# Build an image that can serve mlflow models.
FROM ubuntu:22.04

RUN apt-get -y update && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends wget curl nginx ca-certificates bzip2 build-essential cmake git-core

# Setup miniconda
RUN curl --fail -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"
# Remove default channels to avoid `CondaToSNonInteractiveError`.
# See https://github.com/mlflow/mlflow/pull/16752 for more details.
RUN conda config --system --remove channels defaults && conda config --system --add channels conda-forge


# Setup Java
RUN apt-get install -y --no-install-recommends openjdk-17-jdk maven
ENV JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64

WORKDIR /opt/mlflow

# Install MLflow from local source
COPY mlflow-project /opt/mlflow
RUN pip install /opt/mlflow

# Install minimal serving dependencies
RUN python -c "from mlflow.models.container import _install_pyfunc_deps;_install_pyfunc_deps(None, False)"

ENV MLFLOW_DISABLE_ENV_CREATION=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/

# clean up apt cache to reduce image size
RUN rm -rf /var/lib/apt/lists/*

ENTRYPOINT ["python", "-c", "import sys; from mlflow.models import container as C; C._init(sys.argv[1], 'conda')"]