mlflow-dockerfile/Dockerfile

# Build an image that can serve mlflow models.
FROM ubuntu:20.04

RUN apt-get -y update
RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends          wget          curl          nginx          ca-certificates          bzip2          build-essential          cmake          openjdk-8-jdk          git-core          maven     && rm -rf /var/lib/apt/lists/*


# Setup miniconda
RUN curl -L https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh >> miniconda.sh
RUN bash ./miniconda.sh -b -p /miniconda && rm ./miniconda.sh
ENV PATH="/miniconda/bin:$PATH"



ENV JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
ENV GUNICORN_CMD_ARGS="--timeout 60 -k gevent"
# Set up the program in the image
WORKDIR /opt/mlflow

RUN pip install mlflow==2.1.1
RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.1.1:pom -DoutputDirectory=/opt/java 
RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.1.1:jar -DoutputDirectory=/opt/java/jars 
RUN cp /opt/java/mlflow-scoring-2.1.1.pom /opt/java/pom.xml
RUN cd /opt/java && mvn --batch-mode dependency:copy-dependencies -DoutputDirectory=/opt/java/jars 



                    COPY model_dir/recommender-model /opt/ml/model
                    RUN python -c                     'from mlflow.models.container import _install_pyfunc_deps;                    _install_pyfunc_deps(                        "/opt/ml/model",                         install_mlflow=False,                         enable_mlserver=False,                         env_manager="conda")'
                    ENV MLFLOW_DISABLE_ENV_CREATION="true"
                    ENV ENABLE_MLSERVER=False
                    

# granting read/write access and conditional execution authority to all child directories 
# and files to allow for deployment to AWS Sagemaker Serverless Endpoints 
# (see https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)
RUN chmod o+rwX /opt/mlflow/

ENTRYPOINT ["python", "-c", "from mlflow.models import container as C;C._serve('conda')"]