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
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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?
I'm running a Windows computer with just a CPU (no GPU). When I run pip install tensorflow -vvv in order to see what pip is doing, it lists a lot of links, but for all of them, it says "Skipping link ... it is not compatible with this Python."
Does tensorflow support Python 3.6.4 on Windows? If so, what binary URL should I use to install it?
(I previously installed with this version due to reading this, but ran into this error without the DLL load failed message, so I'm wondering if there's a better version I should use.)
Also, I'm aware that Tensorflow says they support Python 3.x, but right now it hasn't been working for me.
You have probably installed Python 32bits, you need the 64bits version
I installed CUDA driver, Cudnn and tensorflow-gpu. I can run "nvidia-smi" and "nvcc --version". However, I still cannot use gpu, I don't know why, anyone can tell me?
I use an example from this: https://www.tensorflow.org/tutorials/using_gpu
But what I get is:
enter image description here
You need to use same version cuda-8 as nvidia drivers.
If you use nvidia-384.xx you need to cuda_8.0.xx_384.xx
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.
I'm using tensorflow with gpu. My computer have NVIDIA gforce 750 ti and I'm gonna replace it with 1080 ti. do I have to re install tensorflow(or other drivers etc.)? If it is true, what exactly do I have to re-install?
One more question, Can I speed up the training process by install one more gpu in the computer?
As far as I know the only thing you need to reinstall are the GPU drivers (CUDA an/or cuDNN). If you install the exact same version with the exact same bindings Tensorflow should not notice you changed the GPU and continue working...
And yes, you can speed up the training process with multiple GPUs, but telling you how to install and manage that is a bit too broad for a Stackoverflow answer....