![]() ![]() Open the Activity Monitor and you can see that Python is using GPU resources. evaluate ( test_images, test_labels ) test_acc fit ( train_images, train_labels, epochs = 5, batch_size = 64 ) test_loss, test_acc = model. compile ( optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ) model. astype ( 'float32' ) / 255 train_labels = to_categorical ( train_labels ) test_labels = to_categorical ( test_labels ) model. astype ( 'float32' ) / 255 test_images = test_images. load_data () train_images = train_images. Install Xcode Command Line Tools by downloading it from Apple Developer or by typing:įrom import mnist from import to_categorical ( train_images, train_labels ), ( test_images, test_labels ) = mnist. This article serves as an update of the Apple Silicon Mac M1/M2 Machine Learning Environment (TensorFlow, JupyterLab, VSCode), and will give you a detailed introduction to how to install the latest supported GPU Accelerated TensorFlow. ![]() Exactly what we need Until you see that the interface to work with Metal is available in Swift and Objective-C. Our first stop on the world wide web is Apple’s own calculations on GPU, titled Performing Calculations on a GPU using Metal. You can now leverage Apple’s tensorflow-metal PluggableDevice in TensorFlow v2.5 for accelerated training on Mac GPUs directly with Metal. Performing Calculations on a GPU using Metal. A few days ago, I saw that has been archived, and the README stated that TensorFlow v2.5 natively supports M1. ![]()
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