This string of commands will download the script to your desktop, modify its permissions to allow it to run, then run it. Once in recovery mode, open the Terminal from the Utilities menu, then type:Ĭd ~/Desktop & curl -o automate-eGPU.sh & chmod +x automate-eGPU.sh & sudo. To do, we have to boot into recovery mode by holding ⌘-R (Command-R) during startup. It also forces us to enable Thunderbolt 3 support in macOS by disabling System Integrity Protection. This necessitates the use of the adapter listed above. My MacBook Pro is only a Thunderbolt 1 capable device. This site maintains a good summary of current graphics card offerings for machine learning, and, as long as I've followed it, stayed up-to-date.Īpple Thunderbolt 3 to Thunderbolt 2 Adapter - This adapter seems to be intended to be used on the 'computer' side of the connection, but I'm using it on the 'device' side without any problems. 1 Many recommend it as a good value to performance tradeoff. GEFORCE® GTX 1080 Ti Graphics Card - This GPU is just short of the top-of-the-line Titan series from NVIDIA. It is somewhat larger than I expected it to be based on the pictures on the web. In this post, I'll document how I set up an external GPU with my mid-2012 15-inch Retina MacBook Pro (MacbookPro10,1).Īkitio Node eGPU box - This box has been shipping for only a short time now, but received great review and is extremely reasonably priced.

Fortunately, the advancements in high-speed external buses have made an external GPU, or eGPU, an option. This makes is difficult to do this work on any modern Mac, as they all utilize AMD graphics cards. Much of this machine learning work is well suited for processing on the GPU and most libraries that facilitate that processing rely on NVIDIA's CUDA platform, necessitating the use of an NVIDIA graphics card. The work started with our entry into the 2016 Maritime RobotX Challenge and leverages the great body of work being done by autonomous car researchers. Recently, the C.R.A.W.LAB has been doing more machine learning research, driven by our Autonomous Surface Vehicle (ASV) research.

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