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?
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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 Gforce 1080 Ti GPU and I installed visuall studio 2017 enterprise, 430.64-desktop-win10-64bit-international-whql, cuda_10.0.130_411.31_win10, cudnn-9.0-windows10-x64-v7.4.2.24 and Anaconda3-5.2.0-Windows-x86_64 respectively on my computer. after that, I make a virtual environment variable using Anaconda command prompt and install TensorFlow-GPU using this command: pip install --ignore-installed --upgrade tensorFlow-gpu==1.9 but my system using CPU instead of gpu.one time at first it used gpu and then during learning my network, it used CPU again. what is the problem? and what should I do to solve this problem and make force my system to use GPU? please help me. thank you.
According to https://www.tensorflow.org/install/source#tested_source_configurations
tensorflow_gpu-1.9.0 only supports CUDA 9.0, it might be the issue. I suggest you could try tensorflow_gpu-1.13.1
The question is,I cannot make my computer work for my tensorflow-gpu on ubuntu system. Because NVIDIA driver cannot be installed on ubuntu.So I run tensorflow-gpu on Windows10,but it doesnot support tensorflow-serving.
I know Docker can help me to do it,and i really installed it,but just tensorflow-cpu.That would be very slowly if I just run tensorflow-cpu version.
In case that,I came up with a thought that I install two tensorflow,one is GPU version and on system,the other is CPU version on Docker.GPU version for training and save a model,then CPU version loading the saved model.
What I want to know is does this way work,and is it time saving?Or put it simply,does it take less time than just run tensorflow-cpu version on Docker?
TensorFlow GPU with NVIDIA GPUs on Ubuntu is supported, and there are drivers available. Check this tutorial.
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.