In May 2018, it even added support for Metal. PlaidML supports Nvidia, AMD, and Intel GPUs. To put this to work, I relied on Intel’s PlaidML. I have been amazed at macOS’s ability to get the most out of this card. I have been doing neural network training on my 2017 MacBook Pro using an external AMD Vega Frontier Edition graphics card. This makes on-device machine learning usable where it wouldn’t have been before. The Neural Engine runs Metal and Core ML code faster than ever, so on-device predictions and computer vision work better than ever. Apple has opened the Neural Engine to third-party developers. Nvidia have stepped into the gap to try to provide eGPU macOS drivers, but they are slow to release updates for new versions of macOS, and those drivers lack Apple’s support.) Neural EngineĢ018’s iPhones and new iPad Pro run on the A12 and A12X Bionic chips, which include an 8-core Neural Engine. This won’t let you use Nvidia’s parallel computing platform CUDA. If you are in the domain of neural networks or other tools that would benefit from GPU, macOS Mojave brought good news: It added support for external graphics cards (eGPUs). Scikit-learn and some others only support the CPU, with no plans to add GPU support. The new MacBook Pro’s 6 cores and 32 GB of memory make on-device machine learning faster than ever.ĭepending on the problem you are trying to solve, you might not be using the GPU at all. 2019 Started Strong More Cores, More Memory Let’s take a look at where machine learning is on macOS now and what we can expect soon. While that might have been true a few years ago, Apple has been stepping up its machine learning game quite a bit. You usually hear that serious machine learning needs a beefy computer and a high-end Nvidia graphics card. Every company is sucking up data scientists and machine learning engineers.
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