sedna/examples/multi-edge-inference-pedestrian-tracking-gpu-feature-extraction.Dockerfile

FROM pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel

WORKDIR /home

## Install git
RUN apt update -o Acquire::https::developer.download.nvidia.com::Verify-Peer=false

# Required by OpenCV
RUN apt install libglib2.0-0 libgl1 libglx-mesa0 libgl1-mesa-glx -y

# RUN apt install -y git
RUN apt install -y gfortran libopenblas-dev liblapack-dev

# Update Python 
RUN apt install python3.8 python3.8-distutils python3-venv curl -y
RUN python3.8 --version
RUN curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
RUN python3.8 get-pip.py

## Install base dependencies
RUN python3.8 -m pip install torch torchvision tqdm opencv-python pillow pytorch-ignite --trusted-host=developer.download.nvidia.com

## Add Kafka Python library
RUN python3.8 -m pip install kafka-python --trusted-host=developer.download.nvidia.com

# ONNX
RUN pip install onnx protobuf==3.16.0

## SEDNA SECTION ##

COPY ./lib/requirements.txt /home
RUN python3.8 -m pip install -r /home/requirements.txt --trusted-host=developer.download.nvidia.com

# This instructions should make Sedna reachable from the dertorch code part
ENV PYTHONPATH "${PYTHONPATH}:/home/lib"

WORKDIR /home/work
COPY ./lib /home/lib

# Add M3L imports
COPY examples/multiedgeinference/pedestrian_tracking/feature_extraction /home/work

ENV PYTHONPATH "${PYTHONPATH}:/home/work"
ENV LOG_LEVEL="INFO"

ENTRYPOINT ["python3.8"]
CMD ["worker.py"]