This is how i configured the installation of Tensorflow -> screenshot
, at the end cuda libraries are not setting up.
I have Installed Cuda 8.0, cuDNN 5.1.10 and i have Nvidia GForce 1060 graphics card.Can anyone tell me the solution for this. Thanks in advance.
I am new to stackoverflow, if any mistakes kindly apologise me.
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I'm working with mask rcnn. I need a gpu to do real time work. When I look at the system requirements for this, it wants cuda 10.0 (tensorflow-gpu=1.15) for this, I installed ubuntu 18.04 and after installing cuda 10.0, when I reboot, the screen freezes at user login. Can you help me to solve this problem?
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
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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
After installing tensorflow gpu = 2.0.0 it got stuck after detecting gpu.
enviornment settings for this project is
ubuntu 18.04
cuda 10.0
cudnn 7.4.1
created a virtual enviornment
install tensorflow-gpu=2.0.0
While trying to check gpu with tf.test.is_gpu_available().compliation got stucked it is shown below.
enter image description here
changed cudnn version to 7.6.2.Then it works well.
I am trying to install Tensorflow in Ubuntu 16.04 ( in google cloud ). What I have done so far is created an compute instance. I have added a NVIDIA Tesla K80 to this instance.
Also, made sure that the proper version of tensorflow ( version 1.14.0 ) is installed and
Cuda version of 8.0 is installed
and
CudNN version of 6.0 is installed as per the tensorflow gpu - cuda mapping
When I run a simple tensorflow program, I get
Cannot assign a device for operation MatMul: {{node MatMul}}was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
Can anyone please let me know where I am doing wrong. Is the instance selection is correct?
Please do let me know and thanks for your help.
The CUDA and CudNN versions that have been tested with Tensorflow 1.14 are the 10.0 and the 7.4, respectively.
More information about version compatibility can be found here.
I have a problem since few days.
I installed NVIDIA drivers and cuDNN using some step by steph tutoriaI from the Internet.
The installation succeded since tests on CUDA samples passed.
I then installed python 3.7, jupyter and Tensorflow-gpu.
However, Tensorflow don't see my 2 GPUs and see only XLA_GPUs.
I tried recommandations from other posts (such us uninstalling and installing tensorflow) but this did not solve my problem.
Anyone have an idea how to solve this problem ?