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Facebook Research – Detectron and Singularity Containers

By Adam Hughes

Oct 1, 2018 | Blog, How To Guides

Detectron is an object detection system written in Python and powered by Caffe2. Detectron is a Facebook Research project created to solve real world problems, particularly for deep learning applications.

Singularity provides a basis for repeatability with a container runtime that does not require root owned daemon or escalation of user privilege to run. With it’s single file image format (SIF) Singularity is ideal for BYOE science (bring your own environment) and for the mobility of compute with deep learning applications.

The Singularity Definition file is:


BootStrap: docker
From: caffe2/caffe2:snapshot-py2-cuda9.0-cudnn7-ubuntu16.04

%runscript
    # run a simple test with this data: /app/data/model_final.pkl
    cp -rf /detectron .
    cd ./detectron && python2 tools/infer_simple.py \
      --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml \
      --output-dir /tmp/detectron-visualizations \
      --image-ext jpg \
      --wts /app/data/model_final.pkl \
      demo

%environment
    export PYTHONPATH=/usr/local/caffe2_build:/detectron/lib:$PYTHONPATH
    export Caffe2_DIR=/usr/local/caffe2_build
    export LD_LIBRARY_PATH=/usr/local/caffe2_build/lib:$LD_LIBRARY_PATH
    export LC_ALL=C

%post
    apt-get -y update
    # basic-tools:
    apt-get -y install vim curl hostname wget unzip tar gzip bc less \
        util-linux strace bzip2 man-db
    apt-get clean

    # detectron based on:
    # https://hub.docker.com/r/suhangpro/detectron/~/dockerfile/

    mv /usr/local/caffe2 /usr/local/caffe2_build
    export Caffe2_DIR=/usr/local/caffe2_build

    export PYTHONPATH=/usr/local/caffe2_build:$PYTHONPATH
    export LD_LIBRARY_PATH=/usr/local/caffe2_build/lib:$LD_LIBRARY_PATH

    # get Detectron code version from a fixed date for reproducibility reasons
    git clone https://github.com/facebookresearch/detectron /detectron
    cd /detectron
    git checkout "`git rev-list master  -n 1 --first-parent --before=2018-08-22`"
    cd -

    # Install Python dependencies from detectron requirements
    pip install -r /detectron/requirements.txt

    # Install the COCO API
    git clone https://github.com/cocodataset/cocoapi.git /cocoapi
    cd /cocoapi
    git checkout "`git rev-list master  -n 1 --first-parent --before=2018-08-22`"
    cd -
    cd /cocoapi/PythonAPI
    make install
    cd -

    cd /detectron
    make
    make ops
    cd -

    chmod -R a+rX /usr/lib/python*
    chmod -R a+rX /detectron

    # add some sample data to test the container
    mkdir -p /app/data

    curl -sSL "https://s3-us-west-2.amazonaws.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl" > /app/data/model_final.pkl

    apt-get clean

To build the image run the following command:

$ sudo singularity build detectron.sif detectron.def

The definition file above has a %runscript section that allows direct execution from outside the container with the following command:

$ singularity run --nv detectron.sif

This will run detectron against the /app/data/model_final.pkl test data.

NOTE: Detectron currently does not have a CPU implementation; a system with one of more nVidia GPU’s is required to run

See the Sylabs Detectron repository for the definition file.

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