In my case it told me to install CUDA 8.0 and cuDNN 5.0, but I have been to able to use other verison than these also. which tells you the version of CUDA and cuDNN that is compatible with your GPU version, b ut, It dosen’t work properly now. There is a tensorflow script available online named as tensorflow_self_check.py.
OS : Windows-10 64 bit (i7, 8th Gen processor)įor the sake of simplicity I am going to keep this post as short as possible, also mentioning the challenges I faced during my installations.īut before moving forward there are things you must avoid getting into: This post is a step-by-step guide to installing Tensorflow -GPU on a windows 10 Machine. Getting started with Tensorflow-GPU on Windows 10 Sudo ln -s /usr/bin/gcc-6 /usr/local/cuda-9.2/bin/gcc $ sudo apt install nvidia-cuda-toolkit gcc-6 (installing gcc version 6, and making symbolic link) Install version 6 0f gcc as CUDA requires gcc 6.
nvidia-smi -l 1 gives the gpu running update every 1 sec interval.This is specifically for CUDA 9.0 and cuDNN 7.0.5 Finally install Tensorflow-GPU $ pip install –upgrade tensorflow-gpu=1.12.0.Now open “bashrc” file to add path of CudaĪdd these following lines to the end of bashrc file.Now finally we need to install LIBCUPTI.$ sudo chmod a+r /usr/local/cuda-9.0/include/cudnn.h /usr/local/cuda/lib64/libcudnn* $ sudo cp cuda/include/cudnn*.h /usr/local/cuda-9.0/include/ $ sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-9.0/lib64/ Yes to cuda install (now complete the whole installation)ĭownload “cuDNN v7.0.5 Library for Linux”.
No to “Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? say yes to installing with an unsupported configuration.‘-overide’ is used to ignore the gcc compiler check during the installation. Navigate to the folder where CUDA toolkit was downloaded and open the terminal.Navigate and download CUDA toolkit 9.0.If this works then Nvidia Display Driver was installed correctly. $ sudo apt-get install nvidia-driver-390 nvidia-settings $ sudo add-apt-repository ppa:graphics-drivers/ppa Purge all preinstalled nvidia drivers, use the following commands to install the drivers.I figured out that Nvidia-390 is the most stable for me, though nvidia-440 was latest driver for my system. The First step in the process is to get the right Nvidia Display drivers.I would like to keep the process and post as short as possible so that, the process can be followed easily by anyone and everyone! OS : Ubuntu 18.04 LTS, 64 bit (i7, 8th Gen processor) Here, I do understand that it need some dependencies.This post is a step-by-step guide to installing Tensorflow -GPU on Ubuntu 18.04 LTS (bionic). Recommends: nvidia-visual-profiler (= 10.1.243-3) but it is not going to be installedĮ: Unable to correct problems, you have held broken packages. Or been Recommends: nvidia-visual-profiler (= 10.1.243-3) but it is not going to be installedĮ: Unable to correct problems, you have held broken packages.oing to be installed Requested an impossible situation or if you are using the unstableĭistribution that some required packages have not yet been created Are the newer versions back-compatible?Īs per Nmath's suggestion, I went on to install CUDA from the Ubuntu repository as follows. I found an old article which says my GPU supports CUDA 2.1. Or should I download CUDA separately in case I wish to run some Tensorflow code. I want to download Pytorch but I am not sure which CUDA version should I download. In other answers for example in this one Nvidia-smi shows CUDA version, but CUDA is not installed there is CUDA version next to the Driver version. AAgAAAAAAAAACAAAAAAAAAA= -shared-files 142MiB | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr.
When I type nvidia-smi in my terminal I can see the following. I've got a supercomputer from my university to train bigger models. I want to use my GPU (Nvidia Quadro 2000 1GB GDDR5) for very basic machine learning models. This is the first GPU I am using and unfortunately, I am using Ubuntu 20.04 not easy easy Windows.