Let’s begin! Step #1: Install and configure Xcodeįor starters, you’ll need to get Xcode from the Apple App Store and install it. If you’ve run into this conundrum, then my install guide is for you. The TensorFlow team is definitely working on Python 3.7 support - but if you’re running macOS Mojave you probably don’t want to twiddle your thumbs and wait until Python 3.7 support is officially released. This presents a challenge to us in the deep learning community because Tensorflow does not yet officially support Python 3.7. You see, Homebrew now by default installs Python 3.7. Inside of Step #3, we’ll do some Homebrew formulae kung fu to get Python 3.6 installed. Inside of this tutorial, we’ll review the seven steps to configuring Mojave for deep learning. macOS Mojave: Install TensorFlow and Keras for Deep Learning To learn how to configure macOS for deep learning and computer vision with Python, just keep reading. If you’re ready with a fresh install of macOS Mojave and are up for today’s challenge, let’s get started configuring your system for deep learning.Īlso released today is my Ubuntu 18.04 deep learning configuration guide with optional GPU support. I’m still actually running High Sierra on my machines, but after putting this guide together I feel confident in recommending Mojave to PyImageSearch readers.Īpple has fixed most of the bugs, but as you’ll see in this guide, Homebrew (an unofficial package manager for macOS) doesn’t make everything especially easy. The OS was plagued with problems from the get-go, and I decided to hold off. Inside this tutorial, you will learn how to configure macOS Mojave for deep learning.Īfter you’ve gone through this tutorial, your macOS Mojave system will be ready for (1) deep learning with Keras and TensorFlow, and (2) ready for Deep Learning for Computer Vision with Python.Ī tutorial on configuring Mojave has been a long time coming on my blog since the Mojave OS was officially released in September 2018. Click here to download the source code to this post
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