I am planning to deploy a model of TensorFlow 1.10.0 using TensorRT. But the version of TensorFlow is too early, so TensorRT 5.0.0Rc is required. Because the version is too early, NVIDIA's official website and GitHub do not provide download sources. Is there any other way to download it? Or is there any other way to deploy TensorFlow 1.10.0? Thank you.
I'm using Colab, and I'm getting the error when I'm training with Tensorflow 2 Object Detection API. Tensorflow version is 2.6.2 and library is downloaded from official repo. This issue just appeared overnight, same code was working until the day before, which makes me think some internal changes happened, but I don't know how to fix this. I'd appreciate any help. Thanks
There are 2 solutions I found:
Downgrade TF to match the CUDA and CUDAnn
Upgrade the CUDAnn
Please check this related GitHub issue.
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.
I'm trying to run a program in my Raspberry but i can't because it needs at least TensorFlow 2.2.0, while I have TensorFlow 2.0.0 . I tried several times to install TensorFlow 2.2.0 and 2.3.0 . But after install it, it always comes that is 2.0.0 still.
Versions of TensorFlow
Somebody can tell me what happens? Thank you!!
Try to find the package in the /python3.x/site-packages and remove the tensorflow directory using rm.
Then install the needed tensorflow version following the installation instruction found here in the official documentation of tensorflow.
Also attaching the image from the comment of #pablo Gracia S.
I am a university professor trying to learn deep learning for a possible class in the future. I have been using google colab with GPU support for the past couple of months. Just recently, the GPU device is not found. But, I am doing everything that I have done in the past. I can't imagine that I have done anything wrong because I am just working through tutorials from books and the tensorflow 2.0 tutorials site.
tensorflow 2 on Colab GPU was broken recently due to an upgrade from CUDA 10.0 to CUDA 10.1. As of this afternoon, the issue should be resolved for the tensorflow builds bundled with Colab. That is, if you run the following magic command:
%tensorflow_version 2.x
then import tensorflow will import a working, GPU-compatible tensorflow 2.0 version.
Note, however, if you attempt to install a version of tensorflow using pip install tensorflow-gpu or similar, the result may not work in Colab due to system incompatibilities.
See https://colab.research.google.com/notebooks/tensorflow_version.ipynb for more information.