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

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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: ${{ inputs.ref || github.ref_name }}
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path: package
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- name: Get package spec
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id: pkg
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run: |
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if [[ -n "${{ 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" >> $GITHUB_OUTPUT
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fi
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VERSION=$(grep "^version = " package/Project.toml | cut -d'"' -f2)
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echo "version=$VERSION" >> $GITHUB_OUTPUT
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- name: Get CUDA major version
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id: cuda
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run: |
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CUDA_MAJOR=$(echo ${{ matrix.cuda }} | cut -d'.' -f1)
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echo "major=${CUDA_MAJOR}" >> $GITHUB_OUTPUT
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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: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ghcr.io/${{ github.repository }}
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tags: |
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type=raw,value=${{ steps.pkg.outputs.name }}-julia${{ matrix.julia }}-julia${{ steps.cuda.outputs.major }}
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type=raw,value=${{ steps.pkg.outputs.name }},enable=${{ matrix.default == true && (github.ref_type == 'tag' || inputs.tag != '') }}
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type=raw,value=latest,enable=${{ matrix.default == true && (github.ref_type == 'tag' || (inputs.tag != '' && inputs.mark_as_latest)) }}
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type=raw,value=dev,enable=${{ matrix.default == true && github.ref_type == 'branch' && inputs.tag == '' }}
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labels: |
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org.opencontainers.image.version=${{ steps.pkg.outputs.version }}
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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: .
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push: true
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provenance: false # the build fetches the repo again, so provenance tracking is not useful
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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 }}

‎Dockerfile

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# example of a Docker container for CUDA.jl with a specific toolkit embedded at run time.
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#
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# supports selecting a Julia and CUDA toolkit version, as well as baking in specific
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# versions of the CUDA.jl package (identified by a spec recorgnized by the Pkg REPL).
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#
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# CUDA.jl and other packages are shipped in a system depot, with the user depot mounted
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# at `/depot`. persistency is possible by mounting a volume at this location.
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# running with reduced privileges (by using `--user`) is also supported.
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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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ARG PACKAGE_SPEC=CUDA
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LABEL org.opencontainers.image.authors="Tim Besard <[email protected]>" \
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org.opencontainers.image.description="A CUDA.jl container with CUDA ${CUDA_VERSION} and Julia ${JULIA_VERSION}" \
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org.opencontainers.image.title="CUDA.jl" \
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org.opencontainers.image.url="https://juliagpu.org/cuda/" \
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org.opencontainers.image.source="https://github.com/JuliaGPU/CUDA.jl" \
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org.opencontainers.image.licenses="MIT"
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# system-wide packages
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# no trailing ':' as to ensure we don't touch anything outside this directory. without it,
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# Julia touches the compilecache timestamps in its shipped depot (for some reason; a bug?)
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ENV JULIA_DEPOT_PATH=/usr/local/share/julia
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RUN julia -e 'using Pkg; Pkg.add("CUDA")'
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# pre-install the CUDA toolkit from an artifact. we do this separately from CUDA.jl so that
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# this layer can be cached independently. it also avoids double precompilation of CUDA.jl in
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# order to call `CUDA.set_runtime_version!`.
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RUN julia -e '#= configure the preference =# \
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env = "/usr/local/share/julia/environments/v$(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 = \"'${CUDA_VERSION}'\""); \
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\
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#= install the JLL =# \
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using Pkg; \
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Pkg.add("CUDA_Runtime_jll")' && \
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#= demote the JLL to an [extras] dep =# \
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find /usr/local/share/julia/environments -name Project.toml -exec sed -i 's/deps/extras/' {} + && \
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#= remove nondeterminisms =# \
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cd /usr/local/share/julia && \
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rm -rf compiled registries scratchspaces logs && \
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find -exec touch -h -d "@0" {} + && \
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touch -h -d "@0" /usr/local/share
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# hard-code a CUDA toolkit version
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RUN julia -e 'using CUDA; CUDA.set_runtime_version!(v"12.2")'
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# re-importing CUDA.jl below will trigger a download of the relevant artifacts
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# generate the device runtime library for all known and supported devices.
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# this is to avoid having to do this over and over at run time.
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RUN julia -e 'using CUDA; CUDA.precompile_runtime()'
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# install CUDA.jl itself
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RUN julia -e 'using Pkg; pkg"add '${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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# we add the user environment from a start-up script
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# so that the user can mount `/depot` for persistency
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ENV JULIA_DEPOT_PATH=/usr/local/share/julia:
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COPY <<EOF /usr/local/share/julia/config/startup.jl
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if !isdir("/depot/environments/v$(VERSION.major).$(VERSION.minor)")
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mkpath("/depot/environments")
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cp("/usr/local/share/julia/environments/v$(VERSION.major).$(VERSION.minor)",
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"/depot/environments/v$(VERSION.major).$(VERSION.minor)")
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end
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pushfirst!(DEPOT_PATH, "/depot")
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EOF
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WORKDIR "/workspace"

‎README.md

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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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