ubuntu apt-get install -f 错误: Can''t exec "local...（ubuntu apt-get command not found）

25-04-11 3

对于想了解ubuntuapt-getinstall-f错误:Can''texec"local...的读者，本文将是一篇不可错过的文章，我们将详细介绍ubuntuapt-getcommandnotfou

对于想了解ubuntu apt-get install -f 错误: Can''t exec "local...的读者，本文将是一篇不可错过的文章，我们将详细介绍ubuntu apt-get command not found，并且为您提供关于caffe ubuntu 14.04 install、Canonical 发布 Ubuntu Nexus 7 Desktop Installer、Install Caffe on Ubuntu 14、install nginx on ubuntu install ubuntu usb install ubuntu 14.04 ubuntu install jd的有价值信息。

本文目录一览：

ubuntu apt-get install -f 错误: Can''t exec "local...（ubuntu apt-get command not found）
caffe ubuntu 14.04 install
Canonical 发布 Ubuntu Nexus 7 Desktop Installer
Install Caffe on Ubuntu 14
install nginx on ubuntu install ubuntu usb install ubuntu 14.04 ubuntu install jd

ubuntu apt-get install -f 错误: Can''t exec

ubuntu apt-get install -f 错误: Can''t exec "local...（ubuntu apt-get command not found）

Can''t exec "locale": No such file or directory at /usr/share/perl5/Debconf/Encoding.pm line 16.
Use of uninitialized value $Debconf::Encoding::charmap in scalar chomp at /usr/share/perl5/Debconf/Encoding.pm line 17.
dpkg: `ldconfig'' not found on PATH.
dpkg: 1 expected program(s) not found on PATH.
NB: root''s PATH should usually contain /usr/local/sbin, /usr/sbin and /sbin.
E: Sub-process /usr/bin/dpkg returned an error code (2)
mitja@cube:~$ printenv PATH
/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games

解决办法：

由于报出缺少 "ldconfig"这个文件，所以可以:aptitude download libc-bin 下载libc-bin(ldconfig文件在这个里面)

然后dpkg -x libc-bin*.deb unpackdir/;   cp unpackdir/sbin/ldconfig /sbin/;

最后sudo apt-get install -f;

就能够修复，然后就能够正常apt-get install

caffe ubuntu 14.04 install

http://www.linuxidc.com/Linux/2015-07/120449.htm

最近因为各种原因，装过不少次Caffe，安装过程很多坑，为节省新手的时间，特此总结整个安装流程。

关于Ubuntu 版本的选择，建议用14.04这个比较稳定的版本，但是千万不要用麒麟版！！！比原版体验要差很多！！！

Caffe的安装过程，基本采纳这篇文章然后稍作改动，跳过大坑。

Caffe + Ubuntu 14.04 64bit + CUDA 6.5 配置说明 http://www.linuxidc.com/Linux/2015-04/116444.htm

1. 安装开发依赖包

sudo apt-get install build-essential
sudo apt-get install vim cmake git
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler

2. 安装CUDA

一般电脑都有双显卡：Intel 的集成显卡 + Nvidia 的独立显卡。要想两个显卡同时运行，需要关闭 lightdm 服务。

2.1 到这里下载安装包，选Linux x86 下的 Ubuntu 14.04， Local Package Installer，下载下来的文件为

　　cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb

2.2 在BIOS设置里选择用Intel显卡来显示或作为主要显示设备

2.3 进入Ubuntu，按 ctrl+alt+F1 ，登入自己的账号，然后输入以下命令

sudo service lightdm stop

2.4 安装 CUDA，cd 到安装包目录，输入以下命令：

sudo dpkg -i cuda-repo-ubuntu1404-7-0-local_7.0-28_amd64.deb
sudo apt-get update
sudo apt-get install cuda

安装完后重启电脑。

3. 安装cuDNN

3.1 到这里注册下载，貌似注册验证要花一两天的样子，嫌麻烦的可以直接到Linux公社资源站下载

资源包下载地址：

------------------------------------------分割线------------------------------------------

FTP地址：ftp://ftp1.linuxidc.com

用户名：ftp1.linuxidc.com

密码：www.linuxidc.com

在 2015年LinuxIDC.com\7月\Caffe在Ubuntu 14.04 64bit 下的安装

下载方法见 http://www.linuxidc.com/Linux/2013-10/91140.htm

------------------------------------------分割线------------------------------------------

3.2 完后到下载目录，执行以下命令安装

tar -zxvf cudnn-6.5-linux-x64-v2.tgz
cd cudnn-6.5-linux-x64-v2
sudo cp lib* /usr/local/cuda/lib64/
sudo cp cudnn.h /usr/local/cuda/include/

再更新下软连接

cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.6.5
sudo ln -s libcudnn.so.6.5.48 libcudnn.so.6.5
sudo ln -s libcudnn.so.6.5 libcudnn.so

3.3 设置环境变量

gedit /etc/profile

在打开的文件尾部加上

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

保存后执行以下命令使之生效

source /etc/profile

同时创建以下文件

sudo vim /etc/ld.so.conf.d/cuda.conf

内容是

/usr/local/cuda/lib64

保存后，使之生效

sudo ldconfig

4. 安装CUDA Sample 及 ATLAS

4.1 Build sample

cd /usr/local/cuda/samples
sudo make all -j8

我电脑是八核的，所以make 时候用-j8参数，大家根据情况更改，整个过程有点长，十分钟左右。

4.2 查看驱动是否安装成功

cd bin/x86_64/linux/release
./deviceQuery

出现以下信息则成功

./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 670"
  CUDA Driver Version / Runtime Version          6.5 / 6.5
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 4095 MBytes (4294246400 bytes)
  ( 7) Multiprocessors, (192) CUDA Cores/MP:     1344 CUDA Cores
  GPU Clock rate:                                1098 MHz (1.10 GHz)
  Memory Clock rate:                             3105 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Bus ID / PCI location ID:           1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 1, Device0 = GeForce GTX 670
Result = PASS

4.3 安装ATLAS

ATLAS是做线性代数运算的，还有俩可以选：一个是Intel 的 MKL，这个要收费，还有一个是OpenBLAS，这个比较麻烦；但是运行效率ATLAS < OpenBLAS < MKL

我就用ATLAS咯：

sudo apt-get install libatlas-base-dev

5. 安装Caffe需要的Python包

网上介绍用现有的anaconda，我反正不建议，因为路径设置麻烦，很容易出错，而且自己安装很简单也挺快的。

首先需要安装pip

sudo apt-get install python-pip

再下载caffe，我把caffe放在用户目录下

cd
git clone https://github.com/BVLC/caffe.git

再转到caffe的python目录，安装scipy

cd caffe/python
sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose

最后安装requirement里面的包，需要root权限

sudo su
for req in $(cat requirements.txt); do pip install $req; done

如果提示报错，一般是缺少必须的包引起的，直接根据提示 pip install <package-name>就行了。

安装完后退出root权限

exit

6. 编译caffe

首先修改配置文件，回到caffe目录

cd ~/caffe
cp Makefile.config.example Makefile.config
gedit Makefile.config

这里仅需修改两处：

i) 使用cuDNN

# USE_CUDNN := 1

这里去掉#，取消注释为

USE_CUDNN := 1

ii) 修改python包目录，这句话

PYTHON_INCLUDE := /usr/include/python2.7 \
　　/usr/lib/python2.7/dist-packages/numpy/core/include

改为

PYTHON_INCLUDE := /usr/include/python2.7 \
　　/usr/local/lib/python2.7/dist-packages/numpy/core/include

因为新安装的python包目录在这里： /usr/local/lib/python2.7/dist-packages/

接下来就好办了，直接make

make all -j4
make test
make runtest
make pycaffe

这时候cd 到caffe 下的 python 目录，试试caffe 的 python wrapper安装好没有：

python
import caffe

如果不报错，那就说明安装好了。

Canonical 发布 Ubuntu Nexus 7 Desktop Installer

Canonical的Victor Palau曾在YouTube上传过一段视频，演示了Nexus 7上运行Ubuntu的效果，从这则极短的视频中可以看出，Ubuntu在Nexus 7上的运行还是很流畅的。昨日，Canonical官方发布了一个小工具———Ubuntu Nexus 7 Desktop Installer，它可以帮助开发人员将Ubuntu 12.10轻松安装到Nexus 7上。

Ubuntu Nexus 7 Desktop Installer拥有图形化管理界面，简单易用，测试镜像下载和安装一键搞定。

上述教程8GB、16GB版N7均适用，部分已知问题点这里查看（刷机后的靓照如下）

友情提醒：刷机有风险，折腾需谨慎！

Install Caffe on Ubuntu 14

Assuming you have already installed CUDA and cudnn as well as anaconda

Install OpenCV

sudo apt-get update
sudo apt-get install -y build-essential
sudo apt-get install -y cmake
sudo apt-get install -y libgtk2.0-dev
sudo apt-get install -y pkg-config
sudo apt-get install -y python-numpy python-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev libjasper-dev

sudo apt-get -qq install libopencv-dev build-essential checkinstall cmake pkg-config yasm libjpeg-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev python-dev python-numpy libtbb-dev libqt4-dev libgtk2.0-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils

wget http://downloads.sourceforge.net/project/opencvlibrary/opencv-unix/2.4.11/opencv-2.4.11.zip
unzip opencv-2.4.11.zip
cd opencv-2.4.11
mkdir release
cd release

cmake -G "Unix Makefiles" -D CMAKE_CXX_COMPILER=/usr/bin/g++ CMAKE_C_COMPILER=/usr/bin/gcc -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 -D BUILD_FAT_JAVA_LIB=ON -D INSTALL_TO_MANGLED_PATHS=ON -D INSTALL_CREATE_disTRIB=ON -D INSTALL_TESTS=ON -D ENABLE_FAST_MATH=ON -D WITH_IMAGEIO=ON -D BUILD_SHARED_LIBS=OFF -D WITH_GSTREAMER=ON -D CUDA_GENERATOR=Kepler ..
make all -j8
sudo make install

After installation,run

sudo gedit /etc/ld.so.conf.d/opencv.conf

and add

/usr/local/lib

in the file and afterwards run

sudo ldconfig
sudo gedit /etc/bash.bashrc

and add

PKG_CONfig_PATH=$PKG_CONfig_PATH:/usr/local/lib/pkgconfig
export PKG_CONfig_PATH

Install ffmpeg(Optional)

sudo add-apt-repository ppa:mc3man/trusty-media
sudo apt-get update
sudo apt-get dist-upgrade
sudo apt-get install ffmpeg

Install Caffe

Modify Makefile.config

## 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 through *_61 lines for compatibility.
# For CUDA < 8.0,comment the *_60 and *_61 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_52,code=sm_52 \
        -gencode arch=compute_60,code=sm_60 \
        -gencode arch=compute_61,code=sm_61 \
        -gencode arch=compute_61,code=compute_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# 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

# Nccl acceleration switch (uncomment to build with Nccl)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_Nccl := 1

# 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 ?= @

and then run

make all -j8
make test -j8
make runtest
make pycaffe

install nginx on ubuntu install ubuntu usb install ubuntu 14.04 ubuntu install jd

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