We provide pre-built wheel for python >= 3.4, Ubuntu >= 14.04 or CentOS >=7. The wheels are staticly linked so no dependency other than CUDA is required.
Downdload wheel for CUDA-9.0(2019.8.20)
Downdload wheel for CUDA-10.0(2019.12.14)
Downdload wheel for CUDA-10.1(2019.12.14)
Install the wheel as
pip3 install https://1dv.aflat.top/mxnet_cu101-1.6.0b20191214-py2.py3-none-manylinux1_x86_64.whl --user
We provide pre-built docker images for both cuda9.0 and cuda10.0.
Maxwell, Pascal, Volta and Turing GPUs are supported.
For nvidia-driver >= 410.48, cuda10 image is recommended.
For nvidia-driver >= 384.81, cuda9 image is recommended.
Aliyun beijing mirror is provided for users pulling from China.
nvidia-docker run -it -v $HOST-SIMPLEDET-DIR:$CONTAINER-WORKDIR rogerchen/simpledet:cuda9 zsh
nvidia-docker run -it -v $HOST-SIMPLEDET-DIR:$CONTAINER-WORKDIR rogerchen/simpledet:cuda10 zsh
nvidia-docker run -it -v $HOST-SIMPLEDET-DIR:$CONTAINER-WORKDIR registry.cn-beijing.aliyuncs.com/rogerchen/simpledet:cuda9 zsh
nvidia-docker run -it -v $HOST-SIMPLEDET-DIR:$CONTAINER-WORKDIR registry.cn-beijing.aliyuncs.com/rogerchen/simpledet:cuda10 zsh
We recommend the users to adopt singualrity as the default environment manager to minimize the efforts of configuration. Singularity is a virtual environment manager like virtualenv, but in the system-level.
# install dependency
sudo apt update
sudo apt install build-essential python libarchive-dev
# install singularity
wget https://github.com/sylabs/singularity/releases/download/2.6.1/singularity-2.6.1.tar.gz
tar xzfv singularity-2.6.1.tar.gz
cd singularity-2.6.1
./configure --prefix=/usr/local
make
sudo make install
wget https://1dv.aflat.top/simpledet.img
Here we need to map the working directory into singularity shell, note that symlink to files outside the working directory will not work since singularity has its own filesystem. Thus we recommend users to map the whole data storage into singularity by replacing $WORKDIR by something like /data
or /mnt/
.
sudo singularity shell --no-home --nv -s /usr/bin/zsh --bind $WORKDIR /path/to/simpledet.img
- Ubuntu 16.04
- Python >= 3.5
# Install a patched cocotools for python3
pip3 install 'git+https://github.com/RogerChern/cocoapi.git#subdirectory=PythonAPI'
# Install dependency
sudo apt-get update
sudo apt-get install -y build-essential git
sudo apt-get install -y libopenblas-dev
git clone --recursive https://github.com/apache/incubator-mxnet /tmp/mxnet && \
git clone https://github.com/Tusimple/simpledet /tmp/simpledet && \
git clone https://github.com/RogerChern/cocoapi /tmp/cocoapi && \
cp -r /tmp/simpledet/operator_cxx/* /tmp/mxnet/src/operator && \
mkdir -p /tmp/mxnet/src/coco_api && \
cp -r /tmp/cocoapi/common /tmp/mxnet/src/coco_api && \
cd /tmp/mxnet && \
echo "USE_SIGNAL_HANDLER = 1" >> ./config.mk && \
echo "USE_OPENCV = 0" >> ./config.mk && \
echo "USE_MKLDNN = 0" >> ./config.mk && \
echo "USE_BLAS = openblas" >> ./config.mk && \
echo "USE_CUDA = 1" >> ./config.mk && \
echo "USE_CUDA_PATH = /usr/local/cuda" >> ./config.mk && \
echo "USE_CUDNN = 1" >> ./config.mk && \
echo "USE_NCCL = 1" >> ./config.mk && \
echo "USE_DIST_KVSTORE = 1" >> ./config.mk && \
echo "CUDA_ARCH = -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70" >> ./config.mk && \
rm /tmp/mxnet/src/operator/nn/group_norm* && \
make -j$((`nproc`-1)) && \
cd python && \
python3 setup.py install && \
rm -rf /tmp/mxnet /tmp/simpledet /tmp/cocoapi