centos 7深度學習環境部署
阿新 • • 發佈：2019-01-29
1.確認有gcc
gcc --version
2.識別kernel headers版本並安裝
[[email protected]]# 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.安裝cuda:
sh cuda_8.0.61_375.26_linux.run
配置環境變數 /etc/profile新增:
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
驗證安裝成功:
任意目錄下建立test資料夾
cuda-install-samples-8.0.sh test
進入test資料夾中的NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery
make
./deviceQuery
顯示以下資訊：
./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.安裝cudnn
解壓:tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz(6.0不行)
拷貝檔案到指定位置:
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.安裝andconda
bash Anaconda2-4.3.1-Linux-x86_64.sh然後一直回車確認,環境將被安裝到目錄 /root/anaconda2 ,環境變數被安裝到 /root/.bashrc
source /root/.bashrc 可使用環境
6.安裝gensim
下載解壓gensim到目錄後 python setup.py install
依賴:bz2file 和 smart_open>1.2.1版本
7.安裝結巴:
下載解壓 python setup.py install
8.安裝tensorflow
pip install tensorflow_gpu-1.1.0-cp27-none-linux_x86_64.whl
依賴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) 都用原始碼安裝
-->Markdown-2.2.0
-->mock>=2.0.0(依賴 pbr>=1.3)
-->protobuf>=3.2.0
9.安裝keras
python setup.py install
gcc --version
2.識別kernel headers版本並安裝
[[email protected]]# 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.安裝cuda:
sh cuda_8.0.61_375.26_linux.run
配置環境變數 /etc/profile新增:
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
驗證安裝成功:
任意目錄下建立test資料夾
cuda-install-samples-8.0.sh test
進入test資料夾中的NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery
make
./deviceQuery
顯示以下資訊：
./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.安裝cudnn
解壓:tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz(6.0不行)
拷貝檔案到指定位置:
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.安裝andconda
bash Anaconda2-4.3.1-Linux-x86_64.sh然後一直回車確認,環境將被安裝到目錄 /root/anaconda2 ,環境變數被安裝到 /root/.bashrc
source /root/.bashrc 可使用環境
6.安裝gensim
下載解壓gensim到目錄後 python setup.py install
依賴:bz2file 和 smart_open>1.2.1版本
7.安裝結巴:
下載解壓 python setup.py install
8.安裝tensorflow
pip install tensorflow_gpu-1.1.0-cp27-none-linux_x86_64.whl
依賴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) 都用原始碼安裝
-->Markdown-2.2.0
-->mock>=2.0.0(依賴 pbr>=1.3)
-->protobuf>=3.2.0
9.安裝keras
python setup.py install