#Install cuda 9 on ubuntu 18.04 install#
#Install cuda 9 on ubuntu 18.04 driver#
#Install cuda 9 on ubuntu 18.04 archive#
#Install cuda 9 on ubuntu 18.04 download#

One command will do: sudo apt install gcc-6 g++-6 There is no need to set the default gcc to 6, because the tensorflow build tool will ask which gcc to use. I read from many sources that building tensorflow with the default gcc 7 is problematic, and should use gcc 6, so I installed gcc 6. bazel homepage says only 16.04 and 14.04 are supported, but I found it runs fine in my 18.04.

#Install cuda 9 on ubuntu 18.04 download#

To install bazel, download bazel-0.15.2-installer-linux-x86_64.sh and run it without root. Sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel

#Install cuda 9 on ubuntu 18.04 driver#

Supports MultiDevice Co-op Kernel Launch: YesДevice PCI Domain ID / Bus ID / location ID: 0 / 1 / 0ДeviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.2, CUDA Runtime Version = 9.2, NumDevs = 1, Device0 = GeForce GTX 1070 TiДЇollow the tensorflow official doc, install the python packages. Support host page-locked memory mapping: YesДevice supports Unified Addressing (UVA): Yes Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)Д¬oncurrent copy and kernel execution: Yes with 2 copy engine(s) Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Maximum number of threads per block: 1024 Maximum number of threads per multiprocessor: 2048 Total number of registers available per block: 65536 Total amount of shared memory per block: 49152 bytes Total amount of constant memory: 65536 bytes Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers (19) Multiprocessors, (128) CUDA Cores/MP: 2432 CUDA Cores Total amount of global memory: 8117 MBytes (8510832640 bytes) usr/local/cuda-9.2/extras/demo_suite/deviceQuery Starting.Д¬UDA Device Query (Runtime API) version (CUDART static linking)Д¬UDA Driver Version / Runtime Version 9.2 / 9.2Д¬UDA Capability Major/Minor version number: 6.1

$ /usr/local/cuda-9.2/extras/demo_suite/deviceQuery To test whether CUDA and the kernel driver is good: create a file named nf which looks like this: IвЂ™d rather put it into /etc/ld.so.conf.d/. We can add the following environment variable:Д®xport LD_LIBRARY_PATH=/usr/local/cuda-9.2/extras/CUPTI/lib64:/usr/local/cuda-9.2/lib64Д«ut I prefer not to mess with LD_LIBRARY_PATH. Since the library installation directory is not a standard one, the dynamic linker donвЂ™t know where to find. To install cuDNN, I installed the following 3 debs: It will be installed into /usr/local/cuda-9.2/.

Nvidia only provides CUDA on Ubuntu 17.10 and 16.04, not 18.04, but I found that CUDA for 17.10 works in 18.04 as well. Step 2: Install latest CUDA and cuDNN SDK GCC version: gcc version 7.3.0 (Ubuntu 7.3.0-16ubuntu3) NVRM version: NVIDIA UNIX x86_64 Kernel Module 396.45 Thu Jul 12 20:49: Note that the purge command is necessary, otherwise apt refuses to install 396.ДЄfter reboot, one should be able to get the following: $ sudo add-apt-repository ppa:graphics-drivers I did the following to install driver 396: Status: CUDA driver version is insufficient for CUDA runtime version 21:56:39.036223: E tensorflow/core/common_runtime/direct_:158] Internal: cudaGetDevice() failed. Up to now, the default installed repository only has driver version up to 390, which is not compatible with CUDA 9.2. I choose Ubuntu PPA because itвЂ™s better integrated into the package management system. There are two way to install it: from Ubuntu PPA, or from Nvidia. Step 1: Install latest nvidia kernel driver 396.x I hate legacy stuff, so I will build tensorflow from source. One has to click into the legacy link to download. The tensorflow homepage only provides prebuilt binary supporting CUDA 9.0, but Nvidia has phased out 9.0 for quite some time. The installation is some how straight forward, but there are still traps that I stepped into. var/cache/apt/archives/nvidia-390_390.30-0ubuntu1_amd64.This is a guide on installing the latest tensorflow with the latest CUDA library on the latest Ubuntu LTS. Trying to overwrite '/usr/lib/x86_64-linux-gnu/libGLX_indirect.so.0', which is also in package libglx-mesa0:amd64 18.0.0~rc5-1ubuntu1Д®rrors were encountered while processing:

#Install cuda 9 on ubuntu 18.04 archive#

Дpkg: error processing archive /var/cache/apt/archives/nvidia-390_390.30-0ubuntu1_b (-unpack): If you come across an error use this to fix the install: If all goes well you will not have an installation error. Now, the install process can take a bit of time. If you do not it will install 9.1! I don't know why it does this, but it happened. Sudo apt-get -i cuda-repo-ubuntu1704_9.0.176-1_bД«e sure to explicitly type out the desired version. 1) Download the network repository meta-data.