novembris 18, 2020 —
Posted by Pankaj Kanwar and Fred Alcober
With TensorFlow 2, best-in-class training performance on a variety of different platforms, devices and hardware enables developers, engineers, and researchers to work on their preferred platform. TensorFlow users on Intel Macs or Macs powered by Apple’s new M1 chip can now take advantage of accelerated training using Apple’s Mac-optimized version of Tensor…
With TensorFlow 2, best-in-class training performance on a variety of different platforms, devices and hardware enables developers, engineers, and researchers to work on their preferred platform. TensorFlow users on Intel Macs or Macs powered by Apple’s new M1 chip can now take advantage of accelerated training using Apple’s Mac-optimized version of TensorFlow 2.4 and the new ML Compute framework. These improvements, combined with the ability of Apple developers being able to execute TensorFlow on iOS through TensorFlow Lite, continue to showcase TensorFlow’s breadth and depth in supporting high-performance ML execution on Apple hardware.
The Mac has long been a popular platform for developers, engineers, and researchers. With Apple’s announcement last week, featuring an updated lineup of Macs that contain the new M1 chip, Apple’s Mac-optimized version of TensorFlow 2.4 leverages the full power of the Mac with a huge jump in performance.
ML Compute, Apple’s new framework that powers training for TensorFlow models right on the Mac, now lets you take advantage of accelerated CPU and GPU training on both M1- and Intel-powered Macs.
For example, the M1 chip contains a powerful new 8-Core CPU and up to 8-core GPU that are optimized for ML training tasks right on the Mac. In the graphs below, you can see how Mac-optimized TensorFlow 2.4 can deliver huge performance increases on both M1- and Intel-powered Macs with popular models.
Training impact on common models using ML Compute on M1- and Intel-powered 13-inch MacBook Pro are shown in seconds per batch, with lower numbers indicating faster training time. |
Training impact on common models using ML Compute on the Intel-powered 2019 Mac Pro are shown in seconds per batch, with lower numbers indicating faster training time. |
Users do not need to make any changes to their existing TensorFlow scripts to use ML Compute as a backend for TensorFlow and TensorFlow Addons.
To get started, visit Apple’s GitHub repo for instructions to download and install the Mac-optimized TensorFlow 2.4 fork.
In the near future, we’ll be making updates like this even easier for users to get these performance numbers by integrating the forked version into the TensorFlow master branch.
You can learn more about the ML Compute framework on Apple’s Machine Learning website.
Footnotes:
novembris 18, 2020
—
Posted by Pankaj Kanwar and Fred Alcober
With TensorFlow 2, best-in-class training performance on a variety of different platforms, devices and hardware enables developers, engineers, and researchers to work on their preferred platform. TensorFlow users on Intel Macs or Macs powered by Apple’s new M1 chip can now take advantage of accelerated training using Apple’s Mac-optimized version of Tensor…