1  # Build an image that can serve mlflow models.
 2  FROM ubuntu:22.04
 3  
 4  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
 5  
 6  # Setup miniconda
 7  RUN curl --fail -L https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh > miniconda.sh
 8  RUN bash ./miniconda.sh -b -p /miniconda && rm ./miniconda.sh
 9  ENV PATH="/miniconda/bin:$PATH"
10  # Remove default channels to avoid `CondaToSNonInteractiveError`.
11  # See https://github.com/mlflow/mlflow/pull/16752 for more details.
12  RUN conda config --system --remove channels defaults && conda config --system --add channels conda-forge
13  
14  
15  # Setup Java
16  RUN apt-get install -y --no-install-recommends openjdk-17-jdk maven
17  ENV JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64
18  
19  WORKDIR /opt/mlflow
20  
21  # Install MLflow
22  RUN pip install ${{ MLFLOW_INSTALL }}
23  
24  # Copy model to image and install dependencies
25  COPY model_dir/model /opt/ml/model
26  RUN python -c "from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=False, env_manager='conda');"
27  
28  ENV MLFLOW_DISABLE_ENV_CREATION=True
29  ENV ENABLE_MLSERVER=False
30  
31  # granting read/write access and conditional execution authority to all child directories
32  # and files to allow for deployment to AWS Sagemaker Serverless Endpoints
33  # (see https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)
34  RUN chmod o+rwX /opt/mlflow/
35  
36  # clean up apt cache to reduce image size
37  RUN rm -rf /var/lib/apt/lists/*
38  
39  ENTRYPOINT ["python", "-c", "from mlflow.models import container as C; C._serve('conda')"]