先贴下官网教程
推荐的环境:
- CUDA 8.0.61 + Cudnn 6.0(必须的,推荐官网的
.run
形式安装) - Anaconda python=2.7 (推荐使用conda 自带的包管理,即:
conda install
)
首先:更新或者安装 nccl
库
从apt-get上的库的地址:http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/ 下载最新的libnccl2, libnccl-dev
(注意版本一致性)。
wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl-dev_2.3.5-2+cuda8.0_amd64.deb
wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl2_2.3.5-2+cuda8.0_amd64.deb
sudo dpkg -i libnccl2_{version}.deb
sudo dpkg -i libnccl_{version}.deb
然后:安装官网中的依赖,并进行源码安装
这里我没使用系统Python:
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
build-essential \
git \
libgoogle-glog-dev \
libgtest-dev \
libiomp-dev \
libleveldb-dev \
liblmdb-dev \
libopencv-dev \
libopenmpi-dev \
libsnappy-dev \
libprotobuf-dev \
openmpi-bin \
openmpi-doc \
protobuf-compiler \
libgflags-dev \
cmake
Anaconda 新建一个环境:
conda create -n caffe2 python=2.7
# 切换环境
source activate caffe2
(caffe2): conda install future \
numpy \
protobuf \
typing \
hypothesis
源码安装:
git clone https://github.com/pytorch/pytorch.git && cd pytorch
git submodule update --init --recursive
USE_LEVELDB=1 USE_LMDB=1 USE_OPENCV=1 BUILD_BINARY=1 python setup.py install
caffe2 最终安装在
anaconda3/envs/caffe2/lib/python2.7/site-packages/caffe2
测试是否成功:
# To check if Caffe2 build was successful
python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
# To check if Caffe2 GPU build was successful
# This must print a number > 0 in order to use Detectron
python -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'
Detectron 安装
git clone https://github.com/facebookresearch/detectron
依赖环境:
conda install --file requirements.txt
# opencv3
conda install -c menpo opencv3
# pydot (to fix:No handlers could be found for logger "caffe2.python.net_drawer")
conda install pydot
源码安装:
(caffe2): python setup.py install