If you're using an M1 variant Mac, it's " Miniforge3-MacOSX-arm64" <- click for direct download.Ĭlicking the link above will download a shell file called Miniforge3-MacOSX-arm64.sh to your Downloads folder (unless otherwise specified).Ĥ. Download the most compatible version of Miniforge (minimal installer for Conda specific to conda-forge, Conda is another package manager and conda-forge is a Conda channel) from GitHub. It will explain what it's doing and what you need to do as you go.Ģ. The command to install Homebrew will look something like: Homebrew is a package manager that sets up a lot of useful things on your machine, including Command Line Tools for Xcode, you'll need this to run things like git. Installing package managers (Homebrew and Miniforge) Think of it like this: a package manager is a piece of software that helps you install other pieces (packages) of software. Note: You're going to see the term "package manager" a lot below. If you're new to creating environments, using an Apple Silicon Mac (M1, M1 Pro, M1 Max, M1 Ultra, M2) and would like to get started running PyTorch and other data science libraries, follow the below steps. How to set up a PyTorch environment on Apple Silicon using Miniforge (longer version) However, since accelerated PyTorch for Mac is still in beta, I'm sure there's room for improvement. Looks like the "mps" device shines when the GPU is getting utilized more. Running TinyVGG on CIFAR10 dataset with batch size 32 and image size 224*224: Running TinyVGG on CIFAR10 dataset with batch size 32 and image size 32*32: Resultsīenchmark results were gathered with the notebook 01_cifar10_tinyvgg.ipynb. ], device='mps:0')Ĭongratulations! Your Apple Silicon device is now running PyTorch and a handful of other helpful data science and machine learning libraries. import torchįinally, you should get something like this: tensor(, MPS stands for Metal Performance Shaders, Metal is Apple's GPU framework. To run data/models on an Apple Silicon GPU, use the PyTorch device name "mps" with. Note: See more on running MPS as a backend in the PyTorch documentation.ġ1. Is MPS (Metal Performance Shader) built? True If it all worked, you should see something like: PyTorch version: 1.12.0 Print(f"PyTorch version: ")ĭevice = "mps" if _available() else "cpu" Create a new notebook by "New" -> "Notebook: Python 3 (ipykernel)" and run the following code to verfiy all the dependencies are available and check PyTorch version/GPU access. conda install jupyter pandas numpy matplotlib scikit-learn tqdmġ0. This will install the following: Installing collected packages: urllib3, typing-extensions, pillow, numpy, idna, charset-normalizer, certifi, torch, requests, torchvision, torchaudioĨ. pip3 install torch torchvision torchaudio Install PyTorch 1.12.0+ default version for Mac with pip from the PyTorch getting started page. Note: Python 3.8 is the most stable for using the following setup. Create a directory to setup a PyTorch environment. Sh ~/Downloads/Miniforge3-MacOSX-arm64.shĥ.
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