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Copy file name to clipboardExpand all lines: README.md
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@@ -4,7 +4,7 @@ CUDA Python is the home for accessing NVIDIA’s CUDA platform from Python. It c
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*[cuda.core](https://nvidia.github.io/cuda-python/cuda-core/latest): Pythonic access to CUDA Runtime and other core functionalities
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*[cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest): Low-level Python bindings to CUDA C APIs
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* cuda.pathfinder: Utilities for locating CUDA components installed in the user's Python virtual environment
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*[cuda.pathfinder](https://github.com/NVIDIA/cuda-python/blob/main/cuda_pathfinder/cuda/pathfinder/README.md): Utilities for locating CUDA components installed in the user's Python environment
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*[cuda.cccl.cooperative](https://nvidia.github.io/cccl/python/cooperative): A Python module providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
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*[cuda.cccl.parallel](https://nvidia.github.io/cccl/python/parallel): A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc. that are callable on the *host*
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*[numba.cuda](https://nvidia.github.io/numba-cuda/): Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
Copy file name to clipboardExpand all lines: cuda_python/docs/source/index.rst
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- `cuda.core`_: Pythonic access to CUDA runtime and other core functionalities
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- `cuda.bindings`_: Low-level Python bindings to CUDA C APIs
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- `cuda.pathfinder`: Utilities for locating CUDA components installed in the user's Python virtual environment
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- `cuda.pathfinder_`: Utilities for locating CUDA components installed in the user's Python environment
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- `cuda.cccl.cooperative`_: A Python module providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
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- `cuda.cccl.parallel`_: A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like ``sort``, ``scan``, ``reduce``, ``transform``, etc, that are callable on the *host*
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- `numba.cuda`_: Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
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* `nvmath-python`_: Pythonic access to NVIDIA CPU & GPU Math Libraries, with both *host* and *device* (through `nvmath.device`_) APIs. It also provides low-level Python bindings to host C APIs (through `nvmath.bindings`_).
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