centos 7深度学习环境部署

1.确认有gcc

gcc --version

2.识别kernel headers版本并安装

[root@A03-R07]# uname -r

3.10.0-327.28.3.el7.x86_64

yum install kernel-devel-3.10.0-327.28.3.el7.x86_64 kernel-headers-3.10.0-327.28.3.el7.x86_64

3.安装cuda:

sh cuda_8.0.61_375.26_linux.run

配置环境变量 /etc/profile添加:

export PATH=/usr/local/cuda/bin:$PATH

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH

验证安装成功:

任意目录下创建test文件夹

cuda-install-samples-8.0.sh test

进入test文件夹中的NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery

make

./deviceQuery

显示以下信息：

./deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 4 CUDA Capable device(s)

Device 0: "Tesla P40"

CUDA Driver Version / Runtime Version 8.0 / 8.0

.....

Device 1: "Tesla P40"

CUDA Driver Version / Runtime Version 8.0 / 8.0

....

4.安装cudnn

解压:tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz(6.0不行)

拷贝文件到指定位置:

cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include

cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64

chmod a+r /usr/local/cuda-8.0/include/cudnn.h /usr/local/cuda-8.0/lib64/libcudnn*

5.安装andconda

bash Anaconda2-4.3.1-Linux-x86_64.sh然后一直回车确认,环境将被安装到目录 /root/anaconda2 ,环境变量被安装到 /root/.bashrc

source /root/.bashrc 可使用环境

6.安装gensim

下载解压gensim到目录后 python setup.py install

依赖:bz2file 和 smart_open>1.2.1版本

7.安装结巴:

下载解压 python setup.py install

8.安装tensorflow

pip install tensorflow_gpu-1.1.0-cp27-none-linux_x86_64.whl

依赖bleach1.5.0(https://pypi.python.org/packages/99/00/25a8fce4de102bf6e3cc76bc4ea60685b2fee33bde1b34830c70cacc26a7/bleach-1.5.0.tar.gz) --> html5lib (https://pypi.python.org/packages/ae/ae/bcb60402c60932b32dfaf19bb53870b29eda2cd17551ba5639219fb5ebf9/html5lib-0.9999999.tar.gz#md5=ef43cb05e9e799f25d65d1135838a96f)
都用源码安装

-->Markdown-2.2.0

-->mock>=2.0.0(依赖 pbr>=1.3)

-->protobuf>=3.2.0

9.安装keras

python setup.py install