We now begin installing all packages that we need. This changes the (base) part to (data-science), which is what we wanted. Type y (for yes) when you are asked whether you want to proceed.Īfter it has finished, it says, among other things: # To activate this environment, use # $ conda activate data-science The '(base)' part means that you've successfully created and activated your first environment! However, for the remainder of this post, we'll use another environment, which we create by typing:

So, rather than merging the projects' dependencies (program 1.0 ~ % Now, project B also requires this program but needs other features only available from version 2.0 onwards. For example, task A might require version 1.0 of a specific program to create fantastic animations. Virtual environments help us maintain differences between different (python/code) projects. We thus begin by setting up virtual environments. This post serves as a follow-up: It shows how to actually prepare the M1 MacBooks for Machine Learning.Ī brief note from my side (March 2022): Nearly half a year after publishing this post's initial version, I still come back here regularly to either update the instructions or get a refresher for myself - especially when setting up TensorFlow… I hope that it serves you equally well.Ī typical setup of Machine Learning includes a) using virtual environments, b) installing all packages within them, c) using python, d) analyzing data, and e) training models. In a previous post, I connected the process of getting things going with our innate desire to learn. To some extent, however, this power can only be unleashed if the system is set up correctly-despite Apple's user-friendliness, this is not a straightforward task. With the newest iteration of its custom M1 chip, the M1 Pro and M1 Max versions, Apple has given the Machine Learning community a powerful tool. 6 min read Photo by Anthony Choren / Unsplash From virtual environments to P圜harm.

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