ubuntu 16.04 安装 tensorflow-gpu 包括 CUDA ,CUDNN,CONDA

显卡驱动装好了，如图：

英文原文链接：

https://github.com/williamFalcon/tensorflow-gpu-install-ubuntu-16.04

英文内容：

Tensorflow GPU install on ubuntu 16.04

1. update apt-get

[code lang=bash]
sudo apt-get update
[/code]

1. Install apt-get deps

[code lang=bash]
sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev
[/code]

1. install nvidia drivers

[code lang=bash]
# The 16.04 installer works with 16.10.
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
apt-get update
apt-get install cuda -y
[/code]

2a. check nvidia driver install

[code lang=bash]
nvidia-smi

# you should see a list of gpus printed
# if not, the previous steps failed.
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1. install cuda toolkit (MAKE SURE TO SELECT N TO INSTALL NVIDIA DRIVERS)

[code lang=bash]
wget https://s3.amazonaws.com/personal-waf/cuda_8.0.61_375.26_linux.run
sudo sh cuda_8.0.61_375.26_linux.run # press and hold s to skip agreement

# Do you accept the previously read EULA?
# accept

# Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
# ************************* VERY KEY ****************************
# ******************** DON"T SAY Y ******************************
# n

# Install the CUDA 8.0 Toolkit?
# y

# Enter Toolkit Location
# press enter

# Do you want to install a symbolic link at /usr/local/cuda?
# y

# Install the CUDA 8.0 Samples?
# y

# Enter CUDA Samples Location
# press enter

# now this prints:
# Installing the CUDA Toolkit in /usr/local/cuda-8.0 …
# Installing the CUDA Samples in /home/liping …
# Copying samples to /home/liping/NVIDIA_CUDA-8.0_Samples now…
# Finished copying samples.
[/code]

1. Install cudnn

[code lang=bash]
wget https://s3.amazonaws.com/personal-waf/cudnn-8.0-linux-x64-v5.1.tgz
sudo tar -xzvf cudnn-8.0-linux-x64-v5.1.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
[/code]

1. Add these lines to end of ~/.bashrc:

[code lang=bash]
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
[/code]

1. Reload bashrc

[code lang=bash]
source ~/.bashrc
[/code]

1. Install miniconda

[code lang=bash]
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

# press s to skip terms

# Do you approve the license terms? [yes|no]
# yes

# Miniconda3 will now be installed into this location:
# accept the location

# Do you wish the installer to prepend the Miniconda3 install location
# to PATH in your /home/ghost/.bashrc ? [yes|no]
# yes

[/code]

1. Reload bashrc

[code lang=bash]
source ~/.bashrc
[/code]

1. Create conda env to install tf

[code lang=bash]
conda create -n tensorflow

# press y a few times
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1. Activate env

[code lang=bash]
source activate tensorflow
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1. Install tensorflow with GPU support for python 3.6

[code lang=bash]
# pip install –ignore-installed –upgrade aTFUrl
pip install –ignore-installed –upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
[/code]

1. Test tf install

[code lang=bash]
# start python shell
python

# run test script
import tensorflow as tf

hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
[/code]