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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/cuda_cooperative/): A Python package for easy access to highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc.
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*[cuda.parallel](https://nvidia.github.io/cccl/cuda_parallel/): A Python package providing CUB's reusable block-wide and warp-wide primitives for use within Numba CUDA kernels
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*[cuda.cooperative](https://nvidia.github.io/cccl/cuda_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/cuda_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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* CUDA Runtime
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* NVRTC
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* nvJitLink
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## Supported Python Versions
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All `cuda-python` subpackages follows CPython [End-Of-Life](https://devguide.python.org/versions/) schedule for supported Python version guarantee.
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Before dropping support there will be an issue raised as a notice.
`cuda.bindings` is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. Checkout the `Overview <https://nvidia.github.io/cuda-python/cuda-bindings/latest/overview.html>`_ for the workflow and performance results.
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`cuda.bindings` is a standard set of low-level interfaces, providing full coverage of and 1:1 access to the CUDA host APIs from Python. Checkout the `Overview <https://nvidia.github.io/cuda-python/cuda-bindings/latest/overview.html>`_ for the workflow and performance results.
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Installation
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============
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`cuda.bindings` can be installed from:
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* PyPI
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* Conda (conda-forge/nvidia channels)
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* Source builds
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Differences between these options are described in `Installation <https://nvidia.github.io/cuda-python/cuda-bindings/latest/install.html>`_ documentation. Each package guarantees minor version compatibility.
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Runtime Dependencies
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====================
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`cuda.bindings` is supported on all the same platforms as CUDA. Specific dependencies are as follows:
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* Driver: Linux (450.80.02 or later) Windows (456.38 or later)
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* CUDA Toolkit 12.x
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Only the NVRTC and nvJitLink redistributable components are required from the CUDA Toolkit, which can be obtained via PyPI, Conda, or local installers (as described in the CUDA Toolkit `Windows <https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html>`_ and `Linux <https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>`_ Installation Guides).
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For the installation instruction, please refer to the `Installation <https://nvidia.github.io/cuda-python/cuda-bindings/latest/install.html>`_ page.
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# `cuda.bindings`: Low-level CUDA interfaces
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`cuda.bindings` is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. Checkout the [Overview](https://nvidia.github.io/cuda-python/cuda-bindings/latest/overview.html) for the workflow and performance results.
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`cuda.bindings` is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. Checkout the [Overview page](https://nvidia.github.io/cuda-python/cuda-bindings/latest/overview.html) for the workflow and performance results.
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`cuda.bindings` is a subpackage of `cuda-python`.
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## Installing
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`cuda.bindings` can be installed from:
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* PyPI
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* Conda (conda-forge/nvidia channels)
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* Source builds
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Differences between these options are described in [Installation](https://nvidia.github.io/cuda-python/cuda-bindings/latest/install.html) documentation. Each package guarantees minor version compatibility.
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## Runtime Dependencies
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`cuda.bindings` is supported on all the same platforms as CUDA. Specific dependencies are as follows:
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* Driver: Linux (450.80.02 or later) Windows (456.38 or later)
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* CUDA Toolkit 12.x
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Only the NVRTC and nvJitLink redistributable components are required from the CUDA Toolkit, which can be obtained via PyPI, Conda, or local installers (as described in the CUDA Toolkit [Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) and [Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) Installation Guides).
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Please refer to the [Installation page](https://nvidia.github.io/cuda-python/cuda-bindings/latest/install.html) for instructions and required/optional dependencies.
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`cuda.bindings` supports the same platforms as CUDA. Runtime dependencies are:
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* Linux (x86-64, arm64) and Windows (x86-64)
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* Python 3.9 - 3.13
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* Driver: Linux (450.80.02 or later) Windows (456.38 or later)
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* CUDA Toolkit 12.x
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*Optionally, NVRTC, nvJitLink, and NVVM from CUDA Toolkit 12.x
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```{note}
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Only the NVRTC and nvJitLink redistributable components are required from the CUDA Toolkit, which can be obtained via PyPI, Conda, or local installers (as described in the CUDA Toolkit [Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) and [Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) Installation Guides).
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The optional CUDA Toolkit components can be installed via PyPI, Conda, OS-specific package managers, or local installers (as described in the CUDA Toolkit [Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) and [Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) Installation Guides).
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```
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Starting from v12.8.0, `cuda-python` becomes a meta package which currently depends only on `cuda-bindings`; in the future more sub-packages will be added to `cuda-python`. In the instructions below, we still use `cuda-python` as example to serve existing users, but everything is applicable to `cuda-bindings` as well.
[^1]: User projects that `cimport` CUDA symbols in Cython must also use CUDA Toolkit (CTK) types as provided by the `cuda.bindings` major.minor version. This results in CTK headers becoming a transitive dependency of downstream projects through CUDA Python.
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[^2]: The CUDA Runtime static library (`libcudart_static.a` on Linux, `cudart_static.lib` on Windows) is part of the CUDA Toolkit. If using conda packages, it is contained in the `cuda-cudart-static` package.
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Source builds require that the provided CUDA headers are of the same major.minor version as the `cuda.bindings` you're trying to build. Despite this requirement, note that the minor version compatibility is still maintained. Use the `CUDA_HOME` (or `CUDA_PATH`) environment variable to specify the location of your headers. For example, if your headers are located in `/usr/local/cuda/include`, then you should set `CUDA_HOME` with:
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