Which TensorFlow and CUDA version combinations are compatible?

问题

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.

回答1:

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.

(figures updated Jun 29, 2019)

Linux GPU

Linux

macOS GPU

macOS

(figure updated May 31, 2018)

Windows

回答2:

The compatibility table given in https://www.tensorflow.org/install/source#tested_build_configurations does not contain specific minor versions for cuda and cuDNN. It is only generally listed as cuda=9 and cuDNN=7. However, if the specific versions are not met, there will be an error.

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

回答3:

Working : tensorflow 1.13.1, CUDA 10, CUDNN 7.4.2, python 3.6 (does not work well with 3.7.. 3.7 has many bugs) For Windows 10

回答4:

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

回答5:

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/

来源：https://stackoverflow.com/questions/50622525/which-tensorflow-and-cuda-version-combinations-are-compatible