In 2020, the field of open-source Data Engineering is finally coming-of-age. In addition to the heavy duty proprietary s

Apple M1 CPU has one of the highest single-core scores at 1687; ok score on multi-core score at 7433 compared to the Intel i9–9880H (Macbook Pro 16”): 1029 single-core, 6012 multi-core score.

Some are claiming you can perform inference from Tensorflow models by taking advantage of the Apple Converter. Here is a link to an article talking about converting from TF models to CoreML. Some others are thinking they are referring to Swift for Tensorflow.

It is unclear right now if Juypter notebooks work with the Apple M1 processor. I have found one link that is running juypter notebooks on an ARM architecture using docker. Time will tell on this one. Maybe VSCode once it is production-ready will have a Juypter extension that will work.

Looking at these values, it would probably work well for video processing, image processing, Apple Arcade, or gaming. The GPU will not work well for machine learning. It could, but just not the performance me and you are probably looking for. Something like the Razer Blade Pro 17 with the RTX 2080 Super would do better at 11.2 TFLOPS. You would be right with that thought. However, Apple also added in the M1 a Neural Engine integrated into the processor specifically for machine learning efficiency.

At the Apple M1 event, Apple mentioned support for Tensorflow. I have yet to find any specifics around that claim. The Tensorflow framework is built for the x86_64 architecture and Nvidia GPUs. There are no other builds for ARM or Apple M1 architecture.

Microsoft also announced on November 11, 2020, that it will launch a new Universal build of its Mac Office 2019 beta that supports the new Apple M1 processor. As a matter of fact, Erik Schwiebert announced on Twitter that Microsoft was releasing a beta version that same day on the beta channel. However, they did not announce any dates for the public release.

Will Scikit-learn work with the Apple M1? I think the short answer is maybe??? So, what we know is that Python will work for the ARM architecture of the M1, and Scikit-learn doesn’t take advantage of or use GPU support. So theoretically, Scikit-learn should work for the Apple M1.

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hibited it. When I dated conventionally, it always followed the same pattern: Meet a girl, get to know them, they like the vibe. Then I start my crazy travel schedule and there’s an awful lot of stress and texting. When are you coming back? Where are you?

When you combined that speed with integrated memory (reduced latency) and 20 hours of battery life, that could be a game-changer for data scientists on the go. You not only get a GPU comparable to an RTX 2080 Super, a CPU that has one of the best single-core scores in the industry, a form factor of the MacBook Air, but also 18–20 hours of battery life. Wow!

Apple M1 Neural Engine advertises 11 trillion operators per second. The question here is, is that TFLOPS (FP32)? This is compared to the Nvidia GeForce RTX 2080 Super at 11.2.

Microsoft announced on November 10, 2020, on Twitter, they are working on a Universal build of VSCode for the Apple M1. As of writing this, they have the ARM experimental build upon their site located here.

The M1 processor also brings Apple’s industry-leading Neural Engine specifically designed for machine learning processes. The neural engine has previously been added to the A-series processor on the iPad and iPhone but has yet to be on the Mac until now.

Disclaimer: I have not gotten my hands on an Apple M1 yet. I have ordered it but yet to received it. I am only using research to make my claims on software support. Don’t make purchases based on my results, please perform your own research before making a purchase.

Apple Silicon uses an ARM-based instruction set architecture. Python already runs on many other platforms including ARM, MIPS, PowerPC, i386, and x86–64. There should be no trouble with Python.

Disclaimer: I have not gotten my hands on an Apple M1 yet. I have ordered it but yet to received it. I am only using research to make my claims on software support. Don’t make purchases based on my results, please perform your own research before making a purchase.