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But first of all, why do we need to use GPU to do Parallel computing? And since the 0.7.0 release on Jan 15 this year, PlaidML includes full Stripe backends for GPU & CPU for all major targets. Since the acquisition by Intel in 2018 and the later 0.3.3 version release, you can utilize your AMD and Intel GPUs to do Parallel Deep Learning jobs with Keras. However, it’s already 2020 now and things could be a little bit different today: PlaidML was initially released in 2017 by Vertex.AI designed to bring “deep learning for every platform”. For a long time, the majority of modern machine learning models can only utilize Nvidia GPUs through the general-purpose GPU library CUDA. Soon I found out that extra piece of GPU brought almost no help when it comes to Machine Learning & Deep Learning. I still remember when I was choosing between MacBook Pro 13’ and 15’, back when I was not familiar with Data Science at all, I chose the 15’ just because it has a discrete AMD GPU. Tf.test.As an owner of MacBook Pro, I am aware of the frustration of not being able to utilize its GPU to do deep learning, considering the incredible quality and texture, and of course, the price of it. Then you can just import tensorflow as tf You may need to uninstall existing tensorflow distributions first or work in a virtual environment. To install, run: pip3 install tensorflow-macos

If you are working with macOS 12.0 or later and would be willing to use TensorFlow instead, you can use the Mac optimized build of TensorFlow, which supports GPU training using Apple's own GPU acceleration library Metal.Ĭurrently, you need Python 3.8 (=3.9 don't work) to run it. AMDs equivalent library ROCm requires Linux. CUDA has not available on macOS for a while and it only runs on NVIDIA GPUs. Unfortunately, no GPU acceleration is available when using Pytorch on macOS. To get started, install the latest nightly build of PyTorch: PyTorch now supports training using Metal.