I've been running this machine for TensorFlow and Keras with Jupyter notebook.
These are my environments:
$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.3 LTS"
$ lspci | grep VGA
01:00.0 VGA compatible controller: NVIDIA Corporation GP104 [GeForce GTX 1080] (rev a1)
Disable secure boot on UEFI
First, we need to disable "secure boot" on UEFI menu because it prevents NVIDIA driver from loading into OS. So enter UEFI menu while boot time by pressing the key something like the delete key.
That's the first time I heard that. I used to use BIOS, but now, a lot of motherboards recently released use UEFI. And UEFI has secure boot (see detail here, this was helpful for me).
If you use an ASUS motherboard, this document also can be helpful.
Remove old NVIDIA driver and CUDA
$ dpkg -l | grep nvidia
$ dpkg -l | grep cuda
$ sudo apt-get --purge remove nvidia-*
$ sudo apt-get --purge remove cuda-*
Install new NVIDIA driver and CUDA9.0
Download CUDA Toolkit 9.0
Download from NVIDIA developer site.
Instatll
$ sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
$ sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
$ sudo apt-get update
$ sudo apt-get install cuda
Add PATH and LD_LIBRARY_PATH
~/.bashrc
export PATH="/usr/local/cuda/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
Then reboot.
sudo reboot
Check that the driver was installed
$ nvidia-smi
This command shows the status of the graphics card installed on your machine like this:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.25 Driver Version: 390.25 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1080 Off | 00000000:01:00.0 On | N/A |
| 27% 36C P8 12W / 180W | 7753MiB / 8116MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1351 G /usr/lib/xorg/Xorg 24MiB |
| 0 12638 C /home/jupyter/tensorflow/bin/python3 7717MiB |
+-----------------------------------------------------------------------------+
Install cuDNN
Download cuDNN
Download Debian package from NVIDA developer site.
Install the rutime library
$ sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
Install the developer library
$ sudo dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
Add CUDA_HOME
~/.bashrc
export CUDA_HOME=/usr/local/cuda-9.0
Check that the cuDNN works
$ cp -r /usr/src/cudnn_samples_v7/ $HOME
$ cd $HOME/cudnn_samples_v7/mnistCUDNN
$ make clean && make
$ ./mnistCUDNN
Test passed!
Stop LightDM
LightDM start to run when you install CUDA. If you use Ubuntu server, it's not necessary to run.
Disable LigthDM
Rewrite GRUB_CMDLINE_LINUX like below:
/etc/default/grub
GRUB_CMDLINE_LINUX="systemd.unit=multi-user.target"
Update and reboot.
sudo update-grub
sudo reboot
