Apple Metal Supported GPU: A Step-by-Step Guide to PyTorch Installation on Mac M1

PyTorch is one of the most popular deep learning frameworks, and with the recent release of Apple's M1-based MacBooks, it's now possible to run PyTorch on these devices using the Metal Performance Shaders (MPS) plugin. In this article, we'll take you through a step-by-step guide on how to install PyTorch on your Mac M1 device and utilize its GPU acceleration capabilities.

Step 1: Install Xcode

Before installing PyTorch, you need to ensure that you have the latest version of Xcode installed on your Mac. If you don't have it already, download and install Xcode from the App Store. You can also use the `xcode-select -install` command in your terminal.

Step 2: Create a New Conda Environment

Next, we'll create a new conda environment specifically for PyTorch. Run the following commands:

```

conda create -n torch-gpu python=3.8

conda activate torch-gpu

```

This will create a new environment called `torch-gpu` with Python 3.8 as its base.

Step 3: Install PyTorch

Now, we'll install PyTorch using the conda package manager. Run the following command:

```

conda install pytorch torchvision torchaudio -c pytorch-nightly

```

This will install the latest version of PyTorch from the PyTorch Nightly channel.

Step 4: Verify Installation

To verify that PyTorch is installed correctly, open a new terminal window and run the following command:

```

python -c 'import torch; print(torch.cuda.is_available())'

```

This should output `True` if PyTorch is installed correctly and can utilize your GPU for acceleration.

In conclusion, installing PyTorch on a Mac M1 device is relatively straightforward, thanks to the Metal Performance Shaders plugin. With this guide, you should now be able to install PyTorch and utilize its GPU acceleration capabilities for your deep learning tasks.

'apple metal supported gpu'

Apple metal supported gpu