#系統啟動時提示nouveauerror: unkown chipset
# nouveau無法識別GTX1080
-禁用nouveau
vi /etc/modprobe.d/blacklist.conf
#添加：
blacklist nouveau
sudo update-initramfs -u
sudo reboot
#準備系統環境
sudo apt-get install build-essential wget
#安裝gcc g++ 4.8
sudo apt-get install gcc-4.8gcc-4.8-multilib g++-4.8 g++-4.8-multilib
sudo update-alternatives --install/usr/bin/gcc gcc /usr/bin/gcc-5 60
sudo update-alternatives --install/usr/bin/gcc gcc /usr/bin/gcc-4.8 50
sudo update-alternatives --install/usr/bin/g++ g++ /usr/bin/g++-5 60
sudo update-alternatives --install/usr/bin/g++ g++ /usr/bin/g++-4.8 50
#切換gcc g++版本
sudo update-alternatives --config gcc
sudo update-alternatives --config g++
#移除gcc g++ 4.8
# sudo update-alternatives --remove gcc/usr/bin/gcc-4.8
# sudo update-alternatives --remove g++/usr/bin/g++-4.8
# CUDA 8.0RC
#https://developer.nvidia.com/cuda-release-candidate-download
#安裝cuda toolkit
#切換到gcc-4.8
sudo dpkg -icuda-repo-ubuntu1604-8-0-rc_8.0.27-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
#配置環境變量
echo "exportCUDA_HOME=/usr/local/cuda" >> ~/.bashrc
echo "exportPATH=/usr/local/cuda/bin:$PATH" >> ~/.bashrc
echo "exportLD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH" >> ~/.bashrc
#安裝cuDNN
tar -xf cudnn-8.0-linux-x64-v5.0-ga.tgz
sudo cp -f cuda/lib64/*.*/usr/local/cuda/lib64/
sudo cp -f cuda/include/*.*/usr/local/cuda/include/
#注意：GeForce GTX1080 Developers must re-install the latest driver from www.nvidia.com/driversafter installing any of these CUDA Toolkits.
#注意：gcc-4.8無法編譯nvidia driver
#切換到gcc-5
sudo sh NVIDIA-Linux-x86_64-*.run
#卸載驅動：sudonvidia-uninstall
#測試
cd/usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery
# modprobe: ERROR: could not insert'nvidia_361_uvm': Invalid argument
#這是因為cuda8.0自帶了361版本的nvidia driver，需要將其卸載
sudo apt-getremove nvidia-361
The following packages will be REMOVED:
cuda cuda-8-0 cuda-demo-suite-8-0cuda-drivers cuda-runtime-8-0 nvidia-361 nvidia-361-dev
0 upgraded, 0 newly installed, 7 to removeand 76 not upgraded.
After this operation, 312 MB disk spacewill be freed.
Do you want to
continue? [Y/n] y（別怕，沒問題）
sudo reboot(重啟顯示有問題，可能無法進入桌面)
Crtl+Alt+F1
sudo apt-add-repository ppa:graphics-drivers/ppa -y
sudo apt update
sudo apt install nvidia-367 nvidia-settingsnvidia-prime
sudo reboot
現在能正常進入桌面了
# Tensorflow 0.9.0 build from source
#安裝bazel
sudo apt-get install openjdk-8-jdk
echo "debhttp://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee/etc/apt/sources.list.d/bazel.list
curlhttps://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add-
sudo apt-get update
sudo apt-get install bazel
#編譯tensorflow
sudo apt-get install python-numpy swigpython-dev
mkdir ~/github && cd ~/github
git clone --recurse-submoduleshttps://github.com/tensorflow/tensorflow
cd ~/github/tensorflow &&./configure
---------------------------------------
Please specify the location of python.[Default is /usr/bin/python]:
Do you wish to build TensorFlow with GoogleCloud Platform support? [y/N] n
No Google Cloud Platform support will beenabled for TensorFlow
Do you wish to build TensorFlow with GPUsupport? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use asthe host compiler. [Default is /usr/bin/gcc]:
Please specify the Cuda SDK version youwant to use, e.g. 7.0. [Leave empty to use system default]: 8.0
Please specify the location where CUDA 8.0toolkit is installed. Refer to README.md for more details. [Default is/usr/local/cuda]:
Please specify the Cudnn version you wantto use. [Leave empty to use system default]: 5 (not 5.0)
Please specify the location where cuDNN 5library is installed. Refer to README.md for more details. [Default is/usr/local/cuda]:
Please specify a list of comma-separatedCuda compute capabilities you want to build with.
You can find the compute capability of yourdevice at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional computecapability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]:
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished
---------------------------------------
bazel build -c opt --config=cuda//tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package/tmp/tensorflow_pkg
sudo pip install/tmp/tensorflow_pkg/tensorflow-…
#測試
python -c "import tensorflow"
# ImportError: cannot import name
pywrap_tensorflow：需要重啟
sudo reboot
# Theano & keras
sudo apt-get install python-numpypython-scipy python-dev python-pip python-nose libopenblas-dev git
sudo pip install Theano
sudo pip install keras
#配置Theano
echo "[global]" > ~/.theanorc
echo "floatX = float32" >>~/.theanorc
echo "device = gpu0" >>~/.theanorc
echo "[nvcc]" >>~/.theanorc
echo "fastmath = True" >>~/.theanorc
#測試
python -c "import keras"
# matplotlib
sudo apt-get build-dep python-matplotlib
# E: You must put some 'source' URIs inyour sources.list
sudo vi /etc/apt/sources.list
#去掉所有deb-src前面的#號
sudo apt-get update
sudo pip install matplotlib
# h5py
sudo apt-get install libhdf5-dev
sudo apt-get install cython
sudo pip install h5py
# Docker
# Update apt sources
sudo apt-get update
sudo apt-get install apt-transport-httpsca-certificates
sudo apt-key adv --keyserverhkp://p80.pool.sks-keyservers.net:80 --recv-keys58118E89F3A912897C070ADBF76221572C52609D
sudo vi /etc/apt/sources.list.d/docker.list
#添加（14.04）：
deb https://apt.dockerproject.org/repoubuntu-trusty main
#添加（16.04）：
deb https://apt.dockerproject.org/repoubuntu-xenial main
sudo apt-get update
sudo apt-get install docker-engine
sudo service docker start
# add user group
sudo groupadd docker
sudo usermod -aG docker [your username]
