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# 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')"]
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