Which TensorFlow and CUDA version combinations are compatible

Question

I have noticed that some newer TensorFlow versions are incompatible with older CUDA and cuDNN versions  Does an overview of the compatible versions or even a list of officially tested combinations exist  I can t find it in the TensorFlow documentation

User · Answer

You can use this configuration for cuda 10.0 (10.1 does not work as of 3/18), this runs for me:

tensorflow>=1.12.0
tensorflow_gpu>=1.4

Install version tensorflow gpu:

pip install tensorflow-gpu==1.4.0

User · Answer

The compatibility table given in the tensorflow site does not contain specific minor versions for cuda and cuDNN  However  if the specific versions are not met  there will be an error when you try to use tensorflow   For tensorflow-gpu  1 12 0 and cuda  9 0  the compatible cuDNN version is 7 1 4  which can be downloaded from here after registration   You can check your cuda version using  nvcc --version   cuDNN version using cat  usr include cudnn h   grep CUDNN MAJOR -A 2  tensorflow-gpu version using pip freeze   grep tensorflow-gpu  UPDATE  Since tensorflow 2 0  has been released  I will share the compatible cuda and cuDNN versions for it as well  for Ubuntu 18 04     tensorflow-gpu   2 0 0 cuda   10 0 cuDNN   7 6 0

User · Answer

I had a similar problem after upgrading to TF 2 0  The CUDA version that TF was reporting did not match what Ubuntu 18 04 thought I had installed   It said I was using CUDA 7 5 0  but apt thought I had the right version installed  What I eventually had to do was grep recursively in  usr local for CUDNN MAJOR  and I found that  usr local cuda-10 0 targets x86 64-linux include cudnn h did indeed specify the version as 7 5 0   usr local cuda-10 1 got it right  and  usr local cuda pointed to  usr local cuda-10 1  so it was  and remains  a mystery to me why TF was looking at  usr local cuda-10 0  Anyway  I just moved  usr local cuda-10 0 to  usr local old-cuda-10 0 so TF couldn t find it any more and everything then worked like a charm  It was all very frustrating  and I still feel like I just did a random hack   But it worked    and perhaps this will help someone with a similar issue

User · Answer

if you are coding in jupyter notebook  and want to check which cuda version tf is using  run the follow command directly into jupyter cell    conda list cudatoolkit   conda list cudnn   and to check if the gpu is visible to tf   tf test is gpu available      cuda only False  min cuda compute capability None

User · Answer

TL DR  See this table  https   www tensorflow org install source gpu Generally  Check the CUDA version  cat  usr local cuda version txt  and cuDNN version  grep CUDNN MAJOR -A 2  usr local cuda include cudnn h  and install a combination as given below in the images or here  The following images and the link provide an overview of the officially supported tested combinations of CUDA and TensorFlow on Linux  macOS and Windows  Minor configurations  Since the given specifications below in some cases might be too broad  here is one specific configuration that works   tensorflow-gpu  1 12 0 cuda  9 0 cuDNN  7 1 4  The corresponding cudnn can be downloaded here  Tested build configurations Please refer to https   www tensorflow org install source gpu for a up-to-date compatibility chart  for official TF wheels    figures updated May 20  2020  Linux GPU  Linux CPU  macOS GPU  macOS CPU  Windows GPU  Windows CPU  Updated as of Dec 5 2020  For the updated information please refer Link for Linux and Link for Windows

User · Answer

I had installed CUDA 10 1 and CUDNN 7 6 by mistake  You can use following configurations  This worked for me - as of 9 10       Tensorflow-gpu    1 14 0 CUDA 10 1 CUDNN 7 6 Ubuntu 18 04   But I had to create symlinks for it to work as tensorflow originally works with CUDA 10    sudo ln -s  opt cuda targets x86 64-linux lib libcublas so  opt cuda targets x86 64-linux lib libcublas so 10 0 sudo cp  usr lib x86 64-linux-gnu libcublas so 10  usr local cuda-10 1 lib64  sudo ln -s  usr local cuda-10 1 lib64 libcublas so 10  usr local cuda-10 1 lib64 libcublas so 10 0 sudo ln -s  usr local cuda targets x86 64-linux lib libcusolver so 10  usr local cuda lib64 libcusolver so 10 0 sudo ln -s  usr local cuda targets x86 64-linux lib libcurand so 10  usr local cuda lib64 libcurand so 10 0 sudo ln -s  usr local cuda targets x86 64-linux lib libcufft so 10  usr local cuda lib64 libcufft so 10 0 sudo ln -s  usr local cuda targets x86 64-linux lib libcudart so  usr local cuda lib64 libcudart so 10 0 sudo ln -s  usr local cuda targets x86 64-linux lib libcusparse so 10  usr local cuda lib64 libcusparse so 10 0   And add the following to my    bashrc -  export PATH  usr local cuda bin  PATH export LD LIBRARY PATH  usr local cuda lib64  LD LIBRARY PATH export PATH  usr local cuda-10 1 bin  PATH export LD LIBRARY PATH  usr local cuda-10 1 lib64  LD LIBRARY PATH export LD LIBRARY PATH  LD LIBRARY PATH  opt cuda targets x86 64-linux lib

[tensorflow] Which TensorFlow and CUDA version combinations are compatible?

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