#Ubuntu 18.04 cuda 10.0 tensorflow how to#
#Ubuntu 18.04 cuda 10.0 tensorflow install#
#Ubuntu 18.04 cuda 10.0 tensorflow update#
#Ubuntu 18.04 cuda 10.0 tensorflow driver#
#Ubuntu 18.04 cuda 10.0 tensorflow zip#

The result will show the information about you GPU devices You can check your cuda installation using following sample:Д¬d ~/NVIDIA_CUDA-10.0_Samples/1_Utilities/deviceQuery Comment your linux kernel version noted in step 5. (not likely) If you got nvidia-smi is not found then you have unsupported linux kernel installed.

#Ubuntu 18.04 cuda 10.0 tensorflow driver#

Step 7: Reboot the system to load the NVIDIA drivers.Д®cho 'export PATH=/usr/local/cuda-10.0/bin$' > ~/.bashrcД¬heck driver version probably Driver Version: 410.48

#Ubuntu 18.04 cuda 10.0 tensorflow install#

Sudo gedit /etc/modprobe.d/nfДownload CUDA install files,download address:CUDA Toolkit 10.0 Download. If you have been installed any version of CUDA,remove previous cuda installation use follow action: Sudo apt-get install linux-headers-$(uname -r) To install linux header supported by your linux kernel do following: Sudo apt-get install python-dev python-pip The x86_64 line indicates you are running on a 64-bit system which is supported by cuda 10 To determine which distribution and release number youвЂ™re running, type the following at the command line: Step 3: Verify You Have a Supported Version of Linux: If your graphics card is from NVIDIA then goto CUDA GPUs and verify if listed in CUDA enabled gpu list.

#Ubuntu 18.04 cuda 10.0 tensorflow update#

If you do not see any settings, update the PCI hardware database that Linux maintains by entering update-pciids (generally found in /sbin) at the command line and rerun the previous lspci command. Step 2: Verify You Have a CUDA-Capable GPU: #suggest to change the apt source to local sites We will also be installing CUDA 10.0 and cuDNN 7.3.1 along with the GPU version of tensorflow 1.12.

#Ubuntu 18.04 cuda 10.0 tensorflow how to#

6.Install CUDA 10.0 Tensorflow-1.12 on ubuntu 18.04ДЄt the present time,the latest tensorflow-gpu-1.12 version installed by system pip is not compatiable to CUDA 10.0,for it was build by CUDA 9.0,so if you want to use the latest version tensorflow-gpu with CUDA 10.0 in 18.04,you need to build from source.This is going to be a tutorial on how to install tensorflow 1.12 GPU version. Please note that each additional compute capability significantly increases yourД«uild time and binary size. You can find the compute capability of your device at. Please specify a list of comma-separated Cuda compute capabilities you want to build with. If NCLL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. Please specify the NCCL version you want to use. Please specify the cuDNN version you want to use: 7.0 Please specify the CUDA SDK version you want to use: 10.0 Default is Дo you wish to build TensorFlow with CUDA support? : Y Please input the desired Python library path to use. : /home/YourUserAccount/anaconda3/envs/pењ“5/bin/python3.5 Note: We use version 0.18.0 here, using version 0.19.0 or higher has caused some problems when we're trying.

#Ubuntu 18.04 cuda 10.0 tensorflow zip#

Sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python

Install Bazel ( Official Bazel Installation Documentation).

Pip install -U keras_preprocessing=1.0.3 -no-deps Pip install -U keras_applications=1.0.5 -no-deps Install the TensorFlow pip package dependencies

Install TensorFlow Package DependenciesДЄctivate your virtual python environment named 'pењ“5'.

Up to now, if everything is ok, Go Fighting to the end!!!Д¬reate a python environment with version 3.5Д¬onda create -name pењ“5 python=3.5 or conda create -n pењ“5 python=3.5.

Sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* Sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 Sudo cp cuda/include/cudnn.h /usr/local/cuda/include NVIDIA-Linux-x86_64-410.78.run -no-opengl-libsД¬opy the files into the CUDA Toolkit directory If there are some troubles, remove CUDA, and run the installer: Test if CUDA is successfully installed, run:

If there are some troubles, remove the driver, and run the installer: Test if driver is successfully installed, run: So we alternatively choose to compile tensorflow-gpu with cuda 10.0 ourselves. Accidentally using cuda 9.0 with RTX-2080-Ti with tensoflow-gpu will cause core dumped sometimes. The reason we configure the environment as following is that tensorflow r1.* only support cuda 9.0 while RTX-2080-Ti only support cuda 10.0. This is a tutorial to configure the environment.Дon't forget to give me a star if you like it, thank you! Ubuntu 18.04 + RTX 2080 Ti Driver + cuda-10.0 + cudnn-7.0 + tensorflow-gpu + Anaconda Tutorial