[tensorflow]各个tensorflow版本和CUDA版本对应,以及各个GPU版本CUDA和cuDNN对应

目录

各个CPU版本tensorflow对应的环境要求

各个CPU版本tensorflow对应的环境要求

VersionPython versionCompilerBuild toolstensorflow-2.5.03.6-3.9MSVC 2019Bazel 3.7.2tensorflow-2.4.03.6-3.8MSVC 2019Bazel 3.1.0tensorflow-2.3.03.5-3.8MSVC 2019Bazel 3.1.0tensorflow-2.2.03.5-3.8MSVC 2019Bazel 2.0.0tensorflow-2.1.03.5-3.7MSVC 2019Bazel 0.27.1-0.29.1tensorflow-2.0.03.5-3.7MSVC 2017Bazel 0.26.1tensorflow-1.15.03.5-3.7MSVC 2017Bazel 0.26.1tensorflow-1.14.03.5-3.7MSVC 2017Bazel 0.24.1-0.25.2tensorflow-1.13.03.5-3.7MSVC 2015 update 3Bazel 0.19.0-0.21.0tensorflow-1.12.03.5-3.6MSVC 2015 update 3Bazel 0.15.0tensorflow-1.11.03.5-3.6MSVC 2015 update 3Bazel 0.15.0tensorflow-1.10.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.9.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.8.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.7.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.6.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.5.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.4.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.3.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.2.03.5-3.6MSVC 2015 update 3Cmake v3.6.3tensorflow-1.1.03.5MSVC 2015 update 3Cmake v3.6.3tensorflow-1.0.03.5MSVC 2015 update 3Cmake v3.6.3

各个GPU版本tensorflow对应的CUDA版本

各个GPU版本tensorflow对应的CUDA版本

VersionPython versionCompilerBuild toolscuDNNCUDAtensorflow_gpu-2.5.03.6-3.9MSVC 2019Bazel 3.7.28.111.2tensorflow_gpu-2.4.03.6-3.8MSVC 2019Bazel 3.1.08.011.0tensorflow_gpu-2.3.03.5-3.8MSVC 2019Bazel 3.1.07.610.1tensorflow_gpu-2.2.03.5-3.8MSVC 2019Bazel 2.0.07.610.1tensorflow_gpu-2.1.03.5-3.7MSVC 2019Bazel 0.27.1-0.29.17.610.1tensorflow_gpu-2.0.03.5-3.7MSVC 2017Bazel 0.26.17.410tensorflow_gpu-1.15.03.5-3.7MSVC 2017Bazel 0.26.17.410tensorflow_gpu-1.14.03.5-3.7MSVC 2017Bazel 0.24.1-0.25.27.410tensorflow_gpu-1.13.03.5-3.7MSVC 2015 update 3Bazel 0.19.0-0.21.07.410tensorflow_gpu-1.12.03.5-3.6MSVC 2015 update 3Bazel 0.15.07.29.0tensorflow_gpu-1.11.03.5-3.6MSVC 2015 update 3Bazel 0.15.079tensorflow_gpu-1.10.03.5-3.6MSVC 2015 update 3Cmake v3.6.379tensorflow_gpu-1.9.03.5-3.6MSVC 2015 update 3Cmake v3.6.379tensorflow_gpu-1.8.03.5-3.6MSVC 2015 update 3Cmake v3.6.379tensorflow_gpu-1.7.03.5-3.6MSVC 2015 update 3Cmake v3.6.379tensorflow_gpu-1.6.03.5-3.6MSVC 2015 update 3Cmake v3.6.379tensorflow_gpu-1.5.03.5-3.6MSVC 2015 update 3Cmake v3.6.379tensorflow_gpu-1.4.03.5-3.6MSVC 2015 update 3Cmake v3.6.368tensorflow_gpu-1.3.03.5-3.6MSVC 2015 update 3Cmake v3.6.368tensorflow_gpu-1.2.03.5-3.6MSVC 2015 update 3Cmake v3.6.35.18tensorflow_gpu-1.1.03.5MSVC 2015 update 3Cmake v3.6.35.18tensorflow_gpu-1.0.03.5MSVC 2015 update 3Cmake v3.6.35.18

各个版本的CUDA和英伟达显卡驱动对应表

CUDA ToolkitLinux x86_64 Driver VersionWindows x86_64 Driver VersionCUDA 11.2.1 Update 1>=460.32.03>=461.09CUDA 11.2.0 GA>=460.27.03>=460.82CUDA 11.1.1 Update 1>=455.32>=456.81CUDA 11.1 GA>=455.23>=456.38CUDA 11.0.3 Update 1>= 450.51.06>= 451.82CUDA 11.0.2 GA>= 450.51.05>= 451.48CUDA 11.0.1 RC>= 450.36.06>= 451.22CUDA 10.2.89>= 440.33>= 441.22CUDA 10.1 (10.1.105 general release, and updates)>= 418.39>= 418.96CUDA 10.0.130>= 410.48>= 411.31CUDA 9.2 (9.2.148 Update 1)>= 396.37>= 398.26CUDA 9.2 (9.2.88)>= 396.26>= 397.44CUDA 9.1 (9.1.85)>= 390.46>= 391.29CUDA 9.0 (9.0.76)>= 384.81>= 385.54CUDA 8.0 (8.0.61 GA2)>= 375.26>= 376.51CUDA 8.0 (8.0.44)>= 367.48>= 369.30CUDA 7.5 (7.5.16)>= 352.31>= 353.66CUDA 7.0 (7.0.28)>= 346.46>= 347.62

从CUDA11开始,对工具包中的各个组件进行了独立的版本控制。 对于CUDA11.3,下表显示了以下版本:

Component NameVersion InformationSupported ArchitecturesCUDA Runtime (cudart)11.3.109x86_64, POWER, Arm64cuobjdump11.3.58x86_64, POWER, Arm64CUPTI11.3.111x86_64, POWER, Arm64CUDA cuxxfilt (demangler)11.3.58x86_64, POWER, Arm64CUDA Demo Suite11.3.58x86_64CUDA GDB11.3.109x86_64, POWER, Arm64CUDA Memcheck11.3.109x86_64, POWERCUDA NVCC11.3.109x86_64, POWER, Arm64CUDA nvdisasm11.3.58x86_64, POWER, Arm64CUDA NVML Headers11.3.58x86_64, POWER, Arm64CUDA nvprof11.3.111x86_64, POWER, Arm64CUDA nvprune11.3.58x86_64, POWER, Arm64CUDA NVRTC11.3.109x86_64, POWER, Arm64CUDA NVTX11.3.109x86_64, POWER, Arm64CUDA NVVP11.3.111x86_64, POWERCUDA Samples11.3.58x86_64, POWER, Arm64CUDA Compute Sanitizer API11.3.111x86_64, POWER, Arm64CUDA cuBLAS11.5.1.109x86_64, POWER, Arm64CUDA cuFFT10.4.2.109x86_64, POWER, Arm64CUDA cuRAND10.2.4.109x86_64, POWER, Arm64CUDA cuSOLVER11.1.2.109x86_64, POWER, Arm64CUDA cuSPARSE11.6.0.109x86_64, POWER, Arm64CUDA NPP11.3.3.95x86_64, POWER, Arm64CUDA nvJPEG11.5.0.109x86_64, POWER, Arm64Nsight Compute2021.1.1.5x86_64, POWER, Arm64 (CLI only)Nsight Windows NVTX1.21018621x86_64, POWER, Arm64Nsight Systems2021.1.3.14x86_64, POWER, Arm64 (CLI only)Nsight Visual Studio Edition (VSE)2021.1.1.21111x86_64 (Windows)NVIDIA Linux Driver465.19.01x86_64, POWER, Arm64NVIDIA Windows Driver465.89x86_64 (Windows)

缺失cudnn64_7.dll文件

安装了cudnn8.0以上版本以后,有时会出现报错 Could not load dynamic library ‘cudnn64_7.dll’; dlerror: cudnn64_7.dll not found。这是因为cudnn8.0以上缺失 cudnn64_7.dll文件。
解决方法:把C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin文件夹下的’ cudnn64_8.dll‘复制一份并命名为为’ cudnn64_7.dll。’

查看本地CUDA版本

  1. 通过控制面板来查看。
    参考如何查看windows的CUDA版本。按照该过程打开以后提示,显卡未连接。这时可以通过命令行实现查看。
  2. cmd中,输入nvcc -V(注意V是大写的)。
    [tensorflow]各个tensorflow版本和CUDA版本对应,以及各个GPU版本CUDA和cuDNN对应

; 查看本地cudnn版本

windows中cuda的安装路径 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include下有 cudnn_version.h文件。打开该文件:

[tensorflow]各个tensorflow版本和CUDA版本对应,以及各个GPU版本CUDA和cuDNN对应
本地cudnn版本为8.1。

Original: https://blog.csdn.net/weixin_44560088/article/details/117457619
Author: 帅兄心安否
Title: [tensorflow]各个tensorflow版本和CUDA版本对应,以及各个GPU版本CUDA和cuDNN对应

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