How to use nonisolated

Generated on 9/8/2024

1 search

It seems like you're interested in the use of nonisolated in Swift. While the context provided doesn't directly address nonisolated, I can guide you on how it might relate to the content presented at WWDC, particularly in the context of Swift and concurrency.

In Swift, nonisolated is used to indicate that a method or property of an actor can be accessed without going through the actor's isolation. This is particularly useful when you want to expose certain functionality of an actor that doesn't require synchronization or when you want to interact with non-actor code.

If you're interested in how Swift is used in data visualization or machine learning contexts, you might find the session on Swift Charts: Vectorized and function plots useful. This session discusses how to use Swift for creating accessible data visualizations, which could be relevant if you're working with data in a concurrent environment.

For more specific details on Swift concurrency and nonisolated, you might want to look at sessions from WWDC that focus on Swift language updates or concurrency improvements. If you have any more specific questions or need further information, feel free to ask!

Deploy machine learning and AI models on-device with Core ML

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

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.

Explore machine learning on Apple platforms

Explore machine learning on Apple platforms

Get started with an overview of machine learning frameworks on Apple platforms. Whether you’re implementing your first ML model, or an ML expert, we’ll offer guidance to help you select the right framework for your app’s needs.

Bring your machine learning and AI models to Apple silicon

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.

Swift Charts: Vectorized and function plots

Swift Charts: Vectorized and function plots

The plot thickens! Learn how to render beautiful charts representing math functions and extensive datasets using function and vectorized plots in your app. Whether you’re looking to display functions common in aerodynamics, magnetism, and higher order field theory, or create large interactive heat maps, Swift Charts has you covered.

Accelerate machine learning with Metal

Accelerate machine learning with Metal

Learn how to accelerate your machine learning transformer models with new features in Metal Performance Shaders Graph. We’ll also cover how to improve your model’s compute bandwidth and quality, and visualize it in the all new MPSGraph viewer.