There is no install that says cudnn for cuda 11.2 so I was wondering if that was even possible.
cuDNN Release 8.1.0 has just been made available, in case you are still interested.
https://docs.nvidia.com/deeplearning/cudnn/release-notes/rel_8.html#rel-810
Yes, you can. Follow the installation instructions as per the guide here: https://docs.nvidia.com/deeplearning/cudnn/install-guide, but ignore the version suggestion in the guide. Use any of the libcudnn versions as listed here that's compatible with CUDA v11.2: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/
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I get this error when I run the model.fit_generator code to train images using the CNN model. I don't understand the error, and what should I do? Can anyone help me?
this is the full error description
`Loaded runtime CuDNN library: 8.0.5, but the source was compiled with: 8.1.0. CuDNN library needs to have a matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, ensure the library loaded at runtime is compatible with the version specified during compile configuration.
I had the same error "tensorflow/stream_executor/cuda/cuda_dnn.cc:362] Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.1.0."
I solved it by downgrading the TensorFlow version, here it says that you use a new version of TensorFlow that is not compatible with the google colab CuDNN version. I used TensorFlow 2.4.0 plus all the dependence required on version 2.4.0.
Here it says which version of TensorFlow to use for cudnn compatibility, https://www.tensorflow.org/install/source
You should always have version of libraries installed that is matching the version dependency you want to use is compiled with.
You can download the version you need from nvidia website or use conda for package management. It will handle all dependencies for you.
You can miniconda and type conda install -c anaconda tensorflow-gpu to get it sorted for you. If you need a specific version of python, you can create environment with it.
My solution:
After confirming that my cuda and cudnn versions are compatible with tensorflow, I first thought that the system did not synchronize after the installation was completed. After several restarts, it was found that it was not and could not be the problem, so I started to check all the cuda in the system. For the software that depends on cudnn, matlab was uninstalled during the period but it was useless. Later, I thought that pytorch is also related to cuda and cudnn. I checked the version of pytorch and found that I was using torch 1.8, and the cuda it was adapted to was 11.1 , The corresponding cudnn is 8.0.5, now the case is solved. Finally upgraded pytorch and solved it.
I have faced the same issue. It seems like if TensorFlow versions requires specific cuDNN version.
Check the link for required versions.
https://www.tensorflow.org/install/source#gpu
Thanks for This answer.
My solution:
After confirming that my cuda and cudnn versions are compatible with
tensorflow, I first thought that the system ...
It helps me a lot,but I use different way to solve this problem.
I found that pytorch 1.8 is compatible with cudnn 8.1.0. So, instead of upgrade pytorch version, I overwrite the cudnn 8.0.5 dll library with cudnn 8.1.0 in directory D:\Program Files\Python37\Lib\site-packages\torch\lib. You can find this location with Everything, which is always helpful.
The requirements of the current version of tensorflow 1.11 to run on GPU are
CUDA® Toolkit —TensorFlow supports CUDA 9.0.
cuDNN SDK (>= 7.2)
However the CuDNN downlad page only lists
Download cuDNN v7.2.1 (August 7, 2018), for CUDA 9.2
Given that CuDNN comes with different binaries for minor revisions of the CUDA toolkit (e.g. CuDNN 7.1.3 has one binary for CUDA 9.1 and another for CUDA 9.0), I suppose that this binary of CuDNN 7.2 is not compatible with CUDA 9.0.
Is is a documentation bug? If not, how to fullfill the requirements of TF 1.11?
I found the below answer by modifying the addresses from the publicly available libraries: https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.2.1/prod/9.0_20180806/cudnn-9.0-windows10-x64-v7.2.1.38
As #emilyfy suggested, addresses for other, hosted but not published versions and OSs can also be acquired.
Go to this page instead. https://developer.nvidia.com/rdp/cudnn-download
It has the link for
Download cuDNN v7.3.0 (Sept 19, 2018), for CUDA 9.0
cuDNN v7.2.1 for CUDA 9.0 used to be there but now that they have v7.3.0 it's not in the archives anymore. I'm having the same problems too with a model I built on another PC. Luckily I hadn't deleted the installers. I'll share them (only the deb installers for Linux) here.
What is the reasoning that Tensorflow-gpu is bound to a specific version of Nvidia's CUDA Toolkit? The current version appears to look for 9.0 specifically and will not work with anything greater. For example I installed the latest Toolkit 9.2 and added it to path but Tensorflow-gpu will not work with it and complains that it is looking for 9.0.
I can see major version updates not being supported but a minor release?
That's a good question. According to NVidia's website,
The CUDA driver is backward compatible, meaning that applications compiled against a particular version of the CUDA will continue to work on subsequent (later) driver releases.
So technically, it should not be a problem to support later iterations of a CUDA driver. And in practice, you will find working non-official pre-built binaries with later versions of CUDA and CuDNN on the net [1], [2]. Even easier to install, the tensorflow-gpu package installed from conda currently comes bundled with CUDA 9.2.
When asked on the topic, a dev answered,
The answer to why is driver issues in the ones required by 9.1, not many new features we need in cuda 9.1, and a few more minor issues.
So the reason looks rather vague -- he might mean that CUDA 9.1 (and 9.2) requires graphics card driver that are perhaps a bit too recent to be really convenient, but that is an uneducated guess.
If NVidia is right about binary compatibility, you may try to simply rename or link your CUDA 9.2 library as a CUDA 9.0 library and it should work. But I would save all my work before attempting this... and the fact that people go as far as recompiling tensorflow to support later CUDA versions may be a hint on how this could end.
When you download TF, you download a pre-built binary file.
In the build process TF is hard linked into a specific version of Cuda, so you cannot use it with different cuda versions.
If you want to work with the new (or sometimes older) version of cuda you will need to install TF from source (check how here)
Or, if you realy don't want to build yourself, check in these repos, there are others that publish specific TF binaries, few examples:
https://github.com/mind/wheels
https://github.com/yaroslavvb/tensorflow-community-wheels
https://github.com/fo40225/tensorflow-windows-wheel
For your convenience I add here the CUDA + cuDNN versions that are required for each prebuilt Tensorflow version:
(I write here just about the TF versions that I worked with, maybe older TF versions use older versions of CUDA as well)
before TF v1.5 cuda 8.0 and cuDNN 6
start from: 1.5 - Prebuilt binaries are now built against CUDA 9 and cuDNN 7.
The issue is not with NVIDIA drivers but Tensorflow itself. I spent an hour trying to make it work, and finally realized that if you download the pre-built binary from googleapi.com, it is hard coded to load libcudart.so.9.0! If you have both cuda 9.0 and 9.2 installed, tensorflow will work (but it's actually loading the dynamic libraries from 9.0). (BTW, I installed TF using anaconda.)
A cleaner approach is to build TF from source. It's not too complicated.
I'm beginer on tensorflow and i want to install the gpu version with cuda 9.0. The problem is that my gpu (Nvidia mx150) doesn't work with cuda 9.0 (only with cuda 9.1)... So i tried to compile my own version of tensorflow with cuda 9.1. But I'm still blocked cause during the compilation the compiler find an error so it doesn't compile... I don't know why but it's very frustrating. So if you have an already compiled version of tensorflow with cuda 9.1 I'm very intrested !
thanks in advance !
I had the exact same problem a few weeks ago.
The problem is that the current version of TensorFlow (1.7) does not support CUDA9.1, please check this issue comment and the discussion below.
Here are some options that I found:
Compile TensorFlow from the source code by your own
Find an existing whl file. ex. I fixed the issue by using a whl from the repo (same as Y. Luo's answer)
Downgrade to CUDA 9.0
Wait until Tensorflow supports CUDA9.1 :)
If you don't have to use tensorflow 1.7, this repository might have what you want. Just to be clear, I never tried any of them myself.
If you need to install on Windows, this repository might be helpful.
Do we specifically need cuDNN v5.1 (as suggested) for TensorFlow, or would the latest version (v6.0) work as well? Is there backward compatibility in cuDNN versions?
No, cuDNN 6.0 is not supported in the latest 1.2 version. But there is a hope: the official release notes tell the following:
TensorFlow 1.2 may be the last time we build with cuDNN 5.1. Starting
with TensorFlow 1.3, we will try to build all our prebuilt binaries
with cuDNN 6.0. While we will try to keep our source code compatible
with cuDNN 5.1, it will be best effort.
So hopefully the next 1.3 version will use cuDNN 6.0. Especially now, when 7.0 is right around the corner.
Today ( 21 August 2017 ) I installed the latest Tensorflow release v1.3 and I can confirm that it REQUIRES cuDNN v6.0 and WILL NOT WORK with v5.1 . It will ask in fact for the library libcudnn.so.6 and not the libcudnn.so.5
P.s. If you want it to work with cuDNN 5.1. you can install a previous version e.g. v1.2 which is at this link:
https://www.tensorflow.org/versions/r0.12/get_started/os_setup#virtualenv_installation
This would be an updated answer of Salvador Dali's response.
I have upgraded the tensorflow version to 1.2.1 and then cudnn 6.0 seems to work with no problem. I used pip
pip install tensorflow
pip install --upgrade tensorflow
Installation details are here.