But it’s still a laptop, so don’t expect some crazy performance. The M1 chip does come with a neural engine, and it should help a bit for basic deep learning tasks if you’re into that. I’m aware no one buys Macs for gaming, but having the option can’t hurt. This means you can forget occasional gaming sessions. It doesn’t support eGPU at all, and there’s nothing you can do about it. Still, having an option to connect a GPU via thunderbolt was always an option with Intel-based Macs. I’m completely fine with sacrificing a dedicated Nvidia GPU to get an ultraportable and sleek-looking laptop. There are some ways around it, like purchasing a docking station with DisplayLink, but the recommended ones weren’t available on Amazon the last time I checked. #Python mac m1 chip pro#Unfortunately, the M1 chip in Macbook Pro and Macbook Air supports only a single monitor. Having two relatively cheap and color-accurate monitors is a way to go for me. Having a single monitor and pivoting it isn’t the best option. In a nutshell - vertical space is a massive productivity booster for anything involving code. #Python mac m1 chip code#Take a look at the following image and you’ll immediately get the gist: Image 2 - Vertical space for code and code editors (image by author) Say what you want, but writing code on a vertical monitor is not something you can easily let go of. One of them is in a normal horizontal position, while the other is pivoted vertically, as you can see from the following image: Image 1–2X Dell U2419H monitors (image by author) Sure, work from home means work from bed on some days, but you’ll need that extra screen real estate more often than not. External Display Supportįeel free to skip this section if you’re using a single external display.ġ3" isn’t enough for comfortable 8+ hours work sessions. #Python mac m1 chip professional#I’m sure any other tech professional can add issues to the list. This was just a short list of things that didn’t work or didn’t work as expected. The only sane way to do so with Python is through Oracle Instant Client, which isn’t ported to the new chip. Still, it’s not native support.įor my daily job, I need to communicate with cloud databases a lot, mainly Oracle. This means the entire distribution runs through an emulator called Rosetta 2, which does a terrific job. Kind of a hassle if you want TensorFlow always available.Īnaconda worked fine, but there wasn’t an official release for the M1 chip at the time of testing. #Python mac m1 chip install#Needless to say, but these versions get overridden when installing other packages if you don’t specify some extra parameters (or if you don’t install them in a virtual environment). Want to install TensorFlow? Great, but please install a specific version of Numpy and five other packages beforehand. Even after the downgrade, the only consistent thing I saw when installing libraries natively was a bunch of red lines in the Terminal. Not a big deal, but an extra step for sure. The default Python 3 on M1 was 3.9.x, which you’ll first have to downgrade to 3.8.x to make some libraries work. Still, Anaconda seemed like a go-to way on the M1 chip. Overall, it’s a great idea, but I prefer a clean installation of Python 3 and dependency management on the fly. I’m not a big fan of Anaconda Python distribution. That’s precisely how the article is structure, so feel free to navigate to a section that interests you the most: In case you want a single sentence summary - some data science libraries are either impossible or near to impossible to run natively, connecting two external displays is a nightmare, and finally, eGPUs aren’t supported. Imagine a blazingly fast processor, all-day battery life, and no thermal issues.
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