Shushi Hong
1c27ccf531
This pr is the follow-up pr of #19754. It removes the redundant `pip install -U pip cmake ninja` (since those are already installed in docker) and the unused python_bin variable, and update the comment to match: the image provides the build tools, and PATH only needs the bundled CPython (for the configure) plus the CUDA toolchain (for nvcc). Also fixs a stale comment in the Windows sidecar: it claimed the conda 13.0.2 pin matched the Linux hook, but the Linux side is now CUDA 13.1 (from the image). The win-64 nvidia channel still caps at 13.0.x, so Windows stays on 13.0.2 -- slightly behind Linux but harmless, since the sidecar has no device code and links the CUDA runtime by soname only. The Windows pip install is kept: that build uses the Ninja generator and runs on a runner without the prebuilt CUDA image.
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TVM wheel packaging
The wheels are built by a standard cibuildwheel flow, configured in
.github/workflows/publish_wheel.yml and pyproject.toml ([tool.cibuildwheel]
and [tool.scikit-build]). This directory holds the few helper scripts that flow
invokes:
manylinux_build_libtvm_runtime_cuda.sh— run by thebuild_cuda_runtimeCI stage; builds thelibtvm_runtime_cuda.sosidecar inside the prebuiltquay.io/manylinux_cudaimage (CUDA toolkit preinstalled).windows_build_libtvm_runtime_cuda.bat— the Windows equivalent (run withshell: cmd); installs the CUDA toolkit via conda and buildstvm_runtime_cuda.dll.build-environment.yaml— conda environment for building the wheel.