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Add pygrep-hooks in .pre-commit-config.yaml to catch common mistakes in .rst files (#899)
* Copy pygrep-hooks from pybind11/.pre-commit-config.yaml
* cleanup stray whitespace
* Clean up cuda_core/DESCRIPTION.rst (piggy-back minor grammar fix)
* Clean up cuda_python/DESCRIPTION.rst
* Clean up cuda_core/docs/source/interoperability.rst
* Clean up cuda_core/docs/source/release/*-notes.rst
`cuda.core` is currently under active development. Any feedbacks or suggestions are welcomed!
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``cuda.core`` is currently under active development. Feedback and suggestions are welcome!
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Installation
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pip install cuda-core[cu12]
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Please refer to the `installation instructions <https://nvidia.github.io/cuda-python/cuda-core/latest/install.html>`_ for different ways of installing `cuda.core`, including building from source.
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Please refer to the `installation instructions <https://nvidia.github.io/cuda-python/cuda-core/latest/install.html>`_ for different ways of installing ``cuda.core``, including building from source.
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@@ -32,7 +32,7 @@ New features
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- :class:`~_module.Kernel` adds :attr:`~_module.Kernel.num_arguments` and :attr:`~_module.Kernel.arguments_info` for introspection of kernel arguments. (#612)
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- Add pythonic access to kernel occupancy calculation functions via :attr:`Kernel.occupancy`. (#648)
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- Support launching cooperative kernels by setting :attr:`LaunchConfig.cooperative_launch` to `True`.
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- Support launching cooperative kernels by setting :attr:`LaunchConfig.cooperative_launch` to ``True``.
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- A name can be assigned to :class:`ObjectCode` instances generated by both :class:`Program` and :class:`Linker` through their respective options.
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- Expose :class:`Buffer`, :class:`DeviceMemoryResource`, :class:`LegacyPinnedMemoryResource`, and :class:`MemoryResource` to the top namespace.
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- Before this release, the internal :class:`Buffer` class had an ``__init__()`` constructor. To align with the design of cuda.core objects,
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----------------
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- **LaunchConfig grid parameter interpretation**: When :attr:`LaunchConfig.cluster` is specified, the :attr:`LaunchConfig.grid` parameter now correctly represents the number of clusters instead of blocks. Previously, the grid parameter was incorrectly interpreted as blocks, causing a mismatch with the expected C++ behavior. This change ensures that ``LaunchConfig(grid=4, cluster=2, block=32)`` correctly produces 4 clusters × 2 blocks/cluster = 8 total blocks, matching the C++ equivalent ``cudax::make_hierarchy(cudax::grid_dims(4), cudax::cluster_dims(2), cudax::block_dims(32))``.
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- When :class:`Buffer` is closed, :attr:`Buffer.handle` is now set to `None`. It was previously set to ``0`` by accident.
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- When :class:`Buffer` is closed, :attr:`Buffer.handle` is now set to ``None``. It was previously set to ``0`` by accident.
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@@ -10,22 +10,22 @@ CUDA Python is the home for accessing NVIDIA's CUDA platform from Python. It con
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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.cooperative <https://nvidia.github.io/cccl/python/cooperative>`_: A Python package providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
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* `cuda.parallel <https://nvidia.github.io/cccl/python/parallel>`_: A Python package 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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* `cuda.parallel <https://nvidia.github.io/cccl/python/parallel>`_: A Python package 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.
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For access to NVIDIA CPU & GPU Math Libraries, please refer to `nvmath-python <https://docs.nvidia.com/cuda/nvmath-python/latest>`_.
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CUDA Python is currently undergoing an overhaul to improve existing and bring up new components. All of the previously available functionalities from the `cuda-python` package will continue to be available, please refer to the `cuda.bindings <https://nvidia.github.io/cuda-python/cuda-bindings/latest>`_ documentation for installation guide and further detail.
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CUDA Python is currently undergoing an overhaul to improve existing and bring up new components. All of the previously available functionalities from the ``cuda-python`` package will continue to be available, please refer to the `cuda.bindings <https://nvidia.github.io/cuda-python/cuda-bindings/latest>`_ documentation for installation guide and further detail.
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cuda-python as a metapackage
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============================
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`cuda-python` is now a metapackage that contains a collection of subpackages. Each subpackage is versioned independently, allowing installation of each component as needed.
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``cuda-python`` is now a metapackage that contains a collection of subpackages. Each subpackage is versioned independently, allowing installation of each component as needed.
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Subpackage: cuda.core
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---------------------
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The `cuda.core` package offers idiomatic, pythonic access to CUDA Runtime and other functionalities.
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The ``cuda.core`` package offers idiomatic, pythonic access to CUDA Runtime and other functionalities.
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The goals are to
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Subpackage: cuda.bindings
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-------------------------
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The `cuda.bindings` package is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python.
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The ``cuda.bindings`` package is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python.
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