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Update Dockerfile and add workflow to register.
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Diff for: ‎.github/workflows/Container.yml

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,92 @@
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name: Publish Docker image
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on:
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workflow_dispatch:
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inputs:
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tag:
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description: 'Tag to build instead'
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required: false
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default: ''
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mark_as_latest:
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description: 'Mark as latest'
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type: boolean
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required: false
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default: false
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push:
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tags:
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- 'v*'
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branches:
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- master
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jobs:
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push_to_registry:
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name: Build container - Julia ${{ matrix.julia }} - CUDA ${{ matrix.cuda }}
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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strategy:
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matrix:
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julia: ["1.10", "1.11"]
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cuda: ["11.8", "12.6"]
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include:
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- julia: "1.11"
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cuda: "12.6"
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default: true
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steps:
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- name: Check out the repo
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uses: actions/checkout@v4
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- name: Check out the package
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uses: actions/checkout@v4
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with:
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ref: ${{ inputs.tag || github.ref_name }}
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path: package
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- name: Get package spec
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id: pkg
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run: |
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if [[ -n "${{ inputs.tag }}" ]]; then
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echo "ref=${{ inputs.tag }}" >> $GITHUB_OUTPUT
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echo "name=${{ inputs.tag }}" >> $GITHUB_OUTPUT
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elif [[ "${{ github.ref_type }}" == "tag" ]]; then
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echo "ref=${{ github.ref_name }}" >> $GITHUB_OUTPUT
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echo "name=${{ github.ref_name }}" >> $GITHUB_OUTPUT
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else
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echo "ref=${{ github.sha }}" >> $GITHUB_OUTPUT
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echo "name=dev" >> $GITHUB_OUTPUT
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fi
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- name: Log in to registry
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uses: docker/login-action@v3
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with:
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registry: ghcr.io
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata (tags, labels)
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ghcr.io/${{ github.repository }}
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tags: |
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type=raw,value=${{ steps.pkg.outputs.name }}-julia${{ matrix.julia }}-cuda${{ matrix.cuda }}
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type=raw,value=latest,enable=${{ matrix.default == true && (github.ref_type == 'tag' || inputs.mark_as_latest) }}
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type=raw,value=dev,enable=${{ matrix.default == true && github.ref_type == 'branch' && inputs.tag == '' }}
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Build and push image
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uses: docker/build-push-action@v6
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with:
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context: package
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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build-args: |
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JULIA_VERSION=${{ matrix.julia }}
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CUDA_VERSION=${{ matrix.cuda }}
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PACKAGE_SPEC=CUDA#${{ steps.pkg.outputs.ref }}

Diff for: ‎Dockerfile

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@@ -1,21 +1,47 @@
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# example of a Docker container for CUDA.jl with a specific toolkit embedded at run time.
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FROM julia:1.8-bullseye
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ARG JULIA_VERSION=1
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FROM julia:${JULIA_VERSION}
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ARG CUDA_VERSION=12.6
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# system-wide packages
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ARG PACKAGE_SPEC=CUDA
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LABEL maintainer="Tim Besard <[email protected]>"
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LABEL description="CUDA.jl container with CUDA ${CUDA_VERSION} installed for Julia ${JULIA_VERSION}"
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LABEL version="1.0"
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ENV JULIA_DEPOT_PATH=/usr/local/share/julia
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RUN julia -e 'using Pkg; Pkg.add("CUDA")'
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# system-wide packages
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# hard-code a CUDA toolkit version
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RUN julia -e 'using CUDA; CUDA.set_runtime_version!(v"12.2")'
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# re-importing CUDA.jl below will trigger a download of the relevant artifacts
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# no trailing ':' as to ensure we don't touch anything outside this directory. without it,
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# Julia touches the compilecache timestamps in its shipped depot (for some reason; a bug?)
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ENV JULIA_DEPOT_PATH=/usr/local/share/julia
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# generate the device runtime library for all known and supported devices.
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# this is to avoid having to do this over and over at run time.
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RUN julia -e 'using CUDA; CUDA.precompile_runtime()'
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# pre-install the CUDA toolkit from an artifact. we do this separately from CUDA.jl so that
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# this layer can be cached independently. it also avoids double precompilation of CUDA.jl in
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# order to call `CUDA.set_runtime_version!`.
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RUN julia -e '#= configure the preference =# \
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env = "/usr/local/share/julia/environments/v$(VERSION.major).$(VERSION.minor)"; \
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mkpath(env); \
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write("$env/LocalPreferences.toml", \
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"[CUDA_Runtime_jll]\nversion = \"'${CUDA_VERSION}'\""); \
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\
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#= install the JLL =# \
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using Pkg; \
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Pkg.add("CUDA_Runtime_jll")' && \
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#= remove nondeterminisms =# \
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cd /usr/local/share/julia && \
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rm -rf compiled registries scratchspaces logs && \
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find -exec touch -h -d "@0" {} + && \
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touch -h -d "@0" /usr/local/share
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# install CUDA.jl itself
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RUN julia -e 'using Pkg; pkg"add '${PACKAGE_SPEC}'"; \
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using CUDA; CUDA.precompile_runtime()' && \
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#= remove useless stuff =# \
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cd /usr/local/share/julia && \
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rm -rf registries scratchspaces logs
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# user environment
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# case there might not be a (writable) home directory.
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RUN mkdir -m 0777 /depot
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ENV JULIA_DEPOT_PATH=/depot:/usr/local/share/julia
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ENV JULIA_DEPOT_PATH=/depot:/usr/local/share/julia:
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# make sure the user depot is the one used by default (which requires a valid Project.toml)
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# TODO: do this with a startup script so that you can mount `/depot` as a volume
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# TODO: demote CUDA_Runtime_jll as an [extra] dep so that it doesn't show in the status
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RUN cp -ar /usr/local/share/julia/environments /depot
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WORKDIR "/workspace"

Diff for: ‎README.md

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@@ -59,6 +59,15 @@ This may take a while, as it will precompile the package and download a suitable
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the CUDA toolkit. If your GPU is not fully supported, the above command (or any other
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command that initializes the toolkit) will issue a warning.
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For quick testing, you can also use [the `juliagpu/cuda.jl` container
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image](https://github.com/JuliaGPU/CUDA.jl/pkgs/container/cuda.jl/versions) from the GitHub
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Container Registry, which provides Julia, a precompiled version of CUDA.jl, and a matching
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CUDA toolkit:
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```sh
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docker run -it --rm --gpus=all ghcr.io/juliagpu/cuda.jl:latest # other tags available too
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```
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For more usage instructions and other information, please refer to [the
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documentation](https://juliagpu.github.io/CUDA.jl/stable/).
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