2a7ca101d7
Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream: Added: - python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc. Includes 204 .py files with full test coverage for all 27 algorithms - ci/ (163 files) — Build/test infrastructure build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml Directly maps to our [INFRA-CI] and [INFRA-BUILD] items - .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md - docs/ (491 files) — Official CCCL documentation CI references, CMake guides, Python compute docs, libcudacxx PTX docs - test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar) - Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml - CLAUDE.md symlink → AGENTS.md (NVIDIA's standard) cccl_upstream now mirrors full NVIDIA/cccl structure: Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks) After: 53M (+python +ci +docs +.agent +test +configs) This completes the CCCL base needed for: - [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds - [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations - [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh - Agent workflow: .agent/skills/ for consistent style and test patterns
53 lines
2.4 KiB
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53 lines
2.4 KiB
ReStructuredText
.. _infra-devcontainer-overview:
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Devcontainer overview
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=====================
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CCCL devcontainers package a CUDA toolkit and host compiler into a Docker image
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that matches a CI environment exactly. The same image that builds and tests a
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project in GitHub Actions runs on a developer's machine. Reproducing a CI
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result locally requires no guesswork about toolchain versions: pick the
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combination, launch the container, and the compiler, CTK, and supporting tools
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are identical to what CI used.
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Two uses: local development and CI
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----------------------------------
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**Local development.** Open the repository in VSCode and select a devcontainer
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from the picker, or launch one directly with ``.devcontainer/launch.sh``.
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:ref:`infra-devcontainer-launch-sh-reference` covers ``launch.sh`` flags and the available
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combinations. ``launch.sh --docker`` runs the container without VSCode and drops
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into a shell or runs a script. Without ``--docker``, it opens the container in
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VSCode.
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**CI.** GitHub Actions runs the identical images. A CI job for a given CTK and
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compiler combination builds and tests inside the same ``rapidsai/devcontainers``
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image a developer would launch locally.
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Image source: rapidsai/devcontainers
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------------------------------------
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The base images are built and published by the
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`rapidsai/devcontainers <https://github.com/rapidsai/devcontainers>`_ repository,
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not by CCCL. Each image bundles a CUDA toolkit, a host compiler, and
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development tooling. CCCL references these images by tag; it does not build them.
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Image tags follow the pattern
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``rapidsai/devcontainers:<version>-cpp-<compiler><version>-cuda<ctk>[ext]``.
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The ``-cuda<ctk>`` segment is present for every combination except nvhpc, which
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bundles its own CUDA toolkit; nvhpc images omit it.
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The ``<version>`` tag is defined in ``ci/matrix.yaml`` under
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``devcontainer_version``. A generated ``.devcontainer/<combo>/devcontainer.json``
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contains the resolved tag for any given combination.
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This upstream dependency gates toolchain changes. Adding a CUDA toolkit version
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or a host compiler requires the matching image to exist in rapidsai/devcontainers
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first. The sequence is:
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#. Update rapidsai/devcontainers to build and publish images for the new
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combination.
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#. Update CCCL's ``ci/matrix.yaml`` to reference the new version.
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#. Regenerate CCCL's devcontainer configs to match all matrix workflow requirements.
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:ref:`infra-devcontainer-adding-toolchain` has additional details on this process.
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