v1.3.0

Changes

• compile_bundle takes ptxas and cuobjdump from the PyPI wheels installed by the producer extra when their paths are not given: nvidia/cu13/bin/ptxas from nvidia-cuda-nvcc and nvidia/cu13/bin/cuobjdump from nvidia-cuda-cuobjdump (with .exe on Windows). It does not read CUDA_HOME. If a wheel or tool is missing, it raises an error that names the package and the searched path and explains how to install the extra or pass the path. The ptxas range check (13.2 or later and earlier than 14) and explicit tool paths are unchanged.
• The producer extra declares triton==3.8.0, nvidia-cuda-nvcc==13.2.*, nvidia-cuda-cuobjdump==13.2.*, nvidia-cuda-crt==13.2.*, and nvidia-nvvm==13.2.*.
• The CMake helpers (cpp/CMakeLists.txt and cpp/cmake/TensoracleCubinPlugin.cmake) find only cuda.h and the CUDA driver link library, and no longer call find_package(CUDAToolkit). Pass the site-packages/nvidia/cu13 directory of PyPI nvidia-cuda-nvcc and nvidia-cuda-runtime 13.2 as CUDAToolkit_ROOT (a CMake variable or environment variable). On Linux the helpers link the NVIDIA driver's libcuda.so, including /usr/lib/wsl/lib/libcuda.so on WSL; on Windows they link the wheel's lib/x64/cuda.lib. When CUDAToolkit_ROOT is not set, they search nvcc on PATH, CUDA_PATH, and /usr/local/cuda as before. Configuration prints the selected header directory and driver library.
• tensoracle_cubin links the internal imported target tensoracle_internal_cuda_driver publicly, so targets that link tensoracle_cubin still receive the CUDA driver include directory and library. The helpers do not define CUDA:: targets, so a project that calls find_package(CUDAToolkit) itself is unaffected.
• The documentation and the example describe cubin production and native plugin builds without an OS-installed CUDA Toolkit. The example cubin bundle is unchanged (bundle digest 207d41310c87ae8b75b0301e881df881cdd2475eec07b82092e0ec66febbd5c0).

Upgrade note

The public Python API, saved engine file format, cache identity, and runtime dependencies are unchanged from 1.2.0. Engines saved by 1.2.0 are reused without a rebuild.

Applications that build cubins with the producer extra no longer need an OS-installed CUDA Toolkit or CUDA_HOME; the extra installs the tools.

Projects that build a native plugin with the CMake helpers no longer need an OS-installed CUDA Toolkit. Pass the PyPI nvidia/cu13 directory as CUDAToolkit_ROOT, or keep passing an OS toolkit root. A project that used CUDAToolkit_INCLUDE_DIRS or CUDA:: targets only because the helper called find_package(CUDAToolkit) must now call find_package(CUDAToolkit) itself, or link tensoracle_cubin.

Windows builds and sm89 / sm120 GPUs were not exercised for this release; sm86 on WSL2 was.

Install

pip install tensoracle==1.3.0