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
Related
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
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 am running Tensorflow 1.3 with CUDA 8.0 atop of Ubuntu 16.04 successfully. The setup has been done according the official installation instructions.
1) I am wondering if Tensorflow is compatible with CUDA 9 as well. Is this supported? If so, is there a significant performance gain?
2) If only CUDA 8 is supported: is cuDNN 7.0.3 supported?
Version compatibility Tensorflow can be viewed at this link
I want to use the GPU enabled version of Tensorflow. I read that it works best with CUDA 7.5 but the server that I am using has CUDA version 5.5 installed.
Can I configure Tensorflow with CUDA 5.5? If yes, how? I have installed tensorflow in a virtual environment.
From the installation doc:
In order to build or run TensorFlow with GPU support, both NVIDIA's Cuda Toolkit (>= 7.0) and cuDNN (>= v2) need to be installed.
If you don't install from source, you will need to use version 7.5.
Also make sure that you have a compatible GPU:
TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.0. Supported cards include but are not limited to:
NVidia Titan
NVidia Titan X
NVidia K20
NVidia K40