网络相关优化
Generated on 3/10/2025
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It seems you are interested in network-related optimizations. While the provided context does not directly address network optimizations, it does cover several sessions related to machine learning and privacy, which might involve network considerations indirectly.
For instance, the session titled "What’s new in privacy" discusses local network access control in macOS Sequoia, which could be relevant if you're interested in network privacy and security optimizations. You can find more about this in the chapter on macOS Extensions transparency and control.
If you are interested in machine learning optimizations, sessions like "Support real-time ML inference on the CPU" and "Accelerate machine learning with Metal" discuss various optimizations that could indirectly affect network performance by improving computational efficiency and reducing data transfer needs.
If you have a more specific question or need further details, feel free to ask!
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.
Support real-time ML inference on the CPU
Discover how you can use BNNSGraph to accelerate the execution of your machine learning model on the CPU. We will show you how to use BNNSGraph to compile and execute a machine learning model on the CPU and share how it provides real-time guarantees such as no runtime memory allocation and single-threaded running for audio or signal processing models.
Explore Swift performance
Discover how Swift balances abstraction and performance. Learn what elements of performance to consider and how the Swift optimizer affects them. Explore the different features of Swift and how they’re implemented to further understand the tradeoffs available that can impact performance.
Meet AdAttributionKit
Introducing AdAttributionKit, a new iOS framework for privacy-preserving ad attribution. Learn how AdAttributionKit supports re-engagement, click-through attribution (including support for custom creative), JWS formatted impressions and postbacks, and more. We’ll provide insight into testing your AdAttributionKit implementation and deep-dive into code snippets and best practices for adopting AdAttributionKit.
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.
What’s new in privacy
At Apple, we believe privacy is a fundamental human right. Learn about new and improved permission flows and other features that manage data in a privacy-preserving way, so that you can focus on creating great app experiences.
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.