nightly-build-example-tests-ppml.yaml

name: Nightly Build Example Tests PPML Spark Local on Graphene

on:
  schedule:
    - cron: '0 17 * * *'
  workflow_dispatch:
    inputs:
      select_running_example:
        description: 'select which example to run'
        required: true
        default: 'all'
        type: choice
        options:
        - all
        - spark_arrow
      sgx_mem_size:
        description: 'memeory size limit'
        required: true
        default: 32G
        type: choice
        options:
        - 16G
        - 32G
        - 64G
        - 128G
      image:
        description: 'docker image version'
        required: true
        default: 10.239.45.10/arda/intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:latest
        type: string

  
jobs:
  example-tests-ppml:
    runs-on: [self-hosted, SGX, Wilwarin]
    permissions:
      contents: read
      packages: write

    steps:
    - uses: actions/checkout@v3
    - name: set variable
      env:
        DEFAULT_EXAMPLE: 'ALL'
        DEFAULT_SGX_MEM_SIZE: 32G
        DEFAULT_IMAGE: 10.239.45.10/arda/intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:latest
      run: |
        echo "SELECT_RUNNING_EXAMPLE=${{ github.event.inputs.select_running_example || env.DEFAULT_EXAMPLE }} " >> $GITHUB_ENV
        echo "SGX_MEM_SIZE=${{ github.event.inputs.sgx_mem_size || env.DEFAULT_MEM_SIZE }}" >> $GITHUB_ENV
        echo "IMAGE=${{ github.event.inputs.image || env.DEFAULT_IMAGE }}" >>$GITHUB_ENV
    - name: start container
      run: |
        set -x
        docker pull ${IMAGE}
        docker rm -f ${CONTAINER_NAME}
        docker run -id --privileged --net=host --name ${CONTAINER_NAME} \
        --cpuset-cpus=$CPUSET \
        --oom-kill-disable \
        --device=/dev/sgx/enclave \
        --device=/dev/sgx/provision \
        -v ~/glorysdj/kuberconfig:/root/.kube/config \
        -v /var/run/aesmd/aesm.socket:/var/run/aesmd/aesm.socket \
        -v $ENCLAVE_KEY:/graphene/Pal/src/host/Linux-SGX/signer/enclave-key.pem \
        -v $DATA_PATH:/ppml/trusted-big-data-ml/work/data \
        -v $KEYS_PATH:/ppml/trusted-big-data-ml/work/keys \
        -e LOCAL_IP=$LOCAL_IP \
        -e SGX_MEM_SIZE=$SGX_MEM_SIZE \
        -e RUNTIME_SPARK_MASTER=k8s://https://192.168.0.112:6443 \
        -e RUNTIME_K8S_SERVICE_ACCOUNT=spark \
        -e RUNTIME_K8S_SPARK_IMAGE=$IMAGE \
        -e RUNTIME_DRIVER_HOST=$LOCAL_IP \
        -e RUNTIME_DRIVER_PORT=54321 \
        -e RUNTIME_EXECUTOR_INSTANCES=1 \
        -e RUNTIME_EXECUTOR_CORES=4 \
        -e RUNTIME_EXECUTOR_MEMORY=80g \
        -e RUNTIME_TOTAL_EXECUTOR_CORES=4 \
        -e RUNTIME_DRIVER_CORES=4 \
        -e RUNTIME_DRIVER_MEMORY=10g \
        -e http_proxy=$HTTP_PROXY \
        -e https_proxy=$HTTPS_PROXY \
        $IMAGE bash
    - name: spark arrow test
      run: |
        if [ ${SECELT_RUNNING_EXAMPLE} = "spark_arrow" ]; then
        docker exec -i $CONTAINER_NAME bash -c " cd /ppml/trusted-big-data-ml && \
        /graphene/Tools/argv_serializer bash -c "export PYSPARK_PYTHON=/usr/bin/python && \
        export ARROW_PRE_0_15_IPC_FORMAT=0 && \
        /opt/jdk8/bin/java \
        -cp '/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/:/ppml/trusted-big-data-ml/work/spark-3.1.2/jars/*' \
        -Xmx2g org.apache.spark.deploy.SparkSubmit \
        --master 'local[4]' \
        --conf spark.python.use.daemon=false \
        --conf spark.python.worker.reuse=false \
        --conf spark.sql.execution.arrow.enabled=true \
        --conf spark.driver.memory=2g \
        --executor-memory 2g \
        /ppml/trusted-big-data-ml/work/spark-3.1.2/examples/src/main/python/sql/arrow.py "> /ppml/trusted-big-data-ml/secured-argvs \
        ./init.sh \
        SGX=1 ./pal_loader bash 2>&1 > test-sql-arrow-sgx.log"
        fi