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矩池云上安装caffe gpu教程
选用CUDA10.0镜像
添加nvidia-cuda和修改apt源
curl -fsSL https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/7fa2af80.pub | apt-key add - && \ echo "deb https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/ /" > /etc/apt/sources.list.d/cuda.list && \ bash /public/script/switch_apt_source.sh
安装curand
apt install cuda-curand-dev-10-0
修改conda源
bash /public/script/switch_conda_source.sh
创建python3.7虚拟环境
conda create -n py37 python=3.7 conda deactivate conda activate py37
安装依赖包
apt-get -y install libboost-dev libprotobuf-dev libgflags-dev libgoogle-glog-dev libhdf5-dev libopencv-dev protobuf-c-compiler protobuf-compiler libopenblas-dev libhdf5-dev libleveldb-dev liblmdb-dev libboost-system-dev libboost-filesystem-dev libsnappy-dev libboost-thread-dev libatlas-base-dev libboost-python-dev
添加nvidia-machine-learning软件源
curl -fsSL https://mirrors.cloud.tencent.com/nvidia-machine-learning/ubuntu1804/x86_64/7fa2af80.pub | apt-key add - && \ echo "deb https://mirrors.cloud.tencent.com/nvidia-machine-learning/ubuntu1804/x86_64/ /" > /etc/apt/sources.list.d/cuda.list
安装剩余依赖包
apt update apt install libnccl2=2.6.4-1+cuda10.0 libnccl-dev=2.6.4-1+cuda10.0 apt-get install -y --no-install-recommends libboost-all-dev pip install boost conda install opencv
git clone caffe仓库
git clone -b 1.0 --depth 1 https://github.com/BVLC/caffe.git cd caffe for req in $(cat python/requirements.txt); do pip install $req; done cp Makefile.config.example Makefile.config git clone -b 1.0 --depth 1 https://gitee.com/matpools/caffe.git
查找对应路径
python -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())"
python -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))"
(py37) root@a688d840812b:/caffe# python -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())"
/root/miniconda3/envs/py37/include/python3.7m
(py37) root@a688d840812b:/caffe# python -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))"
/root/miniconda3/envs/py37/lib
查找numpy路径
find /root/miniconda3/envs/py37/lib/ -name numpy
(py37) root@a688d840812b:/caffe# find /root/miniconda3/envs/py37/lib/ -name numpy /root/miniconda3/envs/py37/lib/python3.7/site-packages/numpy/core/include/numpy
如果也是cuda10纯镜像可以直接复制下面的文件，然后保存。
## 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_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. 如果是自己弄需要改PYTHON_INCLUDE PYTHON_INCLUDE := /root/miniconda3/envs/py37/include/python3.7m \ /root/miniconda3/envs/py37/lib/python3.7/site-packages/numpy/core/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) 如果是自己弄需要改PYTHON_LIBRARIES PYTHON_LIBRARIES := boost_python3 python3.7m # 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. 如果是自己弄需要改PYTHON_LIB PYTHON_LIB := /root/miniconda3/envs/py37/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 /usr/lib/x86_64-linux-gnu # 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 $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @
开始编译
make clean make all -j6 make clean make pycaffe -j6
设置环境变量
export PYTHONPATH=/caffe/python/:$PYTHONPATH export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/miniconda3/envs/py37/lib
使用ipython环境测试
ipython import caffe caffe.set_mode_gpu() caffe.__version__
使用官方examples测试
#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
echo "Downloading..."
for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
doif [ ! -e $fname ]; then
wget --no-check-certificate https://storage.googleapis.com/cvdf-datasets/mnist/${fname}.gz
gunzip ${fname}.gz
fi
done
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
nvidia-smi -l 5
参考文章
https://hub.docker.com/r/floydhub/caffe/tags?page=1&ordering=last_updated
https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/url_checksums/mnist.txt
https://www.cnblogs.com/laosan007/p/11737704.html
https://blog.csdn.net/u010417185/article/details/53559107
https://github.com/BVLC/caffe/issues/720
https://github.com/BVLC/caffe/issues/263
https://github.com/BVLC/caffe/issues/6063