What is max

Generated on 8/1/2024

1 search

The maximum floating-point precision supported in the JAX Metal framework is the Pfloat 16 data type. This data type represents a wide dynamic range of floating point values and is suitable for use cases like mixed precision training. You can use the new data type just like any other data type in JAX.

For more details, you can refer to the session Train your machine learning and AI models on Apple GPUs.

Platforms State of the Union

Platforms State of the Union

Discover the newest advancements on Apple platforms.

Port advanced games to Apple platforms

Port advanced games to Apple platforms

Discover how simple it can be to reach players on Apple platforms worldwide. We’ll show you how to evaluate your Windows executable on Apple silicon, start your game port with code samples, convert your shader code to Metal, and bring your game to Mac, iPhone, and iPad. Explore enhanced Metal tools that understand HLSL shaders to validate, debug, and profile your ported shaders on Metal.

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.

What’s new in USD and MaterialX

What’s new in USD and MaterialX

Explore updates to Universal Scene Description and MaterialX support on Apple platforms. Discover how these technologies provide a foundation for 3D content creation and delivery, and learn how they can help streamline your workflows for creating great spatial experiences. Learn about USD and MaterialX support in RealityKit and Storm, advancements in our system-provided tooling, and more.

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.