如何检测ubuntu中是不是安装了cudnn

Posted 2023-04-05

命令行下输入mysql --help如果是输出一串帮助提示的话，那么就是安装好了的。没有的话就是没安装好。 参考技术A 我来回答，那个乱回答的是S b吧
$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

显示：
#define CUDNN_MAJOR 5
#define CUDNN_MINOR 1
#define CUDNN_PATCHLEVEL 5
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
CuDNN版本为 5.1.5

作者：鱼er
链接：https://www.jianshu.com/p/0234e6e00fdd
来源：简书
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。

ubuntu18.04+cuda9.0+cudnn7.1.4+caffe-ssd+anaconda2安装

几乎是小白一枚，折腾多天才搞定，参考了很多博客和社区问答，把安装经验记下来。

1.检测显卡，安装驱动

1 ~$ ubuntu-drivers devices
2 == /sys/devices/pci0000:00/0000:00:1c.4/0000:09:00.0 ==
3 modalias : pci:v000010DEd00001292sv00001028sd00000616bc03sc02i00
4 vendor : NVIDIA Corporation
5 model : GK208M [GeForce GT 740M]
6 driver : nvidia-340 - distro non-free
7 driver : nvidia-driver-390 - distro non-free recommended
8 driver : xserver-xorg-video-nouveau - distro free builtin

 选择后面带recommended的驱动

1 ~$ sudo apt install nvidia-driver-390

重启，在系统设置-详细信息的图形一栏查看gpu

2.安装CUDA和cuDNN

这方面攻略很多，我就不一一写了，CUDA9.0，共5个文件。cuDNN7.1.4：

cuDNN v7.1.4 Runtime Library for Ubuntu16.04 (Deb)

cuDNN v7.1.4 Developer Library for Ubuntu16.04 (Deb)

cuDNN v7.1.4 Code Samples and User Guide for Ubuntu16.04 (Deb)

gcc、g++降级

1 sudo apt-get install gcc-4.8 
2 sudo apt-get install g++-4.8

在usrin下

sudo rm  g++ ##删除原链接
sudo rm  gcc
sudo ln -s gcc-4.8 gcc ##重建链接
sudo ln -s g++-4.8 g++

下载好对应版本CUDA和cuDNN安装包（此处省略）

安装CUDA

由于前面已安装好驱动，在装CUDA时不用按ctrl+alt+f3切换到命令行界面，不用输入关闭一系列xxx的命令，只需在图形界面下运行.run文件

sudo sh cuda_9.0.176_384.81_linux.run

同意协议后，问是否安装显卡驱动，填no，剩下部分填yes或回车，剩下就没什么问题了。我安装后提示缺少几个文件，有点忘了，这个百度一下就能解决。之后再安装CUDA的几个补丁。

sudo sh cuda_9.0.176.1_linux.run ##共有四个补丁，这里只列一个

最后记得在~/.bashrc里加上这两条：

export PATH="/usr/local/cuda-9.0/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH"

安装cuDNN（省略）

 

3.安装anaconda2（省略）

4.安装caffe-ssd

安装各种依赖：

在/etc/apt下打开sources.list，源代码一项打钩，然后

sudo apt build-dep caffe-cuda

再按照之前办法安装gcc5，g++5，修改链接，之后要用g++5编译caffe

git下载caffe-ssd源代码

git clone https://github.com/weiliu89/caffe.git
cd caffe
git checkout ssd
cp Makefile.config.example Makefile.config

修改Makefile.config，这里贴上我的配置：

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
 USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#    You should not set this flag if you will be reading LMDBs with any
#    possibility of simultaneous read and write
 ALLOW_LMDB_NOLOCK := 1

# Uncomment if you‘re using OpenCV 3
 OPENCV_VERSION := 3

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the lines after *_35 for compatibility.
 #           -gencode arch=compute_20,code=sm_20 
 #           -gencode arch=compute_20,code=sm_21 
CUDA_ARCH := -gencode arch=compute_30,code=sm_30              -gencode arch=compute_35,code=sm_35 
#            -gencode arch=compute_50,code=sm_50 
#            -gencode arch=compute_52,code=sm_52 
#            -gencode arch=compute_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
 BLAS := atlas
#BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
#PYTHON_INCLUDE := /usr/include/python2.7 
        /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it‘s in root.
 ANACONDA_HOME := $(HOME)/anaconda2
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include         $(ANACONDA_HOME)/include/python2.7         $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include 
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.6m
# PYTHON_INCLUDE := /usr/include/python3.6 
#                 /usr/lib/python3/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
# PYTHON_LIB := /usr/lib
 PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c ‘import numpy.core; print(numpy.core.__file__)‘))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
 WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that ‘make runtest‘ will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

安装caffe

在caffe根目录下

make -j8
make py
make test -j8
make runtest -j8

make runtest -j8如果出现这种error：

.build_release/tools/caffe: error while loading shared libraries: 
libhdf5_hl.so.100: cannot open shared object file: No such file or directory

 

，就在.bashrc中加上：

export LD_LIBRARY_PATH="/home/.../anaconda2/lib:$LD_LIBRARY_PATH"

在caffe根目录下的：

source ~/.bashrc
make runtest -j8

应该能通过，那么caffe-ssd就安装好了。

之后如果import caffe时报错：No module named caffe，就在.bashrc文件加上：

export PYTHONPATH="/home/.../caffe/python"

如果跑代码时出现 no module named google.protobuf，用：

conda install protobuf

到这里安装就完成了。

其实我本来配置的是caffe+anaconda3，但ssd貌似是用python2写的，python3跑不起来？所以就换了anaconda2。这里也顺便说下caffe+anaconda3配置：

装好anaconda3后，caffe下Makefile.config为:

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
 USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#    You should not set this flag if you will be reading LMDBs with any
#    possibility of simultaneous read and write
 ALLOW_LMDB_NOLOCK := 1

# Uncomment if you‘re using OpenCV 3
 OPENCV_VERSION := 3

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the lines after *_35 for compatibility.
 #           -gencode arch=compute_20,code=sm_20 
 #           -gencode arch=compute_20,code=sm_21 
CUDA_ARCH := -gencode arch=compute_30,code=sm_30              -gencode arch=compute_35,code=sm_35 
#            -gencode arch=compute_50,code=sm_50 
#            -gencode arch=compute_52,code=sm_52 
#            -gencode arch=compute_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
 BLAS := atlas
#BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
#PYTHON_INCLUDE := /usr/include/python2.7 
        /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it‘s in root.
 ANACONDA_HOME := $(HOME)/anaconda3
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include         $(ANACONDA_HOME)/include/python3.6m         $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include 
# Uncomment to use Python 3 (default is Python 2)
 PYTHON_LIBRARIES := boost_python3 python3.6m
# PYTHON_INCLUDE := /usr/include/python3.6 
#                 /usr/lib/python3/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
# PYTHON_LIB := /usr/lib
 PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c ‘import numpy.core; print(numpy.core.__file__)‘))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
 WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that ‘make runtest‘ will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

还是用g++5，make前，打开.bashrc，要把/home/.../anaconda/lib从LD_LIBRARY_PATH中删去。在make过程中如果有boost::xxx 未定义，去boost官网下载源码，找找相关教程编译安装好，然后在caffe根目录下：

make clean
make -j8

应该就可以了，后续如果有bug可参考前面。