forest_loss_driver_prediction.yaml

name: Forest-Loss-Driver-Prediction

on:
  workflow_dispatch:
  push:
    branches:
      - henryh/favyen/forest-loss-20240917

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}
  SERVICE_NAME: "rslearn_projects"

jobs:
  build:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write
    outputs:
      ghcr_docker_image: ${{ steps.image-names.outputs.ghcr_image_name }}
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Log in to the Container registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Extract metadata (tags, labels) for Docker
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: |
            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=sha,format=long
            type=sha,format=short
            type=raw,value=latest,enable={{is_default_branch}}

      - name: Cleanup disk space
        run: |
          sudo docker rmi $(docker image ls -aq) >/dev/null 2>&1 || true
          sudo docker image prune --all --force >/dev/null 2>&1 || true
          sudo rm -rf /usr/share/dotnet
          sudo rm -rf /opt/ghc
          sudo rm -rf /usr/local/share/boost

      - name: Build and push Docker image
        id: build-push
        uses: docker/build-push-action@v6
        with:
          context: .
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          # cache-from: type=gha
          # cache-to: type=gha,mode=max
          build-args: |
            GIT_USERNAME=${{ secrets.GIT_USERNAME }}
            GIT_TOKEN=${{ secrets.GIT_TOKEN }}

      - name: Store Image Names
        id: image-names
        run: |-
          GHCR_IMAGE="${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}@${{ steps.build-push.outputs.digest }}"
          GHCR_IMAGE=`echo ${GHCR_IMAGE} | tr '[:upper:]' '[:lower:]'` # docker requires that all image names be lowercase
          echo "ghcr.io Docker image name is ${GHCR_IMAGE}"
          echo "ghcr_image_name=\"${GHCR_IMAGE}\"" >> $GITHUB_OUTPUT

  predict:
    runs-on: ubuntu-latest-m
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Log in to the Container registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Cleanup disk space
        run: |
          sudo docker rmi $(docker image ls -aq) >/dev/null 2>&1 || true
          sudo docker image prune --all --force >/dev/null 2>&1 || true
          sudo rm -rf /usr/share/dotnet
          sudo rm -rf /opt/ghc
          sudo rm -rf /usr/local/share/boost

      - name: Authenticate into gcp
        uses: "google-github-actions/auth@v2"
        with:
          credentials_json: ${{ secrets.GOOGLE_CREDENTIALS }}

      - name: Debug script location
        run: |
          pwd
          ls -la

      - name: Run Extract Dataset Job on a VM and Launch Prediction Job on Beaker # We need all of these to become secrets
        run: |
          export GHCR_PAT=${{ secrets.GHCR_PAT_PULL_DOCKER_IMAGE }} && \
          export DATASET_EXTRACT_COMMAND="python -m rslp.main forest_loss_driver extract_dataset" && \
          export RSLP_PROJECT="forest_loss_driver" && \
          bash .github/workflows/deploy_image_on_vm.sh \
            --project-id ${{ secrets.GCP_PROJECT_ID }} \
            --zone "us-west1-b" \
            --machine-type "n2-standard-8" \
            --docker-image ${{ steps.image-names.outputs.ghcr_image_name }} \
            --command "$DATASET_EXTRACT_COMMAND" \
            --user ${{ secrets.GCP_USER }} \
            --ghcr-user ${{ secrets.GHCR_USER }} \
            --service-account ${{ secrets.FOREST_LOSS_DRIVER_INFERENCE_SERVICE_ACCOUNT }} \
            --delete no \
            --beaker-token ${{ secrets.BEAKER_TOKEN }} \
            --beaker-addr ${{ secrets.BEAKER_ADDR }} \
            --beaker-username ${{ secrets.BEAKER_USERNAME }} \
            --rslp-project $RSLP_PROJECT \
            --workflow predict \
            --rslp-prefix ${{ secrets.RSLP_PREFIX }} \
            --gpu-count 1 \
            --shared-memory "64Gib" \
            --cluster ${{ secrets.BEAKER_CLUSTER_INFERENCE }} \
            --priority "normal" \
            --task-name "${RSLP_PROJECT}_inference_$(uuidgen | cut -c1-8)" \
            --budget ${{ secrets.BEAKER_BUDGET }} \
            --workspace ${{ secrets.BEAKER_WORKSPACE }}

# FOR clean up i will just run this vm with an instance schedule for like a day or something and have it log how long everything took somewhere
# I want to just launch predict as a single beaker job and have it run on a gpu