If you see Intel, something went wrong during the installation.Update January 19, 2021: Apple has reverted this server-side change, and it is once again possible to side-load unsupported iPhone and iPad apps on an M1 Mac. If this is the case, under ‘Kind ’ on the Activity Monitor you should see the ‘Apple’ option. To test that everything works, run a simple program: # -*- coding: utf-8 -*- import torch import math dtype = torch.float device = vice("cpu") # Create random input and output data x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype) y = torch.sin(x) # Randomly initialize weights a = torch.randn((), device=device, dtype=dtype) b = torch.randn((), device=device, dtype=dtype) c = torch.randn((), device=device, dtype=dtype) d = torch.randn((), device=device, dtype=dtype) learning_rate = 1e-6 for t in range(2000): # Forward pass: compute predicted y y_pred = a + b * x + c * x ** 2 + d * x ** 3 # Compute and print loss loss = (y_pred - y).pow(2).sum().item() if t % 100 = 99: print(t, loss) # Backprop to compute gradients of a, b, c, d with respect to loss grad_y_pred = 2.0 * (y_pred - y) grad_a = grad_y_pred.sum() grad_b = (grad_y_pred * x).sum() grad_c = (grad_y_pred * x ** 2).sum() grad_d = (grad_y_pred * x ** 3).sum() # Update weights using gradient descent a -= learning_rate * grad_a b -= learning_rate * grad_b c -= learning_rate * grad_c d -= learning_rate * grad_d print(f'Result: y = x^3')Īpart from that, it is imperative to check whether or not the program runs natively on the Apple M1 processor. Optionally, install the Jupyter notebook or lab: $ conda install -c conda-forge jupyter jupyterlab Since we want a minimalistic Pytorch setup, just execute: $ conda install -c pytorch pytorch Check here to find which version is suitable. Also, don’t forget to activate it: $ conda create -name pytorch_m1 python=3.8 $ conda activate pytorch_m1 Let’s create a new conda environment in MiniForge and call it pytorch_m1. Check this article to learn how to manage effectively many conda distributions simultaneously! Step 3: Setup conda environment and install MiniForge For those familiar with the conda ecosystem, only one conda distro can be “functional” at a given time. Anaconda or MiniConda, there is no need to uninstall it in order to use MiniForge. If you already have a pre-existing conda distribution, e.g. To download it, go to this page, choose the installer for Apple Silicon and execute: $ bash Miniforge3-MacOSX-arm64.sh One of its greatest advantages is its compatibility with MacOS, including the M1 devices. Then, install the Xcode Command Line Tools using this command: $ xcode-select -install Step 2: Install MiniForgeĮssentially, MiniForge is a conda installer, comparable with MiniConda. If not, it can easily be downloaded from the App Store. Some of the M1 Macbooks have Xcode preinstalled. Note: If you have already installed Tensorflow, the first 2 steps can be skipped. Pytorch was somewhat left behind in terms of compatibility, however, you are now able to install Pytorch natively on M1 MacBooks. You can access all the articles in the “Setup Apple M1 for Deep Learning” series from here, including the guide on how to install Tensorflow on Mac M1. In addition, Apple has released the new Metal plugin, which enables Tensorflow to utilize the GPU via the TensorFlow-metal PluggableDevice. As far as Tensorflow is concerned, a lot of progress has been made, both by the community and Apple. Of course, such a move would make sense if at the very least 2 of the most popular data science frameworks, Tensorflow and Pytorch, would be compatible with the new processor. The first thing that all data scientists thought is whether or not there was any potential in moving their workspace to MacOS. When Apple announced the release of the new Apple Silicon M1 Macbook, it took the ML community by surprise.
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