This should be a fair comparison of the relative performance you will be able to expect for training machine learning models once the compatibility quirks are eventually sorted out in a few months. Instead, we used Apple's CreateML to perform our benchmarks. With some effort, we were able to get Jupyter notebooks running on Apple Silicon, for example, but the pre-release version of TensorFlow for Mac wasn't ready for primetime just yet (notably SciPy is not yet compatible with the M1 which is required for TensorFlow's Object Detection API). While Apple announced support for TensorFlow training on the M1, the toolchain isn't quite ready yet. If you prefer a video version of this post, subscribe to our YouTube channel. There have been several impressive benchmarks around its performance relative to its Intel-based predecessors, but we were interested in putting it through its paces on a machine learning (and, specifically, a computer vision) workload. We've also walked through this M1 machine learning benchmark on YouTube.Ī few weeks ago, Apple released its first custom-designed silicon chip for the Mac, the M1. ![]() ![]() As compared to the the Intel-based 13" Macbook Pro.
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