ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory

ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory

When trying to import tensorflow-gpu, I was getting error:  ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory .

 

Diagnosis:

This error is due to the version of tensorflow-gpu installed that is built with a different version of cuda and cudnn and that compatible version of CUDA and CUDNN is not installed in your machine.

The tensorflow website has given a chart mentioning compatible versions of CUDA and CUDNN with tensorflow-gpu.

Hence when installing tensorflow-gpu, first step is to check the GPU model that is installed in your computer and then to check which latest version of CUDA and CUDNN is compatible with your hardware GPU.

How to check Cuda Version compatible with installed GPU

 

Once, you know the CUDA version and CUDNN version, you can choose which version of tensorflow-gpu is compatible with your hardware.

The tensorflow-gpu compatibility with different versions of CUDA and CUDNN are given as:

Linux

Version Python version Compiler Build tools cuDNN CUDA
tensorflow_gpu-1.13.1 2.7, 3.3-3.6 GCC 4.8 Bazel 0.19.2 7.4 10.0
tensorflow_gpu-1.12.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.11.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.10.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.9.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.11.0 7 9
tensorflow_gpu-1.8.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.10.0 7 9
tensorflow_gpu-1.7.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.9.0 7 9
tensorflow_gpu-1.6.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.9.0 7 9
tensorflow_gpu-1.5.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.8.0 7 9
tensorflow_gpu-1.4.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.5.4 6 8
tensorflow_gpu-1.3.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.5 6 8
tensorflow_gpu-1.2.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.5 5.1 8
tensorflow_gpu-1.1.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.2 5.1 8
tensorflow_gpu-1.0.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.2 5.1 8

Updated: 12-18-2018 [source]

Windows:

Version Python version Compiler Build tools cuDNN CUDA
tensorflow_gpu-1.13.0 3.5-3.6 MSVC 2015 update 3 Bazel 0.15.0 7 9
tensorflow_gpu-1.12.0 3.5-3.6 MSVC 2015 update 3 Bazel 0.15.0 7 9
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

 

MacOS:

Version Python version Compiler Build tools cuDNN CUDA
tensorflow_gpu-1.1.0 2.7, 3.3-3.6 Clang from xcode Bazel 0.4.2 5.1 8
tensorflow_gpu-1.0.0 2.7, 3.3-3.6 Clang from xcode Bazel 0.4.2 5.1 8

 

If you have installed CUDA 8 and CUDNN 5.1, then the table above for Linux shows: tensorflow_gpu-1.2

 

To install tensorflow-gpu 1.2:

 

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