![]() ![]() We’ve scoured the app store and tested various Apple Watch recovery apps, and we've put together our top recommendations for the best Apple Watch apps that can help you optimize your health and recovery. We’re now seeing multiple third-party Apple Watch apps that can turn the swathe of health data collected into actionable metrics on our sleep, activity, and recovery. It's not different when it comes to wearable tech, either, with huge amounts fo health data being accessible on our wrists to help us optimize our health. The Complete Data Science and Machine Learning Bootcamp on Udemy is a great next step if you want to keep exploring the data science and machine learning field.įollow us on LinkedIn, Twitter, GitHub, and subscribe to our blog, so you don't miss a new issue.Wearable tech has become a staple in many of our lives, and that's no different for fitness enthusiasts.Īs we strive to improve our health and challenge our mind and body – it's become the norm to monitor our health and activity on a daily basis through the use of wearable technology.Ī focus on recovery has become popular as everyday athletes look to better themselves, from steady recovery runs to cold plunges in ice baths. How to build artificial neural networks with Keras and TensorFlow How to build CNN in TensorFlow(examples, code, and notebooks) How to create custom training loops in Keras Object detection with TensorFlow 2 Object detection API Leave a comment below if you have any challenges setting up PyTorch and TensorFlow on Apple Silicon. The repo below has example TensorFlow and PyTorch notebooks showing how to train deep learning models in Apple Silicon. To train PyTorch models on GPUs on Apple Silicon, set Metal Performance Shaders (MPS) as the backend. conda install pytorch torchvision -c pytorch-nightly This will be stable in the PyTorch v1.12 release. PyTorchĪs of this writing, you must install the Preview (Nightly) build to train the PyTorch model on Apple Silicon GPUs. ![]() You can check the activity monitor to confirm that Python is using the GPU. Train TensorFlow model on Apple Silicon GPUĪfter performing the above instructions, deep learning models will be trained by default on the GPU. You are now ready to train deep learning models on Apple Silicon with TensorFlow. Install TensorFlow: python -m pip install tensorflow-macosįinally, install the tensorflow-metal plugin. Next, install the TensorFlow dependencies in this environment: conda install -c apple tensorflow-deps Sh ~/Downloads/Miniforge3-MacOSX-arm64.sh chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh For this to work, ensure that you are using Python 3.8 or 3.9. Start by installing Conda and creating a virtual environment where TensorFlow will be installed. Let's walk through the installation instructions. We, therefore, install the compatible TensorFlow version and the Tensorflow-metal PluggableDevice. Training TensorFlow deep learning networks on Apple Silicon is done through the PluggableDevice architecture. The PluggableDevice architecture allows integration with new devices without changing TensorFlow core code. TensorFlow introduced the PluggableDevice architecture to enable the running of TensorFlow on new devices without changing TensorFlow code. These data-parallel primitives are specially tuned to take advantage of the unique hardware characteristics of each GPU family to ensure optimal performance. The Metal Performance Shaders framework contains a collection of highly optimized compute and graphics shaders that are designed to integrate easily and efficiently into your Metal app. The Metal Performance Shaders framework provides a large library of optimized compute and rendering shaders that take advantage of each GPU’s unique hardware. ![]()
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