Dockerfile

# The build-stage image:
FROM continuumio/miniconda3:24.3.0-0 AS build

# Install necessary packages
RUN conda config --set always_yes yes --set changeps1 no
RUN conda update --all -y
RUN conda config --add channels conda-forge
RUN conda install -c conda-forge conda-pack
        
# Install libmamba and set it as default solver
RUN conda install -n base conda-libmamba-solver
RUN conda config --set solver libmamba
    
# Install the package as normal:
# the prod_environment.yaml file is downloaded from the git repo by the azure pipeline. 
# If this fails, then the repo is specified before this build doesn't have the prod_environment.yaml at its top level
RUN conda install -c conda-forge conda-pack
COPY prod_environment.yaml .
RUN conda env create -f prod_environment.yaml
RUN conda-pack -n csrs -o /tmp/env.tar
RUN conda clean --all --force-pkgs-dirs -y
RUN mkdir /env &&\
    cd /env &&\ 
    tar xf /tmp/env.tar &&\
    rm /tmp/env.tar
    
# We've put env in same path it'll be in final image,
# so now fix up paths:
RUN /env/bin/conda-unpack

# The runtime-stage image; we can use Alpine as the
# base image since the Conda env also includes Python
# for us.
FROM debian:bookworm-slim AS runtime

# Copy /env from the previous stage:
COPY --from=build /env /env
# Install git, wget
RUN apt-get update
RUN apt-get -y install git wget curl
RUN apt-get clean all
RUN apt-get purge
RUN rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

# Open 80 for http
EXPOSE 80

# When image is run, run the code with the environment
# activated env and git clone repo provided as first argument to setup_run_server.sh and cd to it
# and source run_server.sh there expecting it to start server listening to port 80
# Use bash instead of sh
COPY run_server.sh .

# Copy the bootstrap database
COPY /database /database

ENTRYPOINT ["/bin/bash", "run_server.sh", "CentralValleyModeling/csrs"]