[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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[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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目錄
- 各個CPU版本tensorflow對應的環境要求
- 各個GPU版本tensorflow對應的CUDA版本
- 各個版本的CUDA和英偉達顯卡驅動對應表
- 缺失cudnn64_7.dll文件
- 查看本地CUDA版本
- 查看本地cudnn版本
各個CPU版本tensorflow對應的環境要求
各個CPU版本tensorflow對應的環境要求
| tensorflow-2.5.0 | 3.6-3.9 | MSVC 2019 | Bazel 3.7.2 |
| tensorflow-2.4.0 | 3.6-3.8 | MSVC 2019 | Bazel 3.1.0 |
| tensorflow-2.3.0 | 3.5-3.8 | MSVC 2019 | Bazel 3.1.0 |
| tensorflow-2.2.0 | 3.5-3.8 | MSVC 2019 | Bazel 2.0.0 |
| tensorflow-2.1.0 | 3.5-3.7 | MSVC 2019 | Bazel 0.27.1-0.29.1 |
| tensorflow-2.0.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.26.1 |
| tensorflow-1.15.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.26.1 |
| tensorflow-1.14.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.24.1-0.25.2 |
| tensorflow-1.13.0 | 3.5-3.7 | MSVC 2015 update 3 | Bazel 0.19.0-0.21.0 |
| tensorflow-1.12.0 | 3.5-3.6 | MSVC 2015 update 3 | Bazel 0.15.0 |
| tensorflow-1.11.0 | 3.5-3.6 | MSVC 2015 update 3 | Bazel 0.15.0 |
| tensorflow-1.10.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.9.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.8.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.7.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.6.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.5.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.4.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.3.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.2.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.1.0 | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 |
| tensorflow-1.0.0 | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 |
各個GPU版本tensorflow對應的CUDA版本
各個GPU版本tensorflow對應的CUDA版本
| tensorflow_gpu-2.5.0 | 3.6-3.9 | MSVC 2019 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow_gpu-2.4.0 | 3.6-3.8 | MSVC 2019 | Bazel 3.1.0 | 8.0 | 11.0 |
| tensorflow_gpu-2.3.0 | 3.5-3.8 | MSVC 2019 | Bazel 3.1.0 | 7.6 | 10.1 |
| tensorflow_gpu-2.2.0 | 3.5-3.8 | MSVC 2019 | Bazel 2.0.0 | 7.6 | 10.1 |
| tensorflow_gpu-2.1.0 | 3.5-3.7 | MSVC 2019 | Bazel 0.27.1-0.29.1 | 7.6 | 10.1 |
| tensorflow_gpu-2.0.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.26.1 | 7.4 | 10 |
| tensorflow_gpu-1.15.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.26.1 | 7.4 | 10 |
| tensorflow_gpu-1.14.0 | 3.5-3.7 | MSVC 2017 | Bazel 0.24.1-0.25.2 | 7.4 | 10 |
| tensorflow_gpu-1.13.0 | 3.5-3.7 | MSVC 2015 update 3 | Bazel 0.19.0-0.21.0 | 7.4 | 10 |
| tensorflow_gpu-1.12.0 | 3.5-3.6 | MSVC 2015 update 3 | Bazel 0.15.0 | 7.2 | 9.0 |
| tensorflow_gpu-1.11.0 | 3.5-3.6 | MSVC 2015 update 3 | Bazel 0.15.0 | 7 | 9 |
| tensorflow_gpu-1.10.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.9.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.8.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.7.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.6.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.5.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 7 | 9 |
| tensorflow_gpu-1.4.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 6 | 8 |
| tensorflow_gpu-1.3.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 6 | 8 |
| tensorflow_gpu-1.2.0 | 3.5-3.6 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
| tensorflow_gpu-1.1.0 | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
| tensorflow_gpu-1.0.0 | 3.5 | MSVC 2015 update 3 | Cmake v3.6.3 | 5.1 | 8 |
各個版本的CUDA和英偉達顯卡驅動對應表
| CUDA 11.2.1 Update 1 | >=460.32.03 | >=461.09 |
| CUDA 11.2.0 GA | >=460.27.03 | >=460.82 |
| CUDA 11.1.1 Update 1 | >=455.32 | >=456.81 |
| CUDA 11.1 GA | >=455.23 | >=456.38 |
| CUDA 11.0.3 Update 1 | >= 450.51.06 | >= 451.82 |
| CUDA 11.0.2 GA | >= 450.51.05 | >= 451.48 |
| CUDA 11.0.1 RC | >= 450.36.06 | >= 451.22 |
| CUDA 10.2.89 | >= 440.33 | >= 441.22 |
| CUDA 10.1 (10.1.105 general release, and updates) | >= 418.39 | >= 418.96 |
| CUDA 10.0.130 | >= 410.48 | >= 411.31 |
| CUDA 9.2 (9.2.148 Update 1) | >= 396.37 | >= 398.26 |
| CUDA 9.2 (9.2.88) | >= 396.26 | >= 397.44 |
| CUDA 9.1 (9.1.85) | >= 390.46 | >= 391.29 |
| CUDA 9.0 (9.0.76) | >= 384.81 | >= 385.54 |
| CUDA 8.0 (8.0.61 GA2) | >= 375.26 | >= 376.51 |
| CUDA 8.0 (8.0.44) | >= 367.48 | >= 369.30 |
| CUDA 7.5 (7.5.16) | >= 352.31 | >= 353.66 |
| CUDA 7.0 (7.0.28) | >= 346.46 | >= 347.62 |
從CUDA11開始，對工具包中的各個組件進行了獨立的版本控制。 對于CUDA11.3，下表顯示了以下版本：
| CUDA Runtime (cudart) | 11.3.109 | x86_64, POWER, Arm64 |
| cuobjdump | 11.3.58 | x86_64, POWER, Arm64 |
| CUPTI | 11.3.111 | x86_64, POWER, Arm64 |
| CUDA cuxxfilt (demangler) | 11.3.58 | x86_64, POWER, Arm64 |
| CUDA Demo Suite | 11.3.58 | x86_64 |
| CUDA GDB | 11.3.109 | x86_64, POWER, Arm64 |
| CUDA Memcheck | 11.3.109 | x86_64, POWER |
| CUDA NVCC | 11.3.109 | x86_64, POWER, Arm64 |
| CUDA nvdisasm | 11.3.58 | x86_64, POWER, Arm64 |
| CUDA NVML Headers | 11.3.58 | x86_64, POWER, Arm64 |
| CUDA nvprof | 11.3.111 | x86_64, POWER, Arm64 |
| CUDA nvprune | 11.3.58 | x86_64, POWER, Arm64 |
| CUDA NVRTC | 11.3.109 | x86_64, POWER, Arm64 |
| CUDA NVTX | 11.3.109 | x86_64, POWER, Arm64 |
| CUDA NVVP | 11.3.111 | x86_64, POWER |
| CUDA Samples | 11.3.58 | x86_64, POWER, Arm64 |
| CUDA Compute Sanitizer API | 11.3.111 | x86_64, POWER, Arm64 |
| CUDA cuBLAS | 11.5.1.109 | x86_64, POWER, Arm64 |
| CUDA cuFFT | 10.4.2.109 | x86_64, POWER, Arm64 |
| CUDA cuRAND | 10.2.4.109 | x86_64, POWER, Arm64 |
| CUDA cuSOLVER | 11.1.2.109 | x86_64, POWER, Arm64 |
| CUDA cuSPARSE | 11.6.0.109 | x86_64, POWER, Arm64 |
| CUDA NPP | 11.3.3.95 | x86_64, POWER, Arm64 |
| CUDA nvJPEG | 11.5.0.109 | x86_64, POWER, Arm64 |
| Nsight Compute | 2021.1.1.5 | x86_64, POWER, Arm64 (CLI only) |
| Nsight Windows NVTX | 1.21018621 | x86_64, POWER, Arm64 |
| Nsight Systems | 2021.1.3.14 | x86_64, POWER, Arm64 (CLI only) |
| Nsight Visual Studio Edition (VSE) | 2021.1.1.21111 | x86_64 (Windows) |
| NVIDIA Linux Driver | 465.19.01 | x86_64, POWER, Arm64 |
| NVIDIA Windows Driver | 465.89 | x86_64 (Windows) |
缺失cudnn64_7.dll文件
安裝了cudnn8.0以上版本以后，有時會出現報錯Could not load dynamic library ‘cudnn64_7.dll’; dlerror: cudnn64_7.dll not found。這是因為cudnn8.0以上缺失cudnn64_7.dll文件。
解決方法：把C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin文件夾下的‘cudnn64_8.dll’復制一份并命名為為‘cudnn64_7.dll。’
查看本地CUDA版本
參考如何查看windows的CUDA版本。按照該過程打開以后提示，顯卡未連接。這時可以通過命令行實現查看。
查看本地cudnn版本
windows中cuda的安裝路徑C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include下有cudnn_version.h文件。打開該文件：
本地cudnn版本為8.1。
總結
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