transferring data between mechanical drives and the Glyph Studio Raid external device. Specifically the issue pertains to large folders with numerous subfolders. In our initial testing it would appear that the data size is irrelevant beyond a few GB and mostly revolves around the number of subfolders.
If you accidentally set up a Microsoft account instead of a local user and would like to reverse this, this article is perfect.
Now that OctaneRender has been updated to support the Volta GPU architecture, how well does its performance scale when using multiple Titan Vs? And how does that compare to other popular rendering cards like the GeForce GTX 1080 Ti?
As of version 3.08, the Volta GPU architecture is now supported in OctaneRender. How does it stack up compared to other Titan and GeForce series graphics cards – in terms of both performance and value?
In this post I’ll be going over details of Installing Ubuntu 18.04 including the NVIDIA display driver and, any one of the available desktop environments. I’ll do this starting from a base server install. I’ll go over a few possible pitfalls and end with a short discussion on the new netplan configuration tool for Ubuntu networking.
I recently finished reading the book, The Nordstrom Way to Customer Experience Excellence: Creating a Values-Driven Service Culture. The book is a lot more compelling than that ridiculously long title. It includes a lot of inspirational stories, but I wanted to share one story with you along with the one rule found in the employee handbook.
OctaneRender is a GPU-based rendering engine, so the bulk of the processing it does is carried out on the video cards in a system. Different processors and motherboards can impact the number of cards that can fit in a single system, but do they matter beyond that? Does the CPU itself have any impact on rendering speed/performance?
Just like the original Ryzen CPUs, the 2nd Gen Ryzen processors from AMD support a range of different RAM speeds depending on a number of factors. This information is not easily accessible to the public, however, so we decided to put together a quick post with the information we received from our contacts at AMD.
The Core i7-8086K is an 8th-generation microprocessor with six cores, 12 threads and a maximum clock speed of 5GHz. Experience Intels fastest processor ever by purchasing one of a limited supply of Puget Systems workstations powered by the special edition Intel Core i7-8086K processor.
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.
Puget Systems builds custom workstations, servers and storage solutions tailored for your work.
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