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Although release workflows don't run tests, they do go through a week of nightly testing to ensure everything works as expected. See [this page]({% link releases/process.md %}) for more details about the release process.
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RAPIDS uses a collection of reusable GitHub Actions workflows in order to single-source common build configuration settings.
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These reusable workflows can be found in the [rapidsai/shared-workflows](https://github.com/rapidsai/shared-workflows) repository.
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An example of one of the reusable workflows used by RAPIDS is the [`conda-cpp-build.yaml` workflow](https://github.com/rapidsai/shared-workflows/blob/branch-25.08/.github/workflows/conda-cpp-build.yaml), which is the source of truth for which architectures and CUDA versions build RAPIDS C++ packages.
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An example of one of the reusable workflows used by RAPIDS is the [`conda-cpp-build.yaml` workflow](https://github.com/rapidsai/shared-workflows/blob/main/.github/workflows/conda-cpp-build.yaml), which is the source of truth for which architectures and CUDA versions build RAPIDS C++ packages.
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Similarly, the [`conda-cpp-tests.yaml` workflow](https://github.com/rapidsai/shared-workflows/blob/branch-25.08/.github/workflows/conda-cpp-tests.yaml) specifies configurations for testing RAPIDS C++ packages.
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Similarly, the [`conda-cpp-tests.yaml` workflow](https://github.com/rapidsai/shared-workflows/blob/main/.github/workflows/conda-cpp-tests.yaml) specifies configurations for testing RAPIDS C++ packages.
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The majority of these reusable workflows leverage the CI images from the [rapidsai/ci-imgs](https://github.com/rapidsai/ci-imgs/) repository.
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Valid values for `{artifact-name}` can be found on the "Actions" tab in the GitHub Actions UI, as described in "Finding Artifacts in the GitHub UI" above.
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The run IDs can also be identified programmatically.
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For example, the following sequence of commands accomplishes the task *"download the latest `rmm` Python 3.12, CUDA 12 conda packages built from `branch-25.08`"*.
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For example, the following sequence of commands accomplishes the task *"download the latest `rmm` Python 3.12, CUDA 12 conda packages built from `main`"*.
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```shell
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# get the most recent successful branch-25.08 nightly or branch build
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# get the most recent successful main nightly or branch build
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RUN_ID=$(
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gh run list \
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--repo "rapidsai/rmm" \
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--branch "branch-25.08" \
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--branch "main" \
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--workflow "build.yaml" \
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--status "success" \
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--json "createdAt,databaseId" \
@@ -215,14 +215,14 @@ To use these:
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* download them to local directories using the `gh` CLI
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* pass the paths to those directories as channels via `--channel` to `conda` / `mamba` commands
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For example, to create a conda environment that uses the latest `librmm` and `rmm` conda packages built from `branch-25.08` on an x86_64, CUDA 12 system:
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For example, to create a conda environment that uses the latest `librmm` and `rmm` conda packages built from `main` on an x86_64, CUDA 12 system:
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```shell
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# get the most recent successful nightly or branch build
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RUN_ID=$(
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gh run list \
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--repo "rapidsai/rmm" \
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--branch "branch-25.08" \
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--branch "main" \
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--workflow "build.yaml" \
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--status 'success' \
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--json 'createdAt,databaseId' \
@@ -309,7 +309,7 @@ To use these:
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* download them to local directories using the `gh` CLI
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* pass the paths to wheels in those directories to installers like `pip` or `uv`
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For example, to create a virtual environment with `librmm` and `rmm` packages built from `branch-25.08` on an x86_64, CUDA 12 system:
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For example, to create a virtual environment with `librmm` and `rmm` packages built from `main` on an x86_64, CUDA 12 system:
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