![]() ![]() Following the general vibe on the internet, running any data science related, Python based task is “simply impossible right now.” However, with a fair amount of googling, I managed to replicate my setup I initially described here. the beta releases) of VS Code.īut now I am taking a course on Natural Language Processing, and that is heavily engineering driven, including numpy, sciPy and PyTorch. For instance, VS Code stable right now is not released for the ARM architecture, so I had to momentarily switch to the “Insiders” edition (a.k.a. I’ve read on the ups and downs of the new chips extensively, and knew I would run into some trouble when initially setting up everything I am used to. My decision to buy this one was considerably determined by my old MacBook (2017), which both has a keyboard that drives me crazy, and a battery that does no longer deserve that name. (I tend to not use the marketable names “M1” or “Apple Silicon” as they tend to overshadow the fact that the ARM architecture is almost as old as the Intel architecture which ARM has replaced in the new MacBooks.) So a few days ago I finally got my MacBook Pro sporting one of the new ARM-architecture chips. ![]() You can find the new, updated blogpost here! For the most parts, it's still up to date, but I have written a clean, new version based on the developments in the ecosystem. Since a few weeks, I have now received more and more emails by people asking whether this guide is still up-to-date. In this post, I describe how.Īfter I wrote this guide, many many people have relied upon it to set up their data science toolchain on the new M1 MacBooks. However, it is possible to run a development setup natively on the ARM-architecture. However, data scientists and engineers have been wary of upgrading too soon, and in my opinion rightfully so. The new Apple M1 devices have received quite the attention in the past months. ![]()
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