深度学习21天实战caffe学习笔记《3 :准备Caffe环境》
2017-07-14 20:33
489 查看
准备Caffe环境[b]【如果是其他环境下的配置就请绕道喽,我也没有专门去试一试各个环境下的配置,请谅解~】
[/b]官网 http://caffe.berkeleyvision.org/installation.html; 首先在这里介绍一下我的硬件环境:Ubuntu 14.04 ---------[ win10远程连接ssh(putty)+VNC ]:http://www.ubuntu.com/download/desktop
gcc 4.8.4 || cuda 7.5.18 || 1、安装ssh、vnc;win10 : putty 、 VNC Unbuntu : sudo apt-get install ssh[b] [/b]
文件: cuda_7.5.18_linux.run
登录界面前按Ctrl+Alt+F1进入命令提示符 【禁用nouveau驱动】
执行命令: sudo vi /etc/modprobe.d/blacklist-nouveau.conf
输入以下内容
执行命令: sudo update-initramfs -u
再执行命令: lspci | grep nouveau 查看是否有内容
如果没有内容 ,说明禁用成功,如果有内容,就重启一下再查看
sudo reboot
重启后,进入登录界面的时候,不要登录进入桌面,直接按Ctrl+Alt+F1进入命令提示符。
重启后,登录界面时直接按Ctrl+Alt+F1进入命令提示符
安装依赖项:
sudo service lightdm stop
sudo apt-get install g++
sudo apt-get install Git
sudo apt-get install freeglut3-dev
假设cuda_7.5.18_linux.run位于 ~ 目录,切换到~目录: cd ~
执行命令: sudo sh cuda_7.5.18_linux.run
安装的时候,要让你先看一堆文字(EULA),我们直接不停的按空格键到100%,然后输入一堆accept,yes,yes或回车进行安装。安装完成后,重启,然后用ls查看一下:
ls /dev/nvidia*
会看到/dev目录下生成多个nvidia开头文件(夹)
或者输入命令: sudo nvcc –version 会显示类似以下信息
2
3
4
5
1
2
3
4
5
配置环境变量
执行命令: sudo vi /etc/profile
文件底部添加以下内容:
2
1
2
编译samples
安装成功后在~目录下可以看
1bf01
到一个NVIDIA_CUDA-7.5_Samples文件夹,切换到目录
输入sudo make, 大概等个十多分钟后就会把全部的samples编译完毕。生成的可执行文件位于
NVIDIA_CUDA-7.5_Samples/bin/x86_64/Linux/release 目录下
比如运行 ./nbody可以看到以下demo
![](https://oscdn.geek-share.com/Uploads/Images/Content/202010/30/233e7e38c4a2a0b1c6599cff9862dc83)
g++ : Depends: g++-4.8 (>= 4.8.2-5~) but it is not going to be installed
是因为ubuntu 14.04的源过旧或不可访问导致,可以通过更新源解决。
首先,备份原始源文件source.list
sudo cp /etc/apt/sources.list /etc/apt/sources.list_backup
然后
sudo gedit /etc/apt/source.list
在文件尾部添加以下内容deb http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-security main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-updates main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-proposed main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-backports main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-security main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-updates main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-proposed main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-backports main restricted universe multivers
最后 sudo apt-get update
2
3
4
5
6
1
2
3
4
5
6
2
1
2
注意最后的一串密钥就是报错信息里的, 每个人的不一样
2
3
4
安装依赖项:sudo apt-get install libatlas-base-dev
sudo apt-get install libprotobuf-dev
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev
sudo apt-get install libopencv-dev
sudo apt-get install libboost-all-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev
sudo apt-get install protobuf-compiler
编译caffe
cd ~/caffe
sudo cp Makefile.config.example Makefile.config
make all
配置运行环境
sudo vi /etc/ld.so.conf.d/caffe.conf
添加内容:
/usr/local/cuda/lib64
更新配置
sudo ldconfig
caffe测试,执行以下命令:
cd ~/caffe
sudo sh data/mnist/get_mnist.sh
sudo sh examples/mnist/create_mnist.sh
最后测试:
sudo sh examples/mnist/train_lenet.sh
运行结果如下:
![](https://oscdn.geek-share.com/Uploads/Images/Content/202010/30/afb5479f250b8826c345568b91419126)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
可以看到诸如# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1
# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas都是使用默认的设置,我们可以安装其他依赖项提高caffe运行效率
Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南 作者 Xin-Yu Ou(欧新宇) 可以到他的网盘中下载
PS:为了方便大家使用,我提供一个百度云盘,用于分享部分安装过程中需要用到的软件包和链接地址(所有软件包仅供学术交流使用,请大家尽量去官网下载。)。百度云盘链接:http://pan.baidu.com/s/1qX1uFHa 密码:wysa
在Install-opencv-master文件夹中包含安装各个版本opencv脚本
切换到目录执行:
sudo sh Ubuntu/dependencies.sh
安装依赖项
执行opencv3.0安装脚本
sudo sh Ubuntu/3.0/opencv3_0_0.sh
等待安装完成即可
修改Makefile.config# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
(可选)opencv3.1已经发布,如果要安装最新的opencv3.1,我们可以先执行
sudo sh get_latest_version_download_file.sh
获取最新的地址,然后更新opencv3_0_0.sh中的下载地址,同时需要修正文件名等
sudo sh Ubuntu/3.0/opencv3_0_0.sh
出现有个地方一直卡住了,显示在下载一个文件: ippicv_linux_20141027.tgz
因为墙的原因,这个文件无法下载下来
[其他文档] ippicv_linux_20141027.tgz 处下载文件 ippicv_linux_20141027.tgz
下载后拷贝到opencv/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/ 目录下即
http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation
网址: https://software.intel.com/en-us/intel-education-offerings
Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南 作者 Xin-Yu Ou(欧新宇) 可以到他的网盘中下载, 需要自己申请序列号
下载完成后: parallel_studio_xe_2016.tgz
执行以下命令:
$ tar zxvf parallel_studio_xe_2016.tar.gz$ chmod a+x parallel_studio_xe_2016 -R$ sh install_GUI.sh
环境配置:
$ sudo gedit /etc/ld.so.conf.d/intel_mkl.conf
然后添加以下内容
安装MKL完成
修改Makefile.config # BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := mkl
下载地址:https://developer.nvidia.com/cudnn
或者到网盘: http://pan.baidu.com/s/1bnOKBO 下载
下载相应文件cudnn-7.0-linux-x64-v4.0-rc.tgz, 放到~根目录下
切换到~目录,执行命令
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
修改Makefile.config
NVCC src/caffe/layers/deconv_layer.cu
NVCC src/caffe/layers/cudnn_conv_layer.cu
src/caffe/layers/cudnn_conv_layer.cu(81): error: argument of type "cudnnAddMode_t" is incompatible with parameter of type "const void *"
detected during instantiation of "void caffe::CuDNNConvolutionLayer<Dtype>::Forward_gpu(const std::vector<caffe::Blob<Dtype> *, std::allocator<caffe::Blob<Dtype> *>> &, const std::vector<caffe::Blob<Dtype> *, std::allocator<caffe::Blob<Dtype> *>> &) [with Dtype=float]"
(157): here
...
20 errors detected in the compilation of "/tmp/tmpxft_00002703_00000000-16_cudnn_conv_layer.compute_50.cpp1.ii".
make: *** [.build_release/cuda/src/caffe/layers/cudnn_conv_layer.o] Error 1
make: *** Waiting for unfinished jobs....
$ sudo cp lib* /usr/local/cuda/lib64/
$ cd ../include/
$ sudo cp cudnn.h /usr/local/cuda/include/
$ cd /usr/local/cuda/lib64/
$ sudo rm -r libcudnn.so libcudnn.so.7.0
$ sudo ln -sf libcudnn.so.7.0.64 libcudnn.so.7.0
$ sudo ln -sf libcudnn.so.7.0 libcudnn.so
$ sudo ldconfig
sudo make all
sample测试: ( 比不使用cudnn快很多)
sh data/mnist/get_mnist.sh
sh examples/mnist/create_mnist.sh
我们可以将迭代次数增加到50000次
sudo gedit examples/mnist/lenet_solver.prototxt
修改max_iter: 50000
最后:
sh examples/mnist/train_lenet.sh
![](https://oscdn.geek-share.com/Uploads/Images/Content/202010/30/2ca67e8d4a4b3c3a5b58394ccded5ab9)
1
2
3
4
1
2
3
4
sh /usr/local/MATLAB/R2014a/bin/matlab
Makefile.config 中修改 : MATLAB_DIR := /usr/local/MATLAB/R2014a
sudo make matcaffe -j8
搜狗输入法安装
Ubuntu14.04安装搜狗输入法
im-config 然后 ibus选取fcitx
fcitx-config-gtk3
Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南
ubuntu 14.04 install g++问题"g++:Depends:g++-4.8(>= 4.8.2-5
ippicv_linux_20141027.tgz
http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation
[/b]官网 http://caffe.berkeleyvision.org/installation.html; 首先在这里介绍一下我的硬件环境:Ubuntu 14.04 ---------[ win10远程连接ssh(putty)+VNC ]:http://www.ubuntu.com/download/desktop
gcc 4.8.4 || cuda 7.5.18 || 1、安装ssh、vnc;win10 : putty 、 VNC Unbuntu : sudo apt-get install ssh[b] [/b]
cuda7.5安装
cuda7.5下载:地址 https://developer.nvidia.com/cuda-downloads文件: cuda_7.5.18_linux.run
登录界面前按Ctrl+Alt+F1进入命令提示符 【禁用nouveau驱动】
执行命令: sudo vi /etc/modprobe.d/blacklist-nouveau.conf
输入以下内容
blacklist nouveau options nouveau modset=0最后保存退出(:wq)
执行命令: sudo update-initramfs -u
再执行命令: lspci | grep nouveau 查看是否有内容
如果没有内容 ,说明禁用成功,如果有内容,就重启一下再查看
sudo reboot
重启后,进入登录界面的时候,不要登录进入桌面,直接按Ctrl+Alt+F1进入命令提示符。
重启后,登录界面时直接按Ctrl+Alt+F1进入命令提示符
安装依赖项:
sudo service lightdm stop
sudo apt-get install g++
sudo apt-get install Git
sudo apt-get install freeglut3-dev
假设cuda_7.5.18_linux.run位于 ~ 目录,切换到~目录: cd ~
执行命令: sudo sh cuda_7.5.18_linux.run
安装的时候,要让你先看一堆文字(EULA),我们直接不停的按空格键到100%,然后输入一堆accept,yes,yes或回车进行安装。安装完成后,重启,然后用ls查看一下:
ls /dev/nvidia*
会看到/dev目录下生成多个nvidia开头文件(夹)
或者输入命令: sudo nvcc –version 会显示类似以下信息
dl@dl-Z170X-Gaming-3:~$ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2015 NVIDIA Corporation Built on Tue_Aug_11_14:27:32_CDT_2015 Cuda compilation tools, release 7.5, V7.5.171
2
3
4
5
1
2
3
4
5
配置环境变量
执行命令: sudo vi /etc/profile
文件底部添加以下内容:
export PATH=/usr/local/cuda-7.5/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH1
2
1
2
编译samples
安装成功后在~目录下可以看
1bf01
到一个NVIDIA_CUDA-7.5_Samples文件夹,切换到目录
输入sudo make, 大概等个十多分钟后就会把全部的samples编译完毕。生成的可执行文件位于
NVIDIA_CUDA-7.5_Samples/bin/x86_64/Linux/release 目录下
比如运行 ./nbody可以看到以下demo
cuda安装过程中遇到的问题
Q1
–在执行命令: sudo apt-get install g++ 时出现以下错误g++ : Depends: g++-4.8 (>= 4.8.2-5~) but it is not going to be installed
是因为ubuntu 14.04的源过旧或不可访问导致,可以通过更新源解决。
首先,备份原始源文件source.list
sudo cp /etc/apt/sources.list /etc/apt/sources.list_backup
然后
sudo gedit /etc/apt/source.list
在文件尾部添加以下内容deb http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-security main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-updates main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-proposed main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-backports main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-security main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-updates main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-proposed main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-backports main restricted universe multivers
最后 sudo apt-get update
Q2
W: GPG 错误:http://archive.ubuntukylin.com:10006/ubuntukylin xenial InRelease: 由于没有公钥,无法验证下列签名: NO_PUBKEY 8D5A09DC9B929006 W: 仓库 “http://archive.ubuntukylin.com:10006/ubuntukylin xenial InRelease” 没有数字签名。 N: 无法认证来自该源的数据,所以使用它会带来潜在风险。 N: 参见 apt-secure(8) 手册以了解仓库创建和用户配置方面的细节。 W: 以下 ID 的密钥没有可用的公钥: 8D5A09DC9B9290061
2
3
4
5
6
1
2
3
4
5
6
solution:
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 8D5A09DC9B9290061
2
1
2
注意最后的一串密钥就是报错信息里的, 每个人的不一样
Q3
1080Ti显卡安装完CUDA之后要安装显卡驱动,否则提示找不到显卡设备sudo service lightdm stop sudo chmod 777 NVIDIA-Linux-x86_64-378.13.run sudo ./NVIDIA-Linux-x86_64-367.27.run sudo service lightdm start1
2
3
4
安装caffe
下载caffe:执行命令: sudo git clone https://github.com/BVLC/caffe.git安装依赖项:sudo apt-get install libatlas-base-dev
sudo apt-get install libprotobuf-dev
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev
sudo apt-get install libopencv-dev
sudo apt-get install libboost-all-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev
sudo apt-get install protobuf-compiler
编译caffe
cd ~/caffe
sudo cp Makefile.config.example Makefile.config
make all
配置运行环境
sudo vi /etc/ld.so.conf.d/caffe.conf
添加内容:
/usr/local/cuda/lib64
更新配置
sudo ldconfig
caffe测试,执行以下命令:
cd ~/caffe
sudo sh data/mnist/get_mnist.sh
sudo sh examples/mnist/create_mnist.sh
最后测试:
sudo sh examples/mnist/train_lenet.sh
运行结果如下:
其他依赖项
我们查看caffe目录下 Makefile.config 内容如下:## 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 *_50 lines for compatibility. CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \ -gencode arch=compute_20,code=sm_21 \ -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=sm_50 \ -gencode arch=compute_50,code=compute_50 # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := mkl # 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)/anaconda # 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 python3.5m # PYTHON_INCLUDE := /usr/include/python3.5m \ # /usr/lib/python3.5/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 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib # 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 $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
可以看到诸如# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1
# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas都是使用默认的设置,我们可以安装其他依赖项提高caffe运行效率
opencv3.0安装
github上有人写好完整的运行脚本自动下载OpenCV,编译,安装,配置等Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南 作者 Xin-Yu Ou(欧新宇) 可以到他的网盘中下载
PS:为了方便大家使用,我提供一个百度云盘,用于分享部分安装过程中需要用到的软件包和链接地址(所有软件包仅供学术交流使用,请大家尽量去官网下载。)。百度云盘链接:http://pan.baidu.com/s/1qX1uFHa 密码:wysa
在Install-opencv-master文件夹中包含安装各个版本opencv脚本
切换到目录执行:
sudo sh Ubuntu/dependencies.sh
安装依赖项
执行opencv3.0安装脚本
sudo sh Ubuntu/3.0/opencv3_0_0.sh
等待安装完成即可
修改Makefile.config# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
(可选)opencv3.1已经发布,如果要安装最新的opencv3.1,我们可以先执行
sudo sh get_latest_version_download_file.sh
获取最新的地址,然后更新opencv3_0_0.sh中的下载地址,同时需要修正文件名等
arch=$(uname -m) if [ "$arch" == "i686" -o "$arch" == "i386" -o "$arch" == "i486" -o "$arch" == "i586" ]; then flag=1 else flag=0 fi echo "Installing OpenCV 3.0.0" mkdir OpenCV cd OpenCV echo "Removing any pre-installed ffmpeg and x264" sudo apt-get -y remove ffmpeg x264 libx264-dev echo "Installing Dependenices" sudo apt-get -y install libopencv-dev sudo apt-get -y install build-essential checkinstall cmake pkg-config yasm sudo apt-get -y install libtiff4-dev libjpeg-dev libjasper-dev sudo apt-get -y install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev sudo apt-get -y install python-dev python-numpy sudo apt-get -y install libtbb-dev sudo apt-get -y install libqt4-dev libgtk2.0-dev sudo apt-get -y install libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev sudo apt-get -y install x264 v4l-utils ffmpeg sudo apt-get -y install libgtk2.0-dev echo "Downloading OpenCV 3.0.0" wget -O opencv-3.0.0.zip http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/3.0.0/opencv-3.0.0.zip/download echo "Installing OpenCV 3.0.0" unzip opencv-3.0.0.zip cd opencv-3.0.0 mkdir build cd build cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_QT=ON -D WITH_OPENGL=ON .. make -j8 sudo make install sudo sh -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf' sudo ldconfig echo "OpenCV 3.0.0 ready to be used"
安装opencv3遇到的问题
在执行sudo sh Ubuntu/3.0/opencv3_0_0.sh
出现有个地方一直卡住了,显示在下载一个文件: ippicv_linux_20141027.tgz
因为墙的原因,这个文件无法下载下来
[其他文档] ippicv_linux_20141027.tgz 处下载文件 ippicv_linux_20141027.tgz
下载后拷贝到opencv/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/ 目录下即
http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation
安装BLAS——选择MKL
首先下载 MKL(Intel(R) Parallel Studio XE Cluster Edition for Linux 2016)网址: https://software.intel.com/en-us/intel-education-offerings
Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南 作者 Xin-Yu Ou(欧新宇) 可以到他的网盘中下载, 需要自己申请序列号
下载完成后: parallel_studio_xe_2016.tgz
执行以下命令:
$ tar zxvf parallel_studio_xe_2016.tar.gz$ chmod a+x parallel_studio_xe_2016 -R$ sh install_GUI.sh
环境配置:
$ sudo gedit /etc/ld.so.conf.d/intel_mkl.conf
然后添加以下内容
/opt/intel/lib/intel64 /opt/intel/mkl/lib/intel64配置生效: sudo ldconfig -v
安装MKL完成
修改Makefile.config # BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := mkl
cuDNN安装
cudnn下载下载地址:https://developer.nvidia.com/cudnn
或者到网盘: http://pan.baidu.com/s/1bnOKBO 下载
下载相应文件cudnn-7.0-linux-x64-v4.0-rc.tgz, 放到~根目录下
切换到~目录,执行命令
sudo tar xvf cudnn-7.0-linux-x64-v4.0-rc.tgz cd cuda/include sudo cp *.h /usr/local/include/ cd ../lib64 sudo cp lib* /usr/local/lib/ cd /usr/local/lib sudo chmod +r libcudnn.so.4.0.4 sudo ln -sf libcudnn.so.4.0.4 libcudnn.so.4 sudo ln -sf libcudnn.so.4 libcudnn.so sudo ldconfig1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
修改Makefile.config
# cuDNN acceleration switch (uncomment to build with cuDNN). USE_CUDNN := 1
cudnn版本问题
在make工程的时候出现以下错误:...NVCC src/caffe/layers/deconv_layer.cu
NVCC src/caffe/layers/cudnn_conv_layer.cu
src/caffe/layers/cudnn_conv_layer.cu(81): error: argument of type "cudnnAddMode_t" is incompatible with parameter of type "const void *"
detected during instantiation of "void caffe::CuDNNConvolutionLayer<Dtype>::Forward_gpu(const std::vector<caffe::Blob<Dtype> *, std::allocator<caffe::Blob<Dtype> *>> &, const std::vector<caffe::Blob<Dtype> *, std::allocator<caffe::Blob<Dtype> *>> &) [with Dtype=float]"
(157): here
...
20 errors detected in the compilation of "/tmp/tmpxft_00002703_00000000-16_cudnn_conv_layer.compute_50.cpp1.ii".
make: *** [.build_release/cuda/src/caffe/layers/cudnn_conv_layer.o] Error 1
make: *** Waiting for unfinished jobs....
解决方案:
更换V3版本cudnn Caffe 工程的一些编译错误以及解决方案$ cd lib64/$ sudo cp lib* /usr/local/cuda/lib64/
$ cd ../include/
$ sudo cp cudnn.h /usr/local/cuda/include/
$ cd /usr/local/cuda/lib64/
$ sudo rm -r libcudnn.so libcudnn.so.7.0
$ sudo ln -sf libcudnn.so.7.0.64 libcudnn.so.7.0
$ sudo ln -sf libcudnn.so.7.0 libcudnn.so
$ sudo ldconfig
重新编译测试caffe
编译sudo make cleansudo make all
sample测试: ( 比不使用cudnn快很多)
sh data/mnist/get_mnist.sh
sh examples/mnist/create_mnist.sh
我们可以将迭代次数增加到50000次
sudo gedit examples/mnist/lenet_solver.prototxt
修改max_iter: 50000
最后:
sh examples/mnist/train_lenet.sh
编译Python接口
依赖项
sudo apt-get install -y python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags cython ipython1
1
sudo vi ~/.bashrc 添加: export PYTHONPATH=/home/dl/caffe/python:$PYTHONPATH sudo ldconfig sudo make pycaffe -j81
2
3
4
1
2
3
4
编译matlab接口
安装matlab2014sh /usr/local/MATLAB/R2014a/bin/matlab
Makefile.config 中修改 : MATLAB_DIR := /usr/local/MATLAB/R2014a
sudo make matcaffe -j8
其他
Vi编辑命令 常用vi编辑器命令行A:当前行的尾部追加内容 i:游标前插入内容 I:游标后插入内容 o:在鼠标所在行的下面添加内容 O:在鼠标所在行的上面添加内容 ESC:退出编辑模式 Ctrl-T:移动到下一个tab Backspace:向后移动一个字符 Ctrl-U:删除当前 cw:删除游标所在的字符,然后进入编辑模式 cc:删除游标所在的行,然后进入编辑模式 C:删除从游标所在的位置到行尾的字符,然后进入编辑模式 dd:删除当前行 ndd:删除第n行 D:删除当前行游标所在的位置后面的字符 dw:删除邮编所在的字符 d}:删除当前段剩余的字符 d^:删除游标前到行首的字符 c/pat:删除游标后面到第一次匹配字符间的内容 dn:删除游标后面到下一个匹配字符间的内容 dfa:删除当前行游标到匹配字符间的内容(匹配的字符也将被删) dta:删除当前行游标到匹配字符间的内容(匹配的字符不被删) dL:删除从游标到屏幕的最后一行之间的内容 dG:删除从游标到文件末尾之间的内容 J:连结上下两行的内容 p:在游标后面插入buffer中的内容 P:在游标前面插入buffer中的内容 rx:用x替换字符 Rtext:用text从游标开始处进行替换 u:撤销最后的改变 U:还原当前行的内容 x:向后删除游标所在位置的字符 X:向前删除游标前面的字符 nX:删除前面的n个字符,游标所在的字符将不会被删 .:还原最后的改变 ~:反转字母的大小写 y:拷贝当前行到新的buffer yy:拷贝当前行 "xyy:拷贝当前行的buffer名为x的buffer ye:拷贝当单词的末尾
搜狗输入法安装
Ubuntu14.04安装搜狗输入法
im-config 然后 ibus选取fcitx
fcitx-config-gtk3
参考资料
Caffe学习系列(1):安装配置ubuntu14.04+cuda7.5+caffe+cudnnCaffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南
ubuntu 14.04 install g++问题"g++:Depends:g++-4.8(>= 4.8.2-5
ippicv_linux_20141027.tgz
http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation
相关文章推荐
- 深度学习21天实战caffe学习笔记《16:Caffe迁移和部署》
- 深度学习-21天实战caffe 笔记
- 深度学习21天实战Caffe学习笔记--笔记5--caffe中前向传播、反向传播及最优化求解过程
- 深度学习21天实战Caffe学习笔记--深度学习工具汇总
- 深度学习21天实战caffe学习笔记《12:Caffe 最优化求解过程》
- 深度学习21天实战caffe学习笔记《7 :Caffe数据结构》
- 深度学习21天实战Caffe学习笔记一
- 深度学习21天实战caffe学习笔记《1:深度学习的过往》
- 深度学习21天实战caffe学习笔记《6 : Caffe代码梳理》
- 深度学习21天实战caffe学习笔记《2 :深度学习工具》
- 深度学习21天实战caffe学习笔记《17:学习资源>
- 深度学习21天实战实战caffe学习笔记<10:Caffe前向传播>
- 深度学习21天实战Caffe学习笔记--笔记5--caffe中前向传播、反向传播及最优化求解过程
- 深度学习21天实战caffe学习笔记《0 : caffe包解析》
- 深度学习21天实战Caffe学习笔记--笔记2--深度学习工具汇总
- 深度学习21天实战caffe学习笔记《4 : Caffe依赖包解析》
- 深度学习21天实战实战caffe学习笔记<11:Caffe 反向传播>
- 深度学习21天实战Caffe学习笔记--笔记6--caffe的I/O模块,Caffe模型
- 深度学习21天实战caffe学习笔记《13:Caffe 实用工具》
- 深度学习21天实战Caffe学习笔记--笔记4--caffe数据结构