wehub-resource-sync
39 lines
1.6 KiB
Plaintext
39 lines
1.6 KiB
Plaintext
# Build an image that can serve mlflow models.
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FROM ubuntu:22.04
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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
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# Setup miniconda
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RUN curl --fail -L https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh > miniconda.sh
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RUN bash ./miniconda.sh -b -p /miniconda && rm ./miniconda.sh
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ENV PATH="/miniconda/bin:$PATH"
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# Remove default channels to avoid `CondaToSNonInteractiveError`.
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# See https://github.com/mlflow/mlflow/pull/16752 for more details.
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RUN conda config --system --remove channels defaults && conda config --system --add channels conda-forge
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# Setup Java
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RUN apt-get install -y --no-install-recommends openjdk-17-jdk maven
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ENV JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64
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WORKDIR /opt/mlflow
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# Install MLflow
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RUN pip install ${{ MLFLOW_INSTALL }}
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# Copy model to image and install dependencies
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COPY model_dir/model /opt/ml/model
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RUN python -c "from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, env_manager='conda');"
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ENV MLFLOW_DISABLE_ENV_CREATION=True
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# granting read/write access and conditional execution authority to all child directories
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# and files to allow for deployment to AWS Sagemaker Serverless Endpoints
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# (see https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)
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RUN chmod o+rwX /opt/mlflow/
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# clean up apt cache to reduce image size
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RUN rm -rf /var/lib/apt/lists/*
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ENTRYPOINT ["python", "-c", "from mlflow.models import container as C; C._serve('conda')"]
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