如何建立自己推广网站,小说网站开发业务逻辑,长沙娱乐网站开发,合肥网站优化公司继上一次倒腾caffe安装以后#xff0c;因为博士毕业等原因#xff0c;旧的服务器已经不能再使用#xff0c;最近因论文等原因#xff0c;不得不继续来安装一下我的caffe。这次运气比较好#xff0c;经历了一晚上和一早上的痛苦之后#xff0c;最终安装成功了#xff0c;…继上一次倒腾caffe安装以后因为博士毕业等原因旧的服务器已经不能再使用最近因论文等原因不得不继续来安装一下我的caffe。这次运气比较好经历了一晚上和一早上的痛苦之后最终安装成功了而且由于使用极链AI云服务器或者AutoDL来安装的所以应该这次的可复现性更强下面总结一些必要的地方以帮助大家复现安装我的caffe. 首先需要选择极链的云服务器。 - nvidia cuda和gpu的选择 这里我们使用的是极链-华北一区-GeForce RTX 2080 的gpucuda 10.1, python2.7 禁用cudnn. 然后开始安装后面的包。
如果是使用AutoDL的云服务器则可以选择 环境可以选择Miniconda conda3、Python 3.8 、 Cuda 10.1禁用cudnn.
1. 首先仍然需要参考《深度学习21天实战caffe》的第5天那一章来进行安装最后的安装列表为 这里可以不管caffe和opencv只要确保其他的文件如图所示即可。
所有必备的caffe软件【更新版】我们已经上传到百度云盘请大家需要的可以下载 链接https://pan.baidu.com/s/13P9PiTTnjOD6ZiiS4-dCBg 提取码zlut 2. 在安装各个包的过程中一些包的安装会有些困难列举如下 - Boost 这里一定要强调把filesystem也要加上 - glog
初始时刻我用glog-0.3.3.tar来安装的总是出错后来换成了glog-0.3.5.tar安装就对了。
- opencv 这里我们采用了opencv-3.4.14。 可以参考《复盘caffe安装》来安装opencv。
安装过程中可能会卡在IPPICV: Download: ippicv_2020_lnx_intel64_20191018_general.tgz这时果断停止编译。从刚才的百度云盘中下载ippicv_2020_lnx_intel64_20191018_general.tgz然后上传到某一个位置比如./opencv-3.4.14/3rdparty/ippicv/,
接着修改配置文件./opencv-3.4.14/3rdparty/ippicv/ippicv.cmake,
找到https://raw.githubusercontent.com/opencv/opencv_3rdparty/${IPPICV_COMMIT}/ippicv/
将其改为你保存ippicv_2020_lnx_intel64_20191018_general.tgz文件的路径
我的是./opencv-3.4.14/3rdparty/ippicv/,如下 最后再来编译。
unzip opencv-2.4.9.zip
cd opencv-2.4.9/
mkdir build;
cd build/
cmake .. ## 初始时尝试这个命令如果遇到ippicv的问题则离线下载并修改路径后执行下一条命令
cmake -D CMAKE_BUILD_TYPERelease -D CMAKE_INSTALL_PREFIXYOUR_INSTALL_PATH ..
make make install注意这里可以不用ccmake .. 也可以。
------------------------------------------------------------------------------------------- opencv安装需要花费较多时间。安装完成以后为了能后面使用opencv.pc需要在root或者home下面的bashrc中增加如下命令然后使用 source ~/.bashrc更新。
export PATH/user-data/caffe_local_install/bin/:$PATH
export PKG_CONFIG_PATH/user-data/caffe_local_install/lib/pkgconfig/:$PKG_CONFIG_PATH
export LD_LIBRARY_PATH/user-data/caffe_local_install/lib:$LD_LIBRARY_PATH
##(静态库搜索路径) 程序编译期间查找动态链接库时指定查找共享库的路径
export LIBRARY_PATH/user-data/caffe_local_install/lib:$LIBRARY_PATH ##c程序头文件搜索路径
export C_INCLUDE_PATH/user-data/caffe_local_install/include:$C_INCLUDE_PATH
##c程序头文件搜索路径
export CPLUS_INCLUDE_PATH/user-data/caffe_local_install/include:$CPLUS_INCLUDE_PATH 这样以后就可以查看是否安装成功
pkg-config --modversion opencv
显示opencv版本。 - python依赖包的安装
我们需要提前创造一个python2.7的虚拟环境caffe.
由于极链已经预装了anaconda所以可以直接创造虚拟环境否则应该先按照anaconda, 再创建虚拟环境。 可参考安装依赖库常用命令 - 极链AI云支持中心
conda create -n your_env_name pythonX.X2.7、3.6等)我的为
conda create -n caffe python2.7
接着开始按照如下的python包
pip install opencv-python4.2.0.32
pip install opencv-python4.1.1.26 #二选一
pip install protobuf2.6.1
pip install scikit-image0.14.2
3. 配置Makefile.config文件 cp Makefile.config.example 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 : 0# 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 youre using OpenCV 3OPENCV_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 archcompute_30,codesm_30 \-gencode archcompute_35,codesm_35 \-gencode archcompute_50,codesm_50 \-gencode archcompute_52,codesm_52 \-gencode archcompute_60,codesm_60 \-gencode archcompute_61,codesm_61 \-gencode archcompute_61,codecompute_61# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
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 : /user-data/caffe_local_install/include
BLAS_LIB : /user-data/caffe_local_install/lib# 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 : /data1/caiyong.wang/bin/matlab/
# 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 its in root.ANACONDA_HOME : /opt/conda/envs/caffePYTHON_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/libPYTHON_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 : /user-data/caffe_local_install/include $(PYTHON_INCLUDE) /usr/local/include /usr/include
LIBRARY_DIRS : /user-data/caffe_local_install/lib $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib64/ # 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: v1.2.3-1cuda8.0)
# 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 $LIBRARY_DIRS.)
USE_PKG_CONFIG : 1# N.B. both build and distribute dirs are cleared on make clean
BUILD_DIR : build
DISTRIBUTE_DIR : distribute# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG : 1# The ID of the GPU that make runtest will use to run unit tests.
TEST_GPUID : 0# enable pretty build (comment to see full commands)
Q ? 编译caffe
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
make all -j8
make pycaffe
编译到最后的结果 4. 编辑root或者home下面的bashrc,
export PYTHONPATH/user-data/software/caffe_tifs/python/:$PYTHONPATH
确保编译成功的caffe被添加到python的路径中最终新增的命令汇总为
export PATH/user-data/caffe_local_install/bin/:$PATH
export PKG_CONFIG_PATH/user-data/caffe_local_install/lib/pkgconfig/:$PKG_CONFIG_PATH
export PYTHONPATH/user-data/software/caffe_tifs/python/:$PYTHONPATH
export LD_LIBRARY_PATH/user-data/caffe_local_install/lib:$LD_LIBRARY_PATH
##(静态库搜索路径) 程序编译期间查找动态链接库时指定查找共享库的路径
export LIBRARY_PATH/user-data/caffe_local_install/lib:$LIBRARY_PATH ##c程序头文件搜索路径
export C_INCLUDE_PATH/user-data/caffe_local_install/include:$C_INCLUDE_PATH
##c程序头文件搜索路径
export CPLUS_INCLUDE_PATH/user-data/caffe_local_install/include:$CPLUS_INCLUDE_PATH
使用 source ~/.bashrc更新。
5. 验证caffe是否安装成功 至此安装成功。
6. 保存镜像 安装好的caffe环境可以保存成镜像以便在租用不同的gpu时快速创建caffe。
