![]() The training and testing took 6.70 seconds, 14% faster than it took on my RTX 2080Ti GPU! I was amazed. I installed the tensorflow_macos on Mac Mini according to the Apple GitHub site instructions and used the following code to classify items from the fashion-MNIST dataset. Since I got the new M1 Mac Mini last week, I decided to try one of my TensorFlow scripts using the new Apple framework. This starts by applying higher-level optimizations such as fusing layers, selecting the appropriate device type and compiling and executing the graph as primitives that are accelerated by BNNS on the CPU and Metal Performance Shaders on the GPU.” “The new tensorflow_macos fork of TensorFlow 2.4 leverages ML Compute to enable machine learning libraries to take full advantage of not only the CPU, but also the GPU in both M1- and Intel-powered Macs for dramatically faster training performance. With Macs powered by the new M1 chip, and the ML Compute framework available in macOS Big Sur, neural networks can now be trained right on the Macs with a massive performance improvement. Since Apple doesn’t support NVIDIA GPUs, until now, Apple users were left with machine learning (ML) on CPU only, which markedly limited the speed of training ML models. Both of them support NVIDIA GPU acceleration via the CUDA toolkit. The two most popular deep-learning frameworks are TensorFlow and PyTorch.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |