docker中cuda9.2+cudnn7+caffe
萬事開頭難, 所以儘量找別人開好的頭, 現在nvidia官方給的都cuda9.2了, 開源的gpu-caffe基本還是cuda8.0, 經過綜合考慮, 還是在docker上面重新搭建, 這樣不影響本地主機的環境又方便以後的移植部署.
1. Pull docker image. (docker的安裝請自行百度下.)
# sudo docker pull nvidia/cuda:9.2-cudnn7-devel-ubuntu16.04
2. Git clone caffe source. (Git的安裝我是用 apt安裝的)
# cd workspace && git clone
3. 啟動docker 映象, 記得一定要用nvidia-docker命令,這個不是docker自帶的, 安裝請百度下.
# sudo nvidia-docker run -it -v #HOME/workspace:/var/workspace --name cuda-caffe nvidia/cuda:9.2-cudnn7-devel-ubuntu16.04 /bin/bash
Ps:啟動過後, 以後進入docker的bash需要執行
# sudo nvidia-docker exec -it cuda-caffe /bin/bash
或者
# sudo docker exec -it cuda-caffe /bin/bash
4. 準備caffe環境, 參考的caffe官網 installation, 其實這裡說得很清楚了, 我這裡也是做一個搬運工.
a. Cuda, 官方說需要cuda7以上版本, 以前的版本也是可以的, 但是不保證沒得小問題. 當然我們docker image直接是cuda9.2,所以不考慮這個問題.
b. Blas, 好像是一個數學的計算庫, 包括矩陣計算啥的, 有MKL, ATLAS, OpenBLAS等都可以, 預設支援MKL, 這個選擇不同配置的makefile-config. 我看OpenBLAS親切, 於是就上的OpenBLAS
# apt install libopenblas-dev
c. Boost, 這個是c++的一個封裝庫, 很強大, 有很多模組. 如果不考慮pycaffe啥的, 可以直接用apt安裝(# apt install libboost-all-dev), 但是我這裡用的anaconda安裝的python3.6, 考慮到apt安裝的預設python3.5版本不相容, 所以下載的boost重新編譯的.
Boost沒有立即git最新版本, 是以前下載的(1.66.0), 但和最新版本應該不超過三個月, 相差不大, 所以最新版本(1.68.0)應該也沒有很大的問題, 靈活處理, 歡迎交流.
下載,編譯與安裝:
# tar xf boost_1_66_0.tar.gz
# cd boost_1_66_0
# ./bootstrap.sh --with-libraries=python --with-toolset=gcc
# ./b2 cflags='-fPIC' cxxflags='-fPIC' --with-python include="/root/anaconda3/include/python3.6m/" # 這裡的include根據自己的python環境進行修改即可
# ./b2 install
d. Protobuf, gflags, hdf5, glog. 前面三個在Ubuntu上可以用apt進行安裝, 最後一個要git原始碼安裝.
# apt install libprotobuf-dev libgflags-dev libhdf5-dev
# cd glog
# ./autogen.sh && ./configure && make && make install #如果沒有安裝autogen工具, 百度安裝下即可
e. Opencv 是現在霸屏似得影象處理工具, caffe官方推薦版本要大於2.4, 當然3.0以後也是沒得問題的. 當然不知道怎麼的, opencv3.3以後的版本沒有c-api的, 需要注意下. 我這裡不糾結直接用apt進行安裝.
# apt install libopencv-dev
Ps: 當然這樣安裝是有壞處的, 首先版本不能控制, 這個完全看官方維護, 我安裝完後查看了一下是 2.4.9.1 . 滿足官方的要求, 我就沒有糾結, 當然我們公司專案用的是opencv 3.2.0 . 那都是後話.
f. Io 庫, 官方需要 lmdb 和leveldb. 直接apt安裝
# apt install liblmdb-dev libleveldb-dev
g. Cudnn, 官方建議v6以上, 我們docker image自帶v7. 所以這裡不做修改.
5. 修改makefile.config
改的細節挺多的, 我這裡直接把改好的config分享出來, 修改的地方用紅色標準.
## 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 CUDA_ARCH := -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/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 PYTHON_LIBRARIES := boost_python3 python3.6m 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 python3.5m # 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 ?= @ |
6. 編譯caffe與測試
# cd $HOME/caffe
# make all -j8
# make pycaffe
# make test
7. Python 環境新增 caffe
a. 在.bashrc中新增一下內容
export PYTHONPATH="/var/source/caffe/python:$PYTHONPATH" export LD_LIBRARY_PATH="/usr/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/lib:$LD_LIBRARY_PATH" |
b. 更新環境
# source $HOME/.bashrc
8. 總結
總體來看, 沒有修改caffe程式碼或者庫, 唯一麻煩的就是配置下Makefile.config檔案. 哈哈, 大功告成, 到這裡基本搞定了, 但部落格總有遺漏的地方, 歡迎大家積極交流.