安装TensorFlow
anaconda
下载: python3.6版本
安装: sudo sh Anaconda3-5.2.0-Linux-x86_64.sh
tensorflow
从pypi下载已经编译好的tensorflow_gpu
tensorflow_gpu依赖的python package, 一部分可以在python uci package下载, 一部分仍在在pypi下载.
tensorflow_gpu-1.10.1-cp36-cp36m-manylinux1_x86_64.whl
absl_py-0.4.0-py2.py3-none-any.whl
astor-0.7.1-py2.py3-none-any.whl
gast-0.2.0-py2.py3-none-any.whl
grpcio-1.14.1-cp36-cp36m-manylinux1_x86_64.whl
msgpack-0.5.6-cp36-cp36m-manylinux1_x86_64.whl
numpy-1.14.5-cp36-cp36m-manylinux1_x86_64.whl(版本限制!)
protobuf-3.6.1-cp36-cp36m-manylinux1_x86_64.whl
termcolor-1.1.0-py2.py3-none-any.whl
tensorboard-1.10.0-py3-none-any.whl
Markdown-2.6.11-py2.py3-none-any.whl
安装: pip install XXX.whl
安装Cuda
下载: cuda9.0
- cuda 下载
-
安装:
sudo sh cuda_9.0.176_384.81_linux.run- 当提示安装
openGL, 若为双显卡且主显为非NVIDIA GPU, 则选择no - 安装cuda出现的问题. 因为cuda需要修改显卡驱动, 因此需要关闭图形界面.
- 当提示安装
# error:
It appears that an X server is running.
Please exit X before installation.
If you're sure that X is not running,
but are getting this error,
please delete any X lock files in /tmp.
# solution:
/etc/init.d/lightdm stop
# 然后重启
reboot
# refs: https://www.cnblogs.com/liyuanhong/articles/4919755.html
# 重启图形界面
sudo service lightdm start
或sudo lightdm restart
- 验证
- 输入
ls /dev/nvidia*- 若显示
/dev/nvidia0 /dev/nvidiactl /dev/nvidia-uvm, 则安装成功. - 若显示
ls: cannot access/dev/nvidia*: No such file or directory, 或/dev/nvidia0 /dev/nvidiactl(安装成功中显示中的一个或两个), 则需修改启动文件sudo vi /etc/rc.local- 把第一行的
#!/bin/sh -e中的-e去掉 - 然后在
exit 0前面插入下面运行脚本 - 输入脚本后, 运行
source /etc/rc.local更新 - 在运行
ls /dev/nvidia*将会看到三个文件, 则安装成功
- 若显示
- 输入
/sbin/modprobe nvidia
if [ "$?" -eq 0 ]; then
# Count the number of NVIDIA controllers found.
NVDEVS=`lspci | grep -i NVIDIA`
N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`
N=`expr $N3D + $NVGA - 1`
for i in `seq 0 $N`; do
mknod -m 666 /dev/nvidia$i c 195 $i
done
mknod -m 666 /dev/nvidiactl c 195 255
else
exit 1
fi
/sbin/modprobe nvidia-uvm
if [ "$?" -eq 0 ]; then
# Find out the major device number used by the nvidia-uvm driver
D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`
mknod -m 666 /dev/nvidia-uvm c $D 0
else
exit 1
fi
-
配置环境变量
- 运行
sudo gedit /etc/profile, 在末尾添加下面文本, 然后运行source /etc/profile更新
- 运行
export CUDA_HOME=/usr/local/cuda-9.0
export PATH=${PATH}:${CUDA_HOME}/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${CUDA_HOME}/lib64
- 安装完成后, 运行
nvidia-smi进行测试
安装cudnn. 这里需要注册一个NVIDIA账户
下载: cuDNN7.2.1
-
cudnn 下载
- 安装(第一个就可以了):
sudo dpkg -i libcudnn7_7.2.1.38-1+cuda9.0_amd64.deb
- 安装(第一个就可以了):
安装出现问题
# error:
/sbin/ldconfig.real: /usr/lib/nvidia-375/libEGL.so.1 is not a symbolic link
# solution:
https://askubuntu.com/questions/900285/libegl-so-1-is-not-a-symbolic-link
第二个脚本可用
安装完成后进行测试
- 运行
python
import tensorflow
print(tensorflow.__version__)
- 运行问题
2018-08-31 16:11:56.214798: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-08-31 16:11:56.217297: E tensorflow/stream_executor/cuda/cuda_driver.cc:397] failed call to cuInit: CUDA_ERROR_NO_DEVICE
2018-08-31 16:11:56.217342: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:157] no NVIDIA GPU device is present: /dev/nvidia0 does not exist
- 运行
nvidia-smi
Failed to initialize NVML: Driver/library version mismatch
- 原因分析:
- 安装cuda失败, 重新安装cuda, cuda的安装可以见cuda 安装.
- 安装的cuda版本不对, 则安装对应的cuda, 或者是cuda版本自动更新, 重启电脑解决.
参考
TensorFlow官方安装教程
cuda 安装
cudnn 安装
其他
代理上网
- windows安装
CCproxy - ubuntu设置环境变量
# proxy envir
MY_PROXY_URL=http://XXX.XX.XX.XX:808/
export ftp_proxy=${MY_PROXY_URL}
export http_proxy=${MY_PROXY_URL}
export https_proxy=${MY_PROXY_URL}
# proxy envir or
sudo apt-get -o Acquire::http::proxy="http://XXX.XX.XX.XX:808/" update
