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99d306450e
* Specify multiple architectures for Julia to precompile to For amd64 (x86_64), we should specify what specific targets the precompilation should be done for. If we don't specify it, it's *only* done for the target of the host doing the compilation. When the container runs on a host that's still x86_64, but a *different* generation of CPU than what the build host was, the precompilation is useless and Julia takes a long long time to start up. This specific multitarget comes from https://docs.julialang.org/en/v1/devdocs/sysimg/#Specifying-multiple-system-image-targets, and is the same set of options that the official Julia x86_64 build is compiled with. If the architecture the container runs on is different, precompilation may still have to be re-done on first startup - but this *should* catch most of the issues. h/t to https://discourse.julialang.org/t/is-it-possible-to-make-precompilation-portable-for-docker-images-built-with-a-different-cpu/95913 which helped point me towards `JULIA_CPU_TARGET`. Fixes https://github.com/jupyter/docker-stacks/issues/2015 for more information * Fix bash syntax issue * Add JULIA_CPU_TARGET for aarch64 as well - Don't need `export` as this is only used within this script - Steal from upstream what should be setup for aarch64 * Re-add export for JULIA_CPU_TARGET Quietens pre-commit
56 lines
2.3 KiB
Bash
Executable File
56 lines
2.3 KiB
Bash
Executable File
#!/bin/bash
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set -exuo pipefail
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# Requirements:
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# - Run as a non-root user
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# - The JULIA_PKGDIR environment variable is set
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# - Julia is already set up, with the setup-julia.bash command
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# If we don't specify what CPUs the precompilation should be done for, it's
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# *only* done for the target of the host doing the compilation. When the
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# container runs on a host that's the same architecture, but a *different*
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# generation of CPU than what the build host was, the precompilation is useless
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# and Julia takes a long long time to start up. This specific multitarget comes
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# from https://github.com/JuliaCI/julia-buildkite/blob/70bde73f6cb17d4381b62236fc2d96b1c7acbba7/utilities/build_envs.sh#L20-L76,
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# and may need to be updated as new CPU generations come out.
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# If the architecture the container runs on is different,
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# precompilation may still have to be re-done on first startup - but this
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# *should* catch most of the issues. See
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# https://github.com/jupyter/docker-stacks/issues/2015 for more information
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if [ "$(uname -m)" == "x86_64" ]; then
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# See https://github.com/JuliaCI/julia-buildkite/blob/70bde73f6cb17d4381b62236fc2d96b1c7acbba7/utilities/build_envs.sh#L24
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# for an explanation of these options
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export JULIA_CPU_TARGET="generic;sandybridge,-xsaveopt,clone_all;haswell,-rdrnd,base(1)"
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elif [ "$(uname -m)" == "aarch64" ]; then
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# See https://github.com/JuliaCI/julia-buildkite/blob/70bde73f6cb17d4381b62236fc2d96b1c7acbba7/utilities/build_envs.sh#L54
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# for an explanation of these options
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export JULIA_CPU_TARGET="generic;cortex-a57;thunderx2t99;carmel"
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fi
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# Install base Julia packages
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julia -e '
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import Pkg;
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Pkg.update();
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Pkg.add([
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"HDF5",
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"IJulia",
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"Pluto"
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]);
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Pkg.precompile();
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'
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# Move the kernelspec out of ${HOME} to the system share location.
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# Avoids problems with runtime UID change not taking effect properly
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# on the .local folder in the jovyan home dir.
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mv "${HOME}/.local/share/jupyter/kernels/julia"* "${CONDA_DIR}/share/jupyter/kernels/"
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chmod -R go+rx "${CONDA_DIR}/share/jupyter"
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rm -rf "${HOME}/.local"
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fix-permissions "${JULIA_PKGDIR}" "${CONDA_DIR}/share/jupyter"
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# Install jupyter-pluto-proxy to get Pluto to work on JupyterHub
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mamba install --yes \
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'jupyter-pluto-proxy' && \
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mamba clean --all -f -y && \
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fix-permissions "${CONDA_DIR}" && \
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fix-permissions "/home/${NB_USER}"
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