FROM sklearn-base:0.23-1-arm64-cpu-py37
ENV SAGEMAKER_SKLEARN_VERSION 0.23-1-arm64

LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true

COPY requirements.txt /requirements.txt
RUN python -m pip install -r /requirements.txt && \
    rm /requirements.txt

COPY dist/sagemaker_sklearn_container-2.0-py3-none-any.whl /sagemaker_sklearn_container-2.0-py3-none-any.whl
# https://github.com/googleapis/google-cloud-python/issues/6647
RUN rm -rf /miniconda3/lib/python3.7/site-packages/numpy-1.19.4.dist-info && \
    pip install --no-cache /sagemaker_sklearn_container-2.0-py3-none-any.whl && \
    rm /sagemaker_sklearn_container-2.0-py3-none-any.whl

ENV SAGEMAKER_TRAINING_MODULE sagemaker_sklearn_container.training:main
ENV SAGEMAKER_SERVING_MODULE sagemaker_sklearn_container.serving:main

#######
# MMS #
#######
# Create MMS user directory
RUN useradd -m model-server
RUN mkdir -p /home/model-server/tmp
RUN chown -R model-server /home/model-server

# Copy MMS configs
COPY docker/$SAGEMAKER_SKLEARN_VERSION/resources/mms/config.properties.tmp /home/model-server
ENV SKLEARN_MMS_CONFIG=/home/model-server/config.properties

# Copy execution parameters endpoint plugin for MMS
RUN mkdir -p /tmp/plugins
COPY docker/$SAGEMAKER_SKLEARN_VERSION/resources/mms/endpoints-1.0.jar /tmp/plugins
RUN chmod +x /tmp/plugins/endpoints-1.0.jar

# Create directory for models
RUN mkdir -p /opt/ml/models
RUN chmod +rwx /opt/ml/models

#####################
# Required ENV vars #
#####################
# Set SageMaker training environment variables
ENV SM_INPUT /opt/ml/input
ENV SM_INPUT_TRAINING_CONFIG_FILE $SM_INPUT/config/hyperparameters.json
ENV SM_INPUT_DATA_CONFIG_FILE $SM_INPUT/config/inputdataconfig.json
ENV SM_CHECKPOINT_CONFIG_FILE $SM_INPUT/config/checkpointconfig.json

# Set SageMaker serving environment variables
ENV SM_MODEL_DIR /opt/ml/model

EXPOSE 8080
ENV TEMP=/home/model-server/tmp

# Required label for multi-model loading
LABEL com.amazonaws.sagemaker.capabilities.multi-models=true