Steps to install jupyter hub on Ubuntu 22.04
Generated on 8/8/2024
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It looks like your question is off-topic for the content presented at WWDC. However, I can provide information related to machine learning and AI models on Apple devices, which was a significant focus at WWDC.
For example, if you're interested in deploying machine learning models on Apple devices, you might want to check out the session Deploy machine learning and AI models on-device with Core ML. This session covers new features in Core ML to help you efficiently deploy and run your machine learning and AI models on-device.
If you have any specific questions about the content presented at WWDC, feel free to ask!
Deploy machine learning and AI models on-device with Core ML
Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.
Train your machine learning and AI models on Apple GPUs
Learn how to train your models on Apple Silicon with Metal for PyTorch, JAX and TensorFlow. Take advantage of new attention operations and quantization support for improved transformer model performance on your devices.
Platforms State of the Union
Discover the newest advancements on Apple platforms.
Bring your machine learning and AI models to Apple silicon
Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.