I have installed checked and installed CUDA-v11.0 and the appropriate version of cudnn too!
when I just run cudart64_110.dll anywhere for my cmd
I can that the file is indeed getting recognized
but, when I try to run TensorFlow, the system is not able to recognize cudart64_110.dll
enter image description here
Im trying all this on a AMD Ryzen 7, RTX 2060 laptop(ASUS TUF fx505DV)
Related
I recently installed Tensorflow-gpu and 2 errors popped up.
Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Thing is I want to use my GPU so I am not sure if this will affect it.
I installed cudnn yet still nothing. I installed VS code 2017 and installed c++ with it yet I still get this error. I am using pycharm as my interpreter.
I am using a package that requires tensorflow-gpu == 2.0.0 and CUDA=10.0.0 with cudann==7.6.0
I am running this code on a NVIDIA gpu cluster and when I run nvidia-smi it shows
this. It still shows cuda 11, which I guess is the one installed on the actually server.
I was told that I can basically 'override' this version by installing the cudatoolkit in the version that I need. I did that and installed cudatoolkit==10.0.
Unfortunately I am now running into a problem when trying to run an LSTM based model with tensorflow-gpu. I get the following:
2022-06-14 17:02:26.988359: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64
2022-06-14 17:02:26.989175: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64
2022-06-14 17:02:26.989208: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
In the path I still see cuda11. Is this causing the problem? How can I resolve this?
As you mentioned in the comment you need to use TensorFlow 2.1, then you need to install cuDNN 7.6 and CUDA 10.1 specifically.
Please follow the below tested build configurations to know about Python and TensorFlow versions compatible CUDA and cuDNN .
Please check this link for more details on GPU setup.
Machine Setting:
GPU: GeForce RTX 3060
Driver Version: 460.73.01
CUDA Driver Veresion: 11.2
Tensorflow: tensorflow-gpu 1.14.0
CUDA Runtime Version: 10.0
cudnn: 7.4.1
Note:
CUDA Runtime and cudnn version fits the guide from Tensorflow official documentation.
I've also tried for TensorFlow-gpu = 2.0, still the same problem.
Problem:
I am using Tensorflow for an objection detection task. My situation is that the program will stuck at
2021-06-05 12:16:54.099778: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10
for several minutes.
And then stuck at next loading process
2021-06-05 12:21:22.212818: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
for even longer time. You may check log.txt for log details.
After waiting for around 30 mins, the program will start to running and WORK WELL.
However, whenever program invoke self.session.run(...), it will load the same two library related to cuda (libcublas and libcudnn) again, which is time-wasted and annoying.
I am confused that where the problem comes from and how to resolve it. Anyone could help?
Discussion Issue on Github
===================================
Update
After #talonmies 's help, the problem was resolved by resetting the environment with correct version matching among GPU, CUDA, cudnn and tensorflow. Now it works smoothly.
Generally, if there are any incompatibility between TF, CUDA and cuDNN version you can observed this behavior.
For GeForce RTX 3060, support starts from CUDA 11.x. Once you upgrade to TF2.4 or TF2.5 your issue will be resolved.
For the benefit of community providing tested built configuration
CUDA Support Matrix
I am building this git hub repository for GPU acceleration in autodock from source. I have the Nvidia Development toolkit and can run the make command without issues (after modifying the make file to specify locations for the Cuda dependencies). However, when I try to run the .exe file that it creates it gives me this error: __cxa_atexit could not be located in the dynamic link library autodock_gpu_128wi.exe. What could be causing this issue? I think it should be compatible.
Machine:
OS: Windows 10
CPU: i7 9750H
GPU: GTX 1650
__cxa_atexit is a cxxabi in glibc
You need to check if linker has -lc by -Wl,-v argument for compiler.
If libc is not found by linker for some reason, you need to specify libc.so.6 path in glibc or just reinstall glibc.
how do you install only a CPU version of Tensorflow 2.x using pip ?
In the past, it was possible to install this 2 different versions.
Since I am running the scripts in a nonen GPU device ( without envidia card, intel card available without cuda support), I am getting following error:
2020-04-14 23:28:14.632879: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2020-04-14 23:28:14.632902: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)
In the past my workaround was to use a CPU only version.
Thanks for the hints in advance
You can choose the CPU-only version of tensorflow depending on your python version.
Check the list here:
https://www.tensorflow.org/install/pip#package-location
e.g. you will need to do the following for Python 3.8:
pip3 install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.3.0-cp38-cp38-manylinux2010_x86_64.whl
Issue solved after installing a CPU only version.
I used pin tensorflow-cpu and the version of the release. Somehow the fallback solution for CPU did not work in my setup.