At the moment, you cannot use GPU acceleration with PyTorch with AMD GPU, i.e. Also, the same goes for the CuDNN framework. You may need to uninstall existing tensorflow distributions first or work in a virtual environment. CUDA is a framework for GPU computing, that is developed by nVidia, for the nVidia GPUs. 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. Learn how to update the software on your Mac. Your Mac must also have macOS High Sierra 10.13.4 installed. Similarly, the M1 Ultra had a score 63 of the RX 6800XT but is now at 78. eGPUs are supported on MacBook Pro notebooks released in 2016 and later 1, iMac computers introduced in 2017 and later, and iMac Pro. For example, the M1 Max previously had a score 86 of the Radeon Pro 5700XT but is now 115. Some significant upward changes for Apple Silicon. Unfortunately, no GPU acceleration is available when using Pytorch on macOS. Metal scores based on new Geekbench 6 favor Apple Silicon in relative GPU rankings. To get started, install the latest nightly build of PyTorch: PyTorch now supports training using Metal.
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