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NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (“Terms of Sale”). NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.Ĭustomer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. Since cuDNN is split into several libraries, dependencies between them need to be taken into account.įor example, when statically linking libcudnn_cnn_infer_static.a into an application, libcudnn_ops_infer_static.a is also needed, in this order (a dependent library followed by its dependency).

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Static cuDNN libs for Windows are not supported. Linux: Add -lcublas_static -lcublasLt_static -lz -lculibos -lnvrtc_static -lnvrtc-builtins_static -lnvptxcompiler_static -lcudart_static to the linker command. Linker dependencies for the static cuDNN libs Windows: Add cublas.lib cublasLt.lib zlibwapi.lib to the linker command. Linux: Add -lcublas -lcublasLt -lz to the linker command. One way to achieve this is by explicitly specifying them on the linker command.įor linker dependencies for the dynamic cuDNN libs Navigate to your directory containing the cuDNN tar file.īecause cuDNN uses symbols defined in external libraries, you need to ensure that the linker can locate these libraries while building a cuDNN dependent program.

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your cuDNN download path is referred to as īefore issuing the following commands, you must replace X.Y and v8.x.x.x with your specific CUDA and cuDNN versions and package date.

your CUDA directory path is referred to as /usr/local/cuda/.

The RPM package installation applies to RHEL7, RHEL8, and RHEL9. The Debian package installation applies to Debian 11, Ubuntu 18.04, Ubuntu 20.04, and 22.04. For example, the tar file installation applies to all Linux platforms. Choose the installation method that meets your environment needs.

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The following steps describe how to build a cuDNN dependent program. Select the cuDNN version that you want to install.A list of available download versions of cuDNN displays.

Complete the short survey and click Submit.

In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

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