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Multiarch builds #72
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Here's a current strategy that lets you run a container built for Intel on Apple silicon: https://tongfamily.com/2021/12/21/docker-on-apple-m1-break-on-jupyter-notebooks-switch-to-podman/ @martinfleis has tried it and confirmed it worked. It's not ideal but gets around for the time being. |
Adding as we start readying for Currently, the |
Quick update that, on a M2 Pro chip, the 9.0 container seems to (mostly) work under Rosetta. This needs to be turned on explicitly in Docker for Mac's advanced options. In terms of performance, all Python libraries seem to work fine (e.g., the GDS Book completes fully), but some of the dev utilities in For folks wanting to install the Python stack natively, there is now an explicit file at: https://github.com/darribas/gds_env/blob/master/gds_py/gds_py_explicit_macos-arm.txt |
We could build an arm version of the container, no? I think that Jupyter containers have both versions. |
The container currently doesn't run on M1 Macs and cannot even be built for
osx-arm64
architecture as it is. I have initiated conda-forge builds for bothosx-arm64
(conda-forge/conda-forge-pinning-feedstock#2161) that is needed to install packages natively via conda andlinux-aarch64
(conda-forge/conda-forge-pinning-feedstock#2167) that is used within arm-based container. Once that work is done (it may take a while as not all libraries can be compiled without issues) we may consider building the container for both architectures, similarly to whatjupyter/minimal-notebook
does. Here's a guide https://www.docker.com/blog/multi-arch-build-and-images-the-simple-way/.Some of the libraries that do not come from conda-forge at the moment can be installed via pip, which compiles them on demand, but I am not sure if all would work (probably not).
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