referenced from Installing Tensorflow in official site of tensorflow

In my computer, I will follow Installing Tensorflow on Ubuntu

Because my environment is Ubuntu16.04 LTS, I also decided to install tensorflow every bit native pip.

subsequently following this whole processing, probably you can install tensorflow anywhere.

Les't play!

Offset of all, you need to install pip or pip3.

basically, the pip or pip3 package director is usually installed on Ubuntu.

Simply, if you didn't install them.

type in like this :

sudo apt-get install python-pip python-dev # for Python 2.7
sudo apt-get install python3-pip python3-dev # for Python 3.n

if you run into the post-obit issue,

            hyunyoung2@hyunyoung2-desktop:~$                            sudo              apt-get              install              python-pip Reading package lists... Done Edifice dependency tree        Reading state data... Done E: Unable to locate package python-pip

you lot need to update package list

sudo apt-get update

            hyunyoung2@hyunyoung2-desktop:~$                            sudo              apt-go update Get:1 http://united states of america.annal.ubuntu.com/ubuntu xenial InRelease              [247 kB] Get:ii http://security.ubuntu.com/ubuntu xenial-security InRelease              [102 kB] Get:3 http://us.archive.ubuntu.com/ubuntu xenial-updates InRelease              [102 kB]        ......

plus, if you upgrade your software version to the latest version. it is much improve.

sudo apt-get -y upgrade

            hyunyoung2@hyunyoung2-desktop:~$                            sudo              apt-get              -y              upgrade Reading package lists... Done Edifice dependency tree        Reading state information... Done Calculating upgrade... Washed The following parcel was automatically installed and is no longer required:   snap-confine Employ              'sudo apt autoremove'              to remove it. The following packages have been kept back:   gnome-software gnome-software-common libmirclient9 linux-generic-hwe-16.04   linux-headers-generic-hwe-16.04 linux-image-generic-hwe-sixteen.04 python3-software-properties   software-properties-common software-properties-gtk ubu .....

pip

From now on, I am explaining to you nether installing the above update or upgrade.

sudo apt-go install python-pip python-dev # for python two.7
sudo apt-go install python3-pip python3-dev # for python iii.northward

Keep in mind that as of now(2017.06.27) the official site of tensorflow strongly recommend version 8.ane or higher of pip or pip3.

if yous already installed i of the above them. y'all got the following screen

  • python-pip python-dev
            hyunyoung2@hyunyoung2-desktop:~$                            sudo              apt-get              install              python-pip python-dev              [              sudo              ]              password              for              hyunyoung2:  Reading package lists... Done Building dependency tree        Reading state data... Washed python-dev is already the newest version              (2.vii.11-i)              .              python-dev              prepare              to manually installed. python-pip is already the newest version              (8.one.1-2ubuntu0.iv)              .              The following package was automatically installed and is no longer required:   snap-confine Utilize              'sudo apt autoremove'              to remove it. 0 upgraded, 0 newly installed, 0 to remove and 10 not upgraded.
  • python3-pip python3-dev
            hyunyoung2@hyunyoung2-desktop:~$                            sudo              apt-get              install              python3-pip python3-dev Reading package lists... Done Edifice dependency tree        Reading state information... Done python3-dev is already the newest version              (3.5.1-3)              .              python3-dev              set up              to manually installed. python3-pip is already the newest version              (8.1.i-2ubuntu0.4)              .              The following parcel was automatically installed and is no longer required:   snap-confine Use              'sudo apt autoremove'              to remove it. 0 upgraded, 0 newly installed, 0 to remove and ten not upgraded.

Finally,it'southward time to install tensorflow under installing prerequisite software like pip or pip3 and and then on

In social club to install tensorflow, choose one of the post-obit commands.

pip install tensorflow # Python 2.7; CPU support (no GPU support)
pip3 install tensorflow # Python 3.n; CPU support (no GPU support)
pip install tensorflow-gpu # Python 2.seven; GPU support
pip3 install tensorflow-gpu # Python 3.n; GPU back up

In my case, I decided to install tensorflow-gpu, considering I bought the graphic care in order to run tensorflow on graphic card

  • pip install tensorflow-gpu
            hyunyoung2@hyunyoung2-desktop:~$              pip              install              tensorflow-gpu Collecting tensorflow-gpu   Downloading tensorflow_gpu-ane.ii.0-cp27-cp27mu-manylinux1_x86_64.whl              (89.2MB)              100% |████████████████████████████████| 89.2MB 20kB/s      ........ Successfully congenital markdown html5lib Installing nerveless packages: half-dozen, funcsigs, pbr, mock, numpy, markdown, html5lib, bleach, wheel, setuptools, protobuf, backports.weakref, werkzeug, tensorflow-gpu Successfully installed backports.weakref bleach funcsigs html5lib-0.999 markdown mock numpy-1.11.0 pbr protobuf setuptools-xx.7.0 half-dozen-ane.10.0 tensorflow-gpu werkzeug wheel-0.29.0 You are using pip version eight.one.1, notwithstanding version 9.0.ane is available. You lot should consider upgrading via the              'pip install --upgrade pip'              control.
  • pip3 install tensorflow-gpu
            hyunyoung2@hyunyoung2-desktop:~$              pip3              install              tensorflow-gpu Collecting tensorflow-gpu   Downloading tensorflow_gpu-ane.ii.0-cp35-cp35m-manylinux1_x86_64.whl              (89.2MB)              100% |████████████████████████████████| 89.2MB 23kB/s      ........ Successfully built html5lib markdown Installing collected packages: half-dozen, setuptools, protobuf, html5lib, wheel, bleach, numpy, backports.weakref, werkzeug, markdown, tensorflow-gpu Successfully installed backports.weakref bleach html5lib-0.999 markdown numpy-1.xi.0 protobuf setuptools-xx.7.0 vi-1.10.0 tensorflow-gpu werkzeug wheel-0.29.0 You lot are using pip version eight.1.1, all the same version ix.0.1 is available. You should consider upgrading via the              'pip install --upgrade pip'              control.

Implementation of TensorFlow

Now, if you are done until the higher up processing, let's cheque if tensorflow works well.

python -c "import tensorflow as tf; impress (tf.__version__)" # for Python ii.7
python3 -c "import tensorflow equally tf; impress (tf.__version__)" # for Python 3.0

Every bit of now, tensorflow doesn't work. I think I didn't install some files related to GPU

Then after prompt the above command, you got the following :

  • python -c "import tensorflow as tf; print (tf.__version__)" # for Python 2.7
            hyunyoung2@hyunyoung2-desktop:~$              python              -c              "import tensorflow as tf; print (tf.__version__)"              Traceback              (most recent call final):   File              "<string>", line 1,              in              <module> ........ ImportError: libcusolver.so.8.0: cannot open up shared object file: No such file or directory   Failed to load the native TensorFlow runtime.  See https://www.tensorflow.org/install/install_sources#common_installation_problems              for              some mutual reasons and solutions.  Include the entire stack trace above this error message when asking              for              help.
  • python3 -c "import tensorflow as tf; print (tf.__version__)" # for Python three.0
            hyunyoung2@hyunyoung2-desktop:~$              python3              -c              "import tensorflow as tf; print (tf.__version__)"              Traceback              (most contempo call final):   File              "/home/hyunyoung2/.local/lib/python3.v/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41,              in              <module>     from tensorflow.python.pywrap_tensorflow_internal import              *              File              "/home/hyunyoung2/.local/lib/python3.five/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28,              in              <module>     _pywrap_tensorflow_internal              =              swig_import_helper()              File              "/dwelling house/hyunyoung2/.local/lib/python3.five/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24,              in              swig_import_helper     _mod              =              imp.load_module(              '_pywrap_tensorflow_internal', fp, pathname, clarification)              File              "/usr/lib/python3.v/imp.py", line 242,              in              load_module              return              load_dynamic(name, filename, file)              File              "/usr/lib/python3.5/imp.py", line 342,              in              load_dynamic              render              _load(spec)              ImportError: libcusolver.and then.8.0: cannot open shared object file: No such file or directory  During handling of the above exception, some other exception occurred:  Traceback              (near recent call last):   File              "<cord>", line ane,              in              <module> .......... ImportError: libcusolver.so.8.0: cannot open up shared object file: No such file or directory   Failed to load the native TensorFlow runtime.  See https://world wide web.tensorflow.org/install/install_sources#common_installation_problems              for              some common reasons and solutions.  Include the unabridged stack trace higher up this error message when request              for              assist.

if you await into fault message, you can verify what mistake is, where you could resolution about the avoe error(ImportError: libcusolver.so.8.0: cannot open shared object file: No such file or directory

Equally yous saw a line which is "See https://www.tensorflow.org/install/install_sources#common_installation_problems"

If you access the above link, you got the following :

As you saw, in the above URL of officail site of tensorflow, you got Stack Overflow link which provide the resoultion that you'r running into

GPU back up of NVIDIA

But, the resolution that tensorflow's offering didn't work in my case,

Considering I didn't install NVIDIA requirements to run TnesorFlow with GPU support.

for GPU support, y'all demand :

  1. you demand CUDA® Toolkit 8.0 for this, yous need to read NVIDIA's documentation

  2. you need to check if The NVIDIA drivers associated with CUDA Toolkit 8.0.

  3. y'all demand cuDNN v5.i, for details, read VIDIA's documentation Simply you are unlike from version number of v5.i, depending on your system

  4. you need to check your GPU card. y'all nee to read VIDIA's documentation for checking GPU with CUDA computer Adequacy 3.0 or higher.

  5. The libcupti-dev library, in lodge to install this Library,

run the following command in bash :

sudo apt-get install libcupti-dev

Before install CUDA® Toolkit 8.0, let's exercise pre-installation Deportment.

  • Verify the system has a CUDA-capable GPU

lspci | grep -i nvidia

            hyunyoung2@hyunyoung2-desktop:~$              lspci |              grep              -i              nvidia 01:00.0 VGA compatible controller: NVIDIA Corporation ......... 01:00.1 Audio device: NVIDIA Corporation ........

you can see system requirements to use GUDA on yous system, come across NVIDIA's documenataion

  • verify the system is running a supported version of Linux

uname -m && cat /etc/*release

            hyunyoung2@hyunyoung2-desktop:~$                            uname              -m              &&              true cat              /etc/*release x86_64              DISTRIB_ID              =Ubuntu              DISTRIB_RELEASE              =16.04 .........
  • verify the arrangement has gcc installed

gcc --version

            hyunyoung2@hyunyoung2-desktop:~$              gcc              --version              gcc              (Ubuntu 5.iv.0-6ubuntu1~16.04.iv)              five.4.0 20220609 Copyright              (C)              2022 Gratis Software Foundation, Inc. This is costless software;              see the              source                            for              copying conditions.  There is NO warranty;              not even              for              MERCHANTABILITY or FITNESS FOR A Detail PURPOSE.
  • verify the system has the right kernel headers and evolution packages installed.

The post-obit control will detect out the version of kernel your system is running :

uname -r

            hyunyoung2@hyunyoung2-desktop:~$                            uname              -r              4.8.0-36-generic

the above is the version of the kernel headers and development packages that must be installed prior to installing the CUDA Drivers.

Let's install kernel headers and development package with the above command,

if you know about another system similar CentOS and and then on, click NVIDIA'south documentation

BUT in my case of Ubuntu

sudo apt-become install linux-headers-$(uname -r)

            hyunyoung2@hyunyoung2-desktop:~$                            sudo              apt-get              install              linux-headers-$(              uname              -r              )              Reading package lists... Done Building dependency tree        Reading state information... Done linux-headers-4.8.0-36-generic is already the newest version              (iv.8.0-36.36~16.04.one)              .              linux-headers-iv.8.0-36-generic              set              to manually installed. The following package was automatically installed and is no longer required:   snap-confine Apply              'sudo apt autoremove'              to remove it. 0 upgraded, 0 newly installed, 0 to remove and x non upgraded.

I've installed CUDA Toolkit with runfile earlier, if you desire to know about this in detail, click NVIDIA's doucmentation

you can download CUDA Toolkit on here, cuda-download.

Just choose platform you are using and download the NVIDIA CUDA Tookit that contains the CUDA driver and tools.

The following is my choice of CUDA Toolkit

you lot need Download Verification with MD5 Checksum

md5sum <file>

            hyunyoung2 @ hyunyoung2-desktop              in              ~/Downloads              [eight:31:51]              $                            md5sum              cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb  d735c7fed8be0e72fa853f65042d5438  cuda-repo-ubuntu1604-eight-0-local-ga2_8.0.61-1_amd64.deb

From now on, I will explaining Package Manager installation. if you desire to know most another package director installation, click Here

This way is much easier than runfile

procedure of installation :

First, Perform the pre-installation actions of NVIDIA'due south documentation

Sencond, install repositary meta-data

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

Third, Update the Apt repository cache

sudo apt-become update

Forth, install CUDA

sudo apt-get install cuda

sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [8:26:08] C:100              $                            sudo              dpkg              -i              cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb Selecting previously unselected package cuda-repo-ubuntu1604-eight-0-local-ga2.              (Reading database ... 197423 files and directories currently installed.)              Preparing to unpack cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb ... Unpacking cuda-repo-ubuntu1604-viii-0-local-ga2              (8.0.61-ane)              ... Setting up cuda-repo-ubuntu1604-8-0-local-ga2              (8.0.61-1)              ... OK

sudo apt-get update

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [8:27:37] C:1              $                            sudo              apt-get update Go:one file:/var/cuda-repo-viii-0-local-ga2  InRelease Ign:1 file:/var/cuda-repo-eight-0-local-ga2  InRelease Get:ii file:/var/cuda-repo-viii-0-local-ga2  Release              [574 B] Get:2 file:/var/cuda-repo-eight-0-local-ga2  Release              [574 B] Get:3 file:/var/cuda-repo-8-0-local-ga2  Release.gpg              [819 B]                                         Get:3 file:/var/cuda-repo-8-0-local-ga2  Release.gpg              [819 B]                          ........ Fetched one,353 kB              in              10s              (129 kB/s)              Reading package lists... Done

sudo apt-go install cuda

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [8:28:08]                            $                            sudo              apt-become              install              cuda Reading package lists... Done Edifice dependency tree        Reading state information... Done The following parcel was automatically installed and is no longer required:   snap-confine Use              'sudo apt autoremove'              to remove information technology. The following boosted packages will be installed:   bbswitch-dkms ca-certificates-java cuda-8-0 cuda-control-line-tools-8-0 cuda-core-8-0 ........ Processing triggers              for              initramfs-tools              (0.122ubuntu8.8)              ... update-initramfs: Generating /kicking/initrd.img-4.8.0-36-generic Processing triggers              for              dbus              (1.10.6-1ubuntu3.3)              ... Processing triggers              for              ureadahead              (0.100.0-19)              ...

later the above processing, Perform Postal service-installation Deportment below

if you want to install CUDA Toolkit with runfile, yous demand to know how to execute text fashion of linux.

about the above matter, refer to this askubuntu

Post-installation Deportment

if yous want to read NVIDIA'southward documentation about post-installation actions, click HERE

Post_installation Actions have two ways :

  • Mandatory Activity : Environment Setup.

  • Recommended Actions : verify the installation.

Mandatory Activity

Some actions must be taken after the installation earlier the CUDA Toolkit and Driver tin be used.

  • Surround Setup

The PATH variable needs to include > /usr/local/cuda-8.0/bin

In order to add this path to the PATH variable :

consign PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}

Below is to have to configure when you used the runfile installation method.

$ export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

In my example, I use oh my zsh, So I want to configure PATH on olny zsh.

On environment set up of oh my zsh

                          # hyunyoung2 @ hyunyoung2-desktop in ~ [nine:37:06]                            $              vim .zshrc              # add together the line below in .zshrc file              # exprot cuda                            export                            PATH              =/usr/local/cuda-viii.0/bin${              PATH:+:${              PATH              }}              # To load the new PATH variable, log out from oh my zsh                            # hyunyoung2 @ hyunyoung2-desktop in ~ [9:39:03] C:127              $                            go out              hyunyoung2@hyunyoung2-desktop:~/Downloads$              zsh              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [9:39:58]                            $

Before configuring PATH variable, you tin't use nvcc –version like the following

            hyunyoung2@hyunyoung2-desktop:~/Downloads$              nvcc              --version              The program              'nvcc'              is currently not installed. You can              install              it past typing:              sudo              apt              install              nvidia-cuda-toolkit

Subsequently configure PATH variable on .zshrc file, Now you can verify installation of CUDA

nvcc --version

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [9:44:51] C:127              $              nvcc              --version              nvcc: NVIDIA              (R)              Cuda compiler driver Copyright              (c)              2005-2016 NVIDIA Corporation Built on Tue_Jan_10_13:22:03_CST_2017 Cuda compilation tools, release eight.0, V8.0.61

So whether or not tensorflow-gpu works well,

python -c "import tensorflow every bit tf; print(tf.__version__)"   # Ptyhon ii.7   python3 -c "import tensorflow equally tf; print(tf.__version__)" # Python 3.n

  • python -c "import tensorflow as tf; impress(tf.__version__)"   # Ptyhon 2.7
                          # hyunyoung2 @ hyunyoung2-desktop in /usr/local/cuda/bin [9:53:42] C:1              $              python              -c              "import tensorflow every bit tf; print(tf.__version__)"              ......... ImportError: libcudnn.so.five: cannot open shared object file: No such file or directory ........
  • python3 -c "import tensorflow as tf; print(tf.__version__)" # Python 3.northward
                          # hyunyoung2 @ hyunyoung2-desktop in /usr/local/cuda/bin [9:53:58] C:i              $              python3              -c              "import tensorflow equally tf; impress(tf.__version__)"              Traceback              (most recent call terminal): ......... ImportError: libcudnn.so.5: cannot open shared object file: No such file or directory  During treatment of the above exception, another exception occurred: ......... ImportError: libcudnn.so.5: cannot open shared object file: No such file or directory .......

So from now on, You demand to install libcudnn, read NVIDIA's documentation

When you lot install libcudnn, specifically wait at the version of cuddn y'all need.

my case is the version number of 5, before download, you need to register NVIDIA Developer.

if yous download and uncompress thie file, cudnn-8.0-linux-x64-v5.0-ga.tgz

tar -xvzf ./cudnn-viii.0-linux-x64-v5.0-ga.tgz

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [x:06:11]                            $                            tar              -xvzf              ./cudnn-eight.0-linux-x64-v5.0-ga.tgz  cuda/include/cudnn.h cuda/lib64/libcudnn.then cuda/lib64/libcudnn.then.5 cuda/lib64/libcudnn.and so.5.0.5 cuda/lib64/libcudnn_static.a

yous need to move the whole files above into the lib64 and include directoies

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [x:08:32] C:ane              $                            ls              cuda  cuda-repo-ubuntu1604-viii-0-local-ga2_8.0.61-1_amd64.deb  cudnn-viii.0-linux-x64-v5.0-ga.tgz              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [10:08:41]                            $                            cd              cuda              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:08:44]                            $                            ls              include  lib64              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:12:19]                            $                            sudo cp              lib64/*              /usr/local/cuda/lib64/              [              sudo              ]              password              for              hyunyoung2:              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:12:51]                            $                            sudo cp              include/*              /usr/local/cuda/include/

Check if the files moved.

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:13:05]                            $                            ls              include  cudnn.h              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:13:19]                            $                            ls              lib64                                   libcudnn.so  libcudnn.so.five  libcudnn.then.five.0.five  libcudnn_static.a              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:xiii:31]                            $                            ls              /usr/local/cuda/lib64  .......... libcudnn.so           libcusparse.then.8.0.61  libnppim.then.8.0.61    libnvrtc-builtins.and then.8.0 libcudnn.so.5         libcusparse_static.a   libnppi.so            libnvrtc-builtins.so.8.0.61 libcudnn.and so.v.0.5     libnppc.then             libnppi.and then.viii.0        libnvrtc.so libcudnn_static.a     libnppc.so.8.0         libnppi.so.8.0.61     libnvrtc.then.8.0 ..........              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads/cuda [10:13:45]                            $                            ls              /usr/local/cuda/include  ....... cudnn.h                    math_functions_dbl_ptx3.hpp               sm_32_intrinsics.hpp .......

Now check if tensorflow-gpu works well, Only information technology doesn't piece of work

Also you lot demand to chang LD_LIBRARY_PATH like this :

consign LD_LIBRARY_PATH=/usr/local/cuda-viii.0/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

                          # hyunyoung2 @ hyunyoung2-desktop in ~ [9:37:06]                            $              vim .zshrc              # exprot cuda                            export                            PATH              =/usr/local/cuda-8.0/bin${              PATH:+:${              PATH              }}              consign                            LD_LIBRARY_PATH              =/usr/local/cuda-8.0/lib64\              ${              LD_LIBRARY_PATH:+:${              LD_LIBRARY_PATH              }}

later the above configuration, tensorflow works well

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [10:23:37]                            $              python              -c              "import tensorflow as tf; impress(tf.__version__)"              1.two.0              # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [ten:23:39]                            $              python3              -c              "import tensorflow as tf; print(tf.__version__)"              1.2.0

Finally, allow's cheque if nvidia driver works well

Verify the installation

nvidia-smi

                          # hyunyoung2 @ hyunyoung2-desktop in /proc/commuter/nvidia [11:10:twoscore]                            $              nvidia-smi       Tue Jun 27 11:12:25 2022        +-----------------------------------------------------------------------------+ | NVIDIA-SMI 375.66                 Driver Version: 375.66                    | |-------------------------------+----------------------+----------------------+ | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC | | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute 1000. | |===============================+======================+======================| |   0  GeForce GTX 1080    Off  | 0000:01:00.0      On |                  N/A | |  0%   40C    P8    11W / 230W |    447MiB /  8105MiB |      1%      Default | +-------------------------------+----------------------+----------------------+                                                                                 +-----------------------------------------------------------------------------+ | Processes:                                                       GPU Memory | |  GPU       PID  Type  Process name                               Usage      | |=============================================================================| |    0      1070    Thousand   /usr/lib/xorg/Xorg                             290MiB | |    0      1759    G   compiz                                         155MiB | +-----------------------------------------------------------------------------+

In order to verify the version, type

cat /proc/driver/nvidia/version

                          # hyunyoung2 @ hyunyoung2-desktop in /proc/driver/nvidia [11:13:55] C:1              $                            true cat              /proc/driver/nvidia/version NVRM version: NVIDIA UNIX x86_64 Kernel Module  375.66  Monday May  ane 15:29:16 PDT 2022 GCC version:  gcc version 5.4.0 20220609              (Ubuntu five.4.0-6ubuntu1~xvi.04.four)

more specifically, in club to verify, run sampli source of NVIDIA.

cd /usr/local/cuda/bin

                          # hyunyoung2 @ hyunyoung2-desktop in /usr/local/cuda/bin [xi:17:47]                            $                            ls              bin2c        cudafe++                     cuda-memcheck        nsight        nvlink   ptxas computeprof  cuda-gdb                     cuobjdump            nvcc          nvprof crt          cuda-gdbserver               fatbinary            nvcc.profile  nvprune cudafe       cuda-install-samples-8.0.sh  gpu-library-advisor  nvdisasm      nvvp

run cuda-install-sample-8.0.sh similar this :

cuda-install-samples-8.0.sh <dir>

<dir> means where yous want install instance sources of NVIDIA

in my case, I installed the source files into ~/sample directory

sudo ./cuda-install-samples-eight.0.sh ~/sample/

                          # hyunyoung2 @ hyunyoung2-desktop in /usr/local/cuda/bin [xi:17:48]                            $                            sudo              ./cuda-install-samples-8.0.sh ~/sample/  Copying samples to /dwelling/hyunyoung2/sample/NVIDIA_CUDA-eight.0_Samples now... Finished copying samples.              # hyunyoung2 @ hyunyoung2-desktop in ~/sample [11:21:57]                            $                            ls              NVIDIA_CUDA-8.0_Samples

move into NVIDIA_CUDA-8.0_Samples, complie any source file

in my case, I chose deviceQuery directory like this :

                          # hyunyoung2 @ hyunyoung2-desktop in ~/sample/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery [11:24:45]                            $                            ls              deviceQuery.cpp  Makefile  NsightEclipse.xml  readme.txt              # hyunyoung2 @ hyunyoung2-desktop in ~/sample/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery [eleven:24:48] C:two              $                            sudo              brand /usr/local/cuda-8.0/bin/nvcc              -ccbin              g++              -I../../common/inc              -m64              -gencode              curvation              =compute_20,lawmaking=sm_20              -gencode              .........              cp              deviceQuery ../../bin/x86_64/linux/release              # hyunyoung2 @ hyunyoung2-desktop in ~/sample/NVIDIA_CUDA-viii.0_Samples/1_Utilities/deviceQuery [11:24:51]                            $                            ls              deviceQuery  deviceQuery.cpp  deviceQuery.o  Makefile  NsightEclipse.xml  readme.txt              # hyunyoung2 @ hyunyoung2-desktop in ~/sample/NVIDIA_CUDA-viii.0_Samples/1_Utilities/deviceQuery [11:25:34]                            $              ./deviceQuery ./deviceQuery Starting...   CUDA Device Query              (Runtime API)              version              (CUDART static linking)              Detected i CUDA Capable device(south)              Device 0:              "GeForce GTX 1080"              CUDA Driver Version / Runtime Version          eight.0 / 8.0   CUDA Capability Major/Minor version number:    6.1   Full corporeality of global retentivity:                 8106 MBytes              (8499691520 bytes)              (20)              Multiprocessors,              (128)              CUDA Cores/MP:     2560 CUDA Cores   GPU Max Clock charge per unit:                            1823 MHz              (1.82 GHz)              Memory Clock rate:                             5005 Mhz   Memory Bus Width:                              256-scrap   L2 Cache Size:                                 2097152 bytes   Maximum Texture Dimension Size              (ten,y,z)              1D              =(131072),              2d              =(131072, 65536),              3D              =(16384, 16384, 16384)              Maximum Layered 1D Texture Size,              (num)              layers              1D              =(32768), 2048 layers   Maximum Layered 2D Texture Size,              (num)              layers              2D              =(32768, 32768), 2048 layers   Full amount of constant memory:               65536 bytes   Total amount of shared memory per block:       49152 bytes   Full number of registers bachelor per block: 65536   Warp size:                                     32   Maximum number of threads per multiprocessor:  2048   Maximum number of threads per block:           1024   Max dimension size of a thread block              (ten,y,z):              (1024, 1024, 64)              Max dimension size of a filigree size              (x,y,z):              (2147483647, 65535, 65535)              Maximum memory pitch:                          2147483647 bytes   Texture alignment:                             512 bytes   Concurrent copy and kernel execution:          Aye with 2 copy engine(s)              Run              time              limit on kernels:                     Yes   Integrated GPU sharing Host Memory:            No   Support host page-locked retentivity mapping:       Aye   Alignment requirement              for              Surfaces:            Aye   Device has ECC support:                        Disabled   Device supports Unified Addressing              (UVA):      Yes   Device PCI Domain ID / Double-decker ID / location ID:   0 / ane / 0   Compute Mode:      < Default              (multiple host threads can use ::cudaSetDevice()              with device simultaneously)              >              deviceQuery, CUDA Commuter              =              CUDART, CUDA Commuter Version              =              8.0, CUDA Runtime Version              =              eight.0, NumDevs              =              1, Device0              =              GeForce GTX 1080 Result              =              PASS

When you complete until completion of the whole processing above correctly.

your CUDA commuter works well. from now on, accept a fun with TensorFlow or CUDA.

Summary with commands to explain how to install tensorflow in Ubutu16.04

In my case, CUDA Toolkit eight.0, cuDNNv6.0, and Python3.5

            $ sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb $ sudo apt update $ sudo apt install cuda$ $ sudp dpkg -i cuda-repo-ubuntu1604-viii-0-local-cublas-performance-update_8.0.61-1_amd64.de $ sudo apt upgrade cuda $ sudo apt-get install python3-pip python3-dev python-virtualenv  $ sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb OR sudo dpkg -i libcudnn6-dev_6.0.21-ane+cuda8.0_amd64.deb $ sudo pip3 install --upgrade tensorflow-gpu  # As well y'all have to configure the path of file, "libcupti.so.8.0.".  # put the path of the bottom of .bashrc like this : # For Tensorflow export LD_LIBRARY_PATH=/usr/local/cuda-8.0/extras/CUPTI/lib64/${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} consign PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}

your installation of tensorflow-gpu is done with those commands.

apart from those commands, you lot don't need to practise anything.

Afterward those commands, bank check if tensorflow-gpu works well as follows:

                          # hyunyoung2 @ hyunyoung2-desktop in ~/Downloads [ii:10:12]                            $              python3              -c              "import tensorflow as tf; print(tf.__version__)"              one.4.0

As you tin can run across, yous identify the version of tensorflow-gpu.

Reference

  • Installing TensorFlow

  • Installing with native pip

  • mutual installation problems with Tensorflow

  • In guild to run TensorFlow with GPU support

  • Installing CUDA with runfile

  • how to insatll pip

  • How to insatll cudnn

  • how to change path of file for cudnn