linux中caffe版本查看指令,linux上caffe安装（docker 超简易操作版本）

向右看齐 2023-01-21 10:25 33阅读 0赞

由于caffe实在是太难安装了，需要依赖的库太多，安装了一天都没安装好(每安一个库都能 遇到一堆问题)，最后选择在docker上安装，轻轻松松解决问题。

1.下载docker镜像

然后查看docker images里面会有下好的这个docker，复制image id (这里是47dee10d8ba0)

$ nvidia-docker run -it -v 挂载目录:挂载目录 --name your\_name 47dee10d8ba0 /bin/bash

然后就进入docker了

2.测试mnist例子

进入/opt/caffe

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docker带的caffe目录，不需要再编译

可以试验下这里的mnist例子。

下载mnist数据集到这个目录下

$ ./data/mnist/get\_mnist.sh

转换格式，在examples/mnist生成了两个目录：mnist\_test\_lmdb和mnist\_train\_lmdb

$ ./examples/mnist/create\_mnist.sh

在 /opt/caffe文件夹下运行

$ ./build/tools/caffe train --solver=examples/mnist/lenet\_solver.prototxt

训练结果

成功！

3. tips

1.docker是个好东西，安装依赖包遇到一个又一个问题太要命了，不过安装过程让我对linux多些了解

2.安装好带caffe的docker，可以直接在pythom后import cafffe，但是在我从github上clone的caffe库里运行mnist的例子不行，还是得编译caffe，因为import各种包的时候位置不对，所以必须得进入docker自带的caffe文件夹里。

4. 后记(编译caffe)

以上步骤，可以使用标准的caffe了，但是caffe这个东西，和tensorflow不一样，它的层还得用c++写在源代码里，重新编译一次caffe才能用。

我需要用的一个代码，自带一个caffe文件夹，下面是他重写过的caffe，要想运行它的代码，就不能用标准caffe而是得编译他的caffe。

在caffe文件夹下，修改Makefile.config文件，

\#\# 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

\# This code is taken from https://github.com/sh1r0/caffe-android-lib

\# USE\_HDF5 := 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.

\# For CUDA >= 9.0, comment the \*\_20 and \*\_21 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

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/local/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 /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

\# 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 ?= @

CUSTOM\_CXX := g++ -std=c++11

然后

$ make clean

$ make all -j8 \#使用8个进程

$ make pycaffe

这样就编译好了，因为之前的docker已经是把其他依赖库都装好了的，所以只需要重新编译下就行了。

如果还是报错“ImportError: No module named \_caffe”，说明python的路径问题，sys.path.insert(0,"/caffe/python")即可

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