I can still shell into my old x86 Hackintosh desktop running plaidML in a pinch. I’ve got the basics of AWS and I’m taking classes on Docker, Kubernetes, Terraform, etc. That said, UX is probably more important to me than raw computing power on my local everyday machine, in a world where the heavy lifting of production ML is increasingly performed on cloud servers. It’s also not the most powerful laptop you could get for the price. Still, the new Apple chips are fast, and the developer community is working to bridge the gap. Basically, it’s too new-a problem I’m not accustomed to having. I was aware of some catches with Apple Silicon: The new arm64 processor architecture has posed a challenge for data science library compatibility. But there’s just a special something about the industrial design of MacBooks! I like how effortless it is to throw a MacBook air in a backpack, to bring it with you.
HOW TO DOWNLOAD FORGE ON MAC 2018 PLUS
I considered switching to Windows (and making use of WSL2): That way, I could still have my stupid GUI apps plus a Linuxy dev environment, then splurge on hardware.
I would have considered an Ubuntu setup, but I’m a sucker for some commercialized mac/win-only apps like Adobe Lightroom. It’s like miniconda, but with Apple M1 supported by default. without Rosetta 2 translation) on your new M1 MacBook, save yourself a lot of hassle: Use miniforge. If you’re hoping to run Python libraries natively (i.e.
In April 2021 I was granted the privilege of unboxing a brand new 2020 M1 Apple MacBook Air.
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