I have NVIDIA A100-sxm4-40 with CUDA 11.0 and Cudnn 8.0.
Which TensorFlow version can work with these specs??
I need the latest appropriate version
Related
I have seen the list of tested TF version and it's CUDA version compatibility Here, but I am having a doubt whether every TF version can be build from source with any CUDA version or not. For example:- can TF 1.14 build with CUDA 11.1 or not?
No, T.F. 1x is not compatible with CUDA 11.1. For CUDA 11.1 you need to go for T.F. 2.3 or higher.
If you wish to use T.F. 1.x then the best will be to use CUDA 10.0-10.2
I created a virtual environment and installed TensorFlow 2.3 without anaconda, like this:
And I installed NVIDIA driver, CUDA, cudnn
But GPU still doesn't work in tf....
I do not know what mistake I have done.
change CUDA 11.0 to CUDA 10.1
and cudnn to 7.6
In my laptop there are three versions of cuda, 8.0, 9.0 and 10.0 installed, all of which are configured in the environment path. When I use tensorflow-gpu 2.0.0, how to know which version of cuda is to be deployed, without considering that the present version of tensorflow is only compatible with cuda 10.0. Is there any way to print the information on python console?
I found answers here get the CUDA and CUDNN version on windows with Anaconda installe:
from tensorflow.python.platform import build_info as tf_build_info
print(tf_build_info.cuda_version_number)
#10.0
print(tf_build_info.cudnn_version_number)
#7
I am currently working as a working student and now I have trouble installing Tensorflow-gpu on a machine using a Nvidia Quadro GV100 GPU.
On the Tensorflow homepage I found out that I need to install CUDA 9.0 and Cudnn 7.x in order to run Tensorflow-gpu 1.9. The problem is that I can't find a suitable CUDA version supporting the GV100. Could it be that there is no CUDA version yet? Is it possible that one can't use the GV100 for tensoflow-gpu?
Sorry for the stupid question, I am new to installing DL frameworks :-)
Thank you very much for your help!
On the Tensorflow homepage I found out that I need to install CUDA 9.0 and Cudnn 7.x in order to run Tensorflow-gpu 1.9.
That is if you want to install a pre-built Tensorflow binary distribution. In that case you need to use the version of CUDA which the Tensorflow binaries were built against, which in this case in CUDA 9.0
The problem is that I can't find a suitable CUDA version supporting the GV100
The CUDA 9.0 and later toolkits fully support Volta cards and that should include the Quadro GV100. The driver which ships with CUDA 9.0 is a 384 series which won't support your GPU. If you are referring to a driver support issue, then the solution would be to install the recommended driver for your GPU, and only install the CUDA toolkit from the CUDA 9.0 bundle, not the toolkit and driver, which is the default.
Otherwise you can use CUDA 9.1 or 9.2, which should have support for your GPU with their supplied drivers, but you will then need to build Tensorflow yourself from source.
I have a machine with cuda 9.0 and cudnn 7.1.
I've tried using tensorflow 1.7.0 on this machine but it does not work since this version of tensorflow has been created for cudnn 7.0
I'm getting this error when launching a training on my gpu:
Loaded runtime CuDNN library: 7102 (compatibility version 7100) but source was compiled with 7005 (compatibility version 7000).
Is there a tensorflow version that is compatible with my cuda and cudnn versions? I also need this working tensorflow version to be >=1.7.0.
I have googled this, searched every question but I never got answers for these particular versions of cuda and cudnn.
This should be possible with tensorflow_gpu-1.9.0. Linked below is a table which displays compatibilities of CUDA and cuDNN with varying versions of tensorflow.
https://www.tensorflow.org/install/install_sources#tested_source_configurations
Ok, seems I missed some installation steps.
By installing the last version of tensorflow, which at the time of writing is 1.9.0, it did work on my machine.