Create virtual environment named tf_macos with conda and install Python 3.8.The installation defaults to directory ~/miniforge3/. I mainly followed instructions from here to install tensorflow-macos, and then created ipykernel of it so that I can run this environment any time by switching the kernel inside jupyter notebook.įirst install miniforge, which natively supports M1. From the docs it appears that full official support of Apple M1 started with 3.9.1: As of 3.9.1, Python now fully supports building and running on macOS 11.0 (Big Sur) and on Apple Silicon Macs (based on the ARM64 architecture). However, my goal was to install it with other python so that I can install additional packages for data science without difficulty. If you use an M1 mac and depend upon scientific packages that have not been built for arm64 yet running the x86 version of python and Conda may be the. It is easy to install it with the system python since the installation script is given by Apple. Install TensorFlow-macOS for Apple Silicon M1 Install TensorFlow-macOS for Apple Silicon M1.The result is not elegant for sure, but I am satisfied for now.
Running everything else natively if possible.Not using the “system python” ( /usr/bin/python).
This is the post written for myself, after running about in confutsion to set up the environment for machine learning on M1 mac. So after (almost) 7 years spent with my MBP (mid 2014), I decided to leave Intel and join M1. It became headlines especially because of its outstanding performance, not in the ARM64-territory, but in all PC industry.Īs a student majoring in statistics with coding hobby, somewhere inbetween a consumer tech enthusiast and a programmer, I was one of the people who was dazzled by the benchmarks and early reviews emphasizing it. Macs with ARM64-based M1 chip, launched shortly after Apple’s initial announcement of their plan to migrate to Apple Silicon, got quite a lot of attention both from consumers and developers.
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