Most deep-learning systems use a compute-graph-based framework such as TensorFlow or PyTorch. Tests of the optimized TensorFlow library on several popular neural network benchmarks show "dramatically faster" training times compared to the standard code, with an up to 7x improvement for the optimized library on Apple's new M1 hardware.
To take full advantage of ML Compute, Apple has provided a version of TensorFlow binaries targeted for the platform. ML Compute improves the performance of compute-graph-based deep-learning libraries such as TensorFlow by optimizing the graph itself and executing its primitives via accelerated libraries such as BNNS for CPU training and Metal Performance Shaders for the GPU. ML Compute provides optimized mathematical libraries to improve training on CPU and GPU on both Intel and M1-based Macs, with up to a 7x improvement in training times using the TensorFlow deep-learning library.Īpple's Machine Learning blog gave a high-level overview of the ML Compute framework. As part of the recent macOS Big Sur release, Apple has included the ML Compute framework.
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