data caching
Generated on 4/29/2025
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Data caching is discussed in several sessions at WWDC 2024, particularly in the context of SwiftData and machine learning models.
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SwiftData: In the session Create a custom data store with SwiftData, the Datastore protocol is highlighted as it defines the functionality needed for a store to be usable by the model context, including caching. This allows for efficient data retrieval and persistence, supporting features like migration, history tracking, and CloudKit sync.
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Machine Learning with Core ML: The session Deploy machine learning and AI models on-device with Core ML discusses the use of a key-value (KV) cache to store key and value vectors for language models. This caching mechanism helps reduce overhead and improve inference efficiency by avoiding the recomputation of vectors, thus speeding up prediction times.
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Machine Learning with Metal: In the session Accelerate machine learning with Metal, the use of a KV cache is also explored. The session explains how to update the cache in place using operations like
sliceupdate, which optimizes memory usage and improves performance.
These sessions provide insights into how caching is implemented and optimized in different contexts, from data persistence in SwiftData to performance improvements in machine learning models.
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Focused on feature discovery, the TipKit framework makes it easy to display tips in your app. Now you can group tips so features are discovered in the ideal order, make tips reusable with custom tip identifiers, match the look and feel to your app, and sync tips using CloudKit. Learn how you can use the latest advances in TipKit to help people discover everything your app has to offer.
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.
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Discover the newest advancements on Apple platforms.
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Create a custom data store with SwiftData
Combine the power of SwiftData’s expressive, declarative modeling API with your own persistence backend. Learn how to build a custom data store and explore how to progressively add persistence features in your app. To get the most out of this session, watch “Meet SwiftData” and “Model your schema with SwiftData” from WWDC23.
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.
What’s new in SwiftData
SwiftData makes it easy to add persistence to your app with its expressive, declarative API. Learn about refinements to SwiftData, including compound uniqueness constraints, faster queries with #Index, queries in Xcode previews, and rich predicate expressions. Join us to explore how you can use all of these features to express richer models and improve performance in your app. To discover how to build a custom data store or use the history API in SwiftData, watch “Create a custom data store with SwiftData” and “Track model changes with SwiftData history”.
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.
Track model changes with SwiftData history
Reveal the history of your model’s changes with SwiftData! Use the history API to understand when data store changes occurred, and learn how to use this information to build features like remote server sync and out-of-process change handing in your app. We’ll also cover how you can build support for the history API into a custom data store.