But this approach lacks the possibility of reproducible environments and therefore was not an option. There is the alternative way of using Miniforge which is a variant of Conda.
#Shelf life install
In short: With pip install I was not able to get a running environment with all necessary packages installed. Furthermore, a lot of major packages like Numpy and Pandas are using C/C++ extensions for getting better performance, too. Although Python is a scripting language, this also holds true for it because the interpreter is written in C. The disadvantage, on the other hand, is that all software either needs to be emulated (with Rosetta2) or recompiled with an architecture-specific compiler for arm64 (M1) instead of x86 like before … Unfortunately, a recompilation is unlikely to work out of the box and code changes must be applied. On the one hand, the advantage of this is that Apple became independent from Intel so they could design their own chip. Then I ran into … ProblemsĪpple’s M1 chip is built on the ARM architecture in contrast to 圆4 chips used in prior Macbook versions. The whole installation started smoothly until I wanted to run my Python projects.
In December last year I was privileged enough to choose a new business laptop and so I took the opportunity to get a Macbook Pro 16 with M1Pro Silicon. But for small experiments, and of course debugging, my hope was to save a lot of time. Usually, I don’t need to run long trainings of neural networks on my laptop. Last year, I got pretty excited about the announcement of the new versions of the Apple M1 chip because it offered a much higher performance. Thus, my language of choice is Python and I am using it in several projects on a daily basis. I have been working as a data scientist at codecentric for several years now.
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