Tensorflow的GPU版本安装

• 查看你电脑是否有英伟达的显卡

sudo lshw -numeric -c display
​ *-display
​ description: 3D controller
​ product: NVIDIA Corporation [10DE:1C20]
​ vendor: NVIDIA Corporation [10DE]
​ physical id: 0
​ bus info: pci@0000:01:00.0
​ version: a1
​ width: 64 bits
​ clock: 33MHz
​ capabilities: pm msi pciexpress bus_master cap_list rom
​ configuration: driver=nvidia latency=0
​ resources: irq:137 memory:93000000-93ffffff memory:50000000-5fffffff memory:60000000-61ffffff ioport:4000(size=128) memory:94000000-9407ffff
​ *-display
​ description: VGA compatible controller
​ product: Intel Corporation [8086:591B]
​ vendor: Intel Corporation [8086]
​ physical id: 2
​ bus info: pci@0000:00:02.0
​ version: 04
​ width: 64 bits
​ clock: 33MHz
​ capabilities: pciexpress msi pm vga_controller bus_master cap_list rom
​ configuration: driver=i915_bpo latency=0
​ resources: irq:126 memory:92000000-92ffffff memory:a0000000-afffffff ioport:5000(size=64)

• 找出你的系统应该安装的英伟达显卡驱动

ubuntu-drivers devices
​ == /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
​ vendor : NVIDIA Corporation
​ modalias : pci:v000010DEd00001C20sv00001025sd0000120Cbc03sc02i00
​ driver : xserver-xorg-video-nouveau - distro free builtin
​ driver : nvidia-384 - third-party non-free recommended

• 安装该驱动

sudo apt install nvidia-384
​ 正在读取软件包列表… 完成
​ 正在分析软件包的依赖关系树
​ 正在读取状态信息… 完成
​ nvidia-384 已经是最新版 (384.130-0ubuntu0.16.04.1)。
​ 升级了 0 个软件包，新安装了 0 个软件包，要卸载 0 个软件包，有 2 个软件包未被升级。

• 验证是否安装成功

nvidia-smi

• 安装cuda并验证

wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

• 配置环境变量

gedit ~/.bashrc
export PATH=/usr/local/cuda-8.0/bin{PATH:+:{PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64{LD_LIBRARY_PATH:+:{LD_LIBRARY_PATH}}
source ~/.bashrc
nvcc -V

    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 8.0, V8.0.61

• 安装cudnn

wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v5.1/prod_20161129/8.0/cudnn-8.0-linux-x64-v5.1-tgz
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*

• 下载安装gpu版本的tensorflow

conda install tensorflow-gpu
python model.py

	gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
	sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
	2018-11-29 17:58:25.345884: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: 
	name: GeForce GTX 1060
	major: 6 minor: 1 memoryClockRate (GHz) 1.733
	pciBusID 0000:01:00.0
	Total memory: 5.93GiB
	Free memory: 5.46GiB