![]() The batch size was 32 for all runs except the RTX 3060, which only had 6 GB VRAM and could only handly a batch size of 8. And I also added in the RTX 3060 and RTX 3080 results that were kindly provided here and here.) (The M1 Max and M1 Ultra results were kindly provided by readers. Here are the results for a VGG16 with CIFAR-10 images rescaled to 224x224 pixels (typical ImageNet sizes for VGG16): Just out of curiosity, I wanted to try this myself and trained deep neural networks for one epoch on various hardware, including the 12-core Intel server-grade CPU of a beefy deep learning workstation and a MacBook Pro with an M1 Pro chip. The PyTorch installer version with CUDA 10.2 support has a file size of approximately 750 Mb.) ![]() (An interesting tidbit: The file size of the PyTorch installer supporting the M1 GPU is approximately 45 Mb large. Then, if you want to run PyTorch code on the GPU, use vice("mps") analogous to vice("cuda") on an Nvidia GPU. ![]() $ pip install -pre torch torchvision torchaudio -extra-index-url $ conda create -n torch-nightly python =3.8 Personally, I recommend installing it as follows from the terminal: But for now, we can install it from the latest nightly release: How do we install the PyTorch version with M1 GPU support? I expect the M1-GPU support to be included in the 1.12 release and recommend watching the release list for updates. I am assuming CPU here refers to the M1 Ultra CPU. According to the fine print, they tested this on a Mac Studio with an M1 Ultra. And it was about 21x faster for inference (evaluation). Along with the announcement, their benchmark showed that the M1 GPU was about 8x faster than a CPU for training a VGG16. Today, the PyTorch Team has finally announced M1 GPU support, and I was excited to try it. However, I should note that I compiled PyTorch myself back then, as an early adopter, and I could only utilize the M1 CPU in PyTorch. It really can’t handle anything beyond LeNets. I recall making a LeNet-5 runtime comparison between the M1 and a GeForce 1080Ti and finding similar speeds.Įven though the M1 MacBook is an amazing machine, it is really not feasible to train modern deep neural networks on it. Similarly, all scikit-learn-related workflows were much faster on the M1 MacBook Air! I could even run small neural networks in PyTorch in a reasonable time (various multilayer perceptrons and convolutional neural networks for teaching purposes). For example, preprocessing the IMDb movie dataset took only 21 seconds instead of 1 minute and 51 seconds on my 2019 Intel MacBook Pro. When I was writing my new book, I noticed that it didn’t only feel fast in everyday use, but it also sped up several computations. It’s been a fantastic machine so far: it is silent, lightweight, super-fast, and has terrific battery life. In this short blog post, I will summarize my experience and thoughts with the M1 chip for deep learning tasks.īack at the beginning of 2021, I happily sold my loud and chunky 15-inch Intel MacBook Pro to buy a much cheaper M1 MacBook Air. This is an exciting day for Mac users out there, so I spent a few minutes tonight trying it out in practice. Today, PyTorch officially introduced GPU support for Apple’s ARM M1 chips.
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