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project_6/cccl_upstream/ci/test_cuda_cccl_headers_python.sh

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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
#!/usr/bin/env bash
set -euo pipefail
ci_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
source "$ci_dir/pyenv_helper.sh"
# Parse common arguments
source "$ci_dir/util/python/common_arg_parser.sh"
parse_python_args "$@"
# Pin cuda-toolkit to the container's CTK minor and set cuda_version /
# cuda_major_version (-ctk-mode latest opts out). See pyenv_helper.sh.
pin_cuda_toolkit "${ctk_mode}"
# Setup Python environment
setup_python_env "${py_version}"
# Fetch or build the cuda_cccl wheel:
if [[ -n "${GITHUB_ACTIONS:-}" ]]; then
wheel_artifact_name=$("$ci_dir/util/workflow/get_wheel_artifact_name.sh")
"$ci_dir/util/artifacts/download.sh" "${wheel_artifact_name}" /home/coder/cccl/
else
"$ci_dir/build_cuda_cccl_python.sh" -py-version "${py_version}"
fi
# Install cuda_cccl
CUDA_CCCL_WHEEL_PATH="$(ls /home/coder/cccl/wheelhouse/cuda_cccl-*.whl)"
ctk_flavor="$(ctk_extra_flavor "${ctk_mode}")"
python -m pip install "${CUDA_CCCL_WHEEL_PATH}[test-${ctk_flavor}${cuda_major_version}]"
# Run tests for core package
cd "/home/coder/cccl/python/cuda_cccl/tests/"
python -m pytest -n auto -v headers/