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
Related
How to set up different versions of CUDA in one OS?
Here is my problem: Lastest Tensorflow with GPU support requires CUDA 11.2, whereas Pytorch works with 11.3. So what is the solution to install both libraries in Windows and Ubuntu?
One solution is to use Docker Container Environment, which would only need the Nvidia Driver to be of version XYZ.AB; in this way, you can use both PyTorch and TensorFlow versions.
A very good starting point for your problem would be this one(ML-WORKSPACE) : https://github.com/ml-tooling/ml-workspace
I'm trying to run a GitHub code, using TensorFlow 1.x.
I'm using colab for this. I'm encountering this kind of problem which I can't find a solution to.
I'm using Tensorflow 1.15, my Cuda version installed is 10.1 and the Nvidia drive version in colab is NVIDIA-SMI 450.51.05 Driver Version: 418.67.
When I tried to run another code above , cuda seems to be functional.
I'm using the GPU mode on colab
Can someone help me, please?
Thanks.
maybe the notebook session didn't connect the gpu, try restarting the session and wait for notebook to allocate the required resources.
Please share the link of Colab as I think you are not configuring the colab to use the GPUs. You can also follow the steps as,
Go to Colab
In the edit option at upper left corner
In the Hardware accelerator dropdown menu, select GPU.
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
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
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.