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Building a deep learning workstation (Practical AI #112)

Podcast |: Changelog Master Feed
Publisher |: Changelog Media
Media Type |: audio
Podknife tags |: Business,; Entrepreneurship,; Interview,; Master Feeds,; Software How-To,; Tech News,; Technology,; Web Development
Categories Via RSS |: Technology
Publication Date |: Nov 17, 2020
Episode Duration |: 00:49:27

Description
Content Encoded

What's it like to try and build your own deep learning workstation? Is it worth it in terms of money, effort, and maintenance? Then once built, what's the best way to utilize it? Chris and Daniel dig into questions today as they talk about Daniel's recent workstation build. He built a workstation for his NLP and Speech work with two GPUs, and it has been serving him well (minus a few things he would change if he did it again).

What’s it like to try and build your own deep learning workstation? Is it worth it in terms of money, effort, and maintenance? Then once built, what’s the best way to utilize it? Chris and Daniel dig into questions today as they talk about Daniel’s recent workstation build. He built a workstation for his NLP and Speech work with two GPUs, and it has been serving him well (minus a few things he would change if he did it again).

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Featuring:

Chris Benson – Twitter, GitHub, LinkedIn, Website
Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

Daniel’s workstation components:

CPU - AMD YD292XA8AFWOF Ryzen Threadripper 2920X
CPU cooler - Noctua NH-U12S TR4-SP3, Premium-Grade CPU Cooler for AMD sTRX4/TR4/SP3
Motherboard - GIGABYTE X399 AORUS PRO
Memory - Corsair Vengeance LPX 16GB (2x 2 packs), total 64GB
Storage 1 - Samsung (MZ-V7S1T0B/AM) 970 EVO Plus SSD 1TB
GPU 1 - RTX 2080 Ti
GPU 2 - Titan RTX
Case - Lian Li PC-O11AIR
Power Supply - Rosewill Hercules
Case fan(s) - Coolmaster 8mm

Daniel’s NUC 9 Extreme machine

References:

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