project_6/cccl_upstream/docs/maintainers/how_tos/index.rst

How Tos
=======

How-to guides for maintainers.

.. toctree::
   :maxdepth: 1

   ../backport_process
   compile_time
   plan_work_with_github_issues
   adding_ci_coverage
   adding_new_devcontainer
feat(cccl): integrate missing CCCL directories — python/, ci/, .agent/, docs/, test/ 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 2026-08-07 02:34:33 +00:00			`How Tos`
			`=======`

			`How-to guides for maintainers.`

			`.. toctree::`
			`:maxdepth: 1`

			`../backport_process`
			`compile_time`
			`plan_work_with_github_issues`
			`adding_ci_coverage`
			`adding_new_devcontainer`