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Convert models from third-party training libraries into Core ML using the coremltools Python package.
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The TensorFlow Metal plugin has achieved its 1.0 release with a streamlined installation process, a graph optimizer, and mixed-precision support using 16-bit float and newly introduced 16-bit brain float datatypes.
With extensive operator and network coverage, improved testing, and performance optimizations, the MPS backend has achieved its beta release. New features, such as profiling, custom kernels, and mixed-precision, enable you to accelerate and optimize more ML models.
The new JAX Metal plugin uses the OpenXLA compiler and PjRT runtime to GPU-accelerate JAX machine learning workloads on Mac platforms.
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Learn how to optimize the design and user experience of your app for machine learning.
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Read the latest machine learning research from Apple. Get updates on upcoming events and stay up to date on all news about machine learning.
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