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
90 lines
3.3 KiB
ReStructuredText
90 lines
3.3 KiB
ReStructuredText
.. _infra-devcontainer-launching:
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Launching a container
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=====================
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``.devcontainer/launch.sh`` starts a CCCL development container with a chosen CUDA
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toolkit and host compiler, mounts the repo, and either opens VSCode or drops you into
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a shell. It is Linux-only (including WSL2). With no toolchain flags, it uses the default devcontainer
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in ``.devcontainer/devcontainer.json``, which uses the latest CTK + gcc.
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Launch in VSCode
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----------------
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**Open the default container.** Run the script with no flags.
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.. code-block:: bash
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.devcontainer/launch.sh
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The script copies the selected ``devcontainer.json`` into a temporary directory and
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opens VSCode against it.
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**Select a toolchain.** Pass ``--cuda`` and ``--host`` to open a specific variant.
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.. code-block:: bash
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.devcontainer/launch.sh --cuda <cuda-version> --host <host-compiler>
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The temporary-directory copy lets you run multiple variants of the same environment
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side by side, each in its own VSCode window.
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Launch directly in Docker
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-------------------------
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**Drop into a shell.** Add ``--docker`` to skip VSCode and run a bash shell inside the
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container.
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.. code-block:: bash
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.devcontainer/launch.sh --docker --cuda <cuda-version> --host <host-compiler>
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The container mounts the repo at ``/home/coder/cccl`` and removes itself on exit. Any
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trailing arguments after the flags run as a command instead of an interactive shell.
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Specify the toolchain
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---------------------
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``--cuda`` selects the CUDA toolkit version. ``--host`` selects the host compiler.
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The two flags resolve to ``.devcontainer/cuda<cuda>-<host>/devcontainer.json``; an
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unknown combination exits with an error.
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Valid values come from the devcontainers located under ``.devcontainer/``.
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Pass through GPUs
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-----------------
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**Add host GPUs.** Pass ``--gpus all`` to expose every host GPU to the container.
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.. code-block:: bash
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.devcontainer/launch.sh --docker --cuda <cuda-version> --host <host-compiler> --gpus all
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``--gpus`` takes any Docker GPU request string. It overrides the ``hostRequirements.gpu``
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default read from the devcontainer config. Without it, the container starts without GPU
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access, which is sufficient for building tests.
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Launch from a git worktree
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--------------------------
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``launch.sh`` handles linked worktrees automatically. A worktree's ``.git`` is a file
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pointing at the main repository's git directory, which the container cannot reach through
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the worktree mount alone. The script bind-mounts the main repo's git common directory at
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its host path so git operations resolve inside the container.
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The ``cccl-build`` and ``cccl-wheelhouse`` Docker volumes are shared across all worktrees
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and the main checkout, so build artifacts collide between them. Do not run multiple
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worktree containers concurrently unless you are careful to avoid conflicts.
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First-time git auth in a worktree needs the main checkout's ``.config/gh``. If the main
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checkout has never run its container, ``launch.sh`` warns that startup will block on an
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interactive ``gh auth login``. Launch the main checkout's container once, complete the
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login, then re-launch the worktree.
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Forward SSH keys
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----------------
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If ``SSH_AUTH_SOCK`` is set in your environment, ``launch.sh`` forwards the agent socket
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into the container automatically. No flag is required. Git operations over SSH inside the
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container use your host agent's keys.
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