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‎.flake8

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[flake8]
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exclude = .git,__pycache__,venv,.venv
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max-line-length = 120

‎.github/workflows/workflow.yml

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# Documentation: https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsuses
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name: github_workflow
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run-name: GitHub Workflow
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env:
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## Common environment variables
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# Service name (must be lowercase and not contain any spaces)
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SERVICE_NAME: ${{ vars.SERVICE_NAME }}
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# Path to the model
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MODEL_PATH: ${{ vars.MODEL_PATH }}
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# S3 access key ID
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S3_ACCESS_KEY_ID: ${{ secrets.S3_ACCESS_KEY_ID }}
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# S3 secret access key
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S3_SECRET_ACCESS_KEY: ${{ secrets.S3_SECRET_ACCESS_KEY }}
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## Development environment variables
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# The URLs of the Core Engine to which the service should connect
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DEV_CORE_ENGINE_URLS: ${{ vars.DEV_CORE_ENGINE_URLS }}
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# The URL that the service (dev) should be accessible at
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DEV_SERVICE_URL: ${{ vars.DEV_SERVICE_URL }}
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# The Kubernetes namespace that the service should be deployed to
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DEV_NAMESPACE: ${{ vars.DEV_NAMESPACE }}
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# Maximum number of tasks the service can accept
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DEV_MAX_TASKS: ${{ vars.DEV_MAX_TASKS }}
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# Number of retries on the Engine for announcement
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DEV_ENGINE_ANNOUNCE_RETRIES: ${{ vars.DEV_ENGINE_ANNOUNCE_RETRIES }}
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# Delay between each retry
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DEV_ENGINE_ANNOUNCE_RETRY_DELAY: ${{ vars.DEV_ENGINE_ANNOUNCE_RETRY_DELAY }}
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# Logging level
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DEV_LOG_LEVEL: ${{ vars.DEV_LOG_LEVEL }}
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# Kube configuration
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DEV_KUBE_CONFIG: ${{ secrets.DEV_KUBE_CONFIG }}
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## Production environment variables
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# The URLs of the Core Engine to which the service should connect
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PROD_CORE_ENGINE_URLS: ${{ vars.PROD_CORE_ENGINE_URLS }}
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# The URL that the service (dev) should be accessible at
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PROD_SERVICE_URL: ${{ vars.PROD_SERVICE_URL }}
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# The Kubernetes namespace that the service should be deployed to
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PROD_NAMESPACE: ${{ vars.PROD_NAMESPACE }}
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# Maximum number of tasks the service can accept
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PROD_MAX_TASKS: ${{ vars.PROD_MAX_TASKS }}
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# Number of retries on the Engine for announcement
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PROD_ENGINE_ANNOUNCE_RETRIES: ${{ vars.PROD_ENGINE_ANNOUNCE_RETRIES }}
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# Delay between each retry
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PROD_ENGINE_ANNOUNCE_RETRY_DELAY: ${{ vars.PROD_ENGINE_ANNOUNCE_RETRY_DELAY }}
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# Logging level
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PROD_LOG_LEVEL: ${{ vars.PROD_LOG_LEVEL }}
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# Kube configuration
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PROD_KUBE_CONFIG: ${{ secrets.PROD_KUBE_CONFIG }}
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# Allow one concurrent deployment
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concurrency:
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group: github_workflow
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cancel-in-progress: true
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on:
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push:
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branches:
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- main
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- prod
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pull_request:
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:
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jobs:
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review:
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runs-on: ubuntu-latest
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if: ${{ vars.RUN_CICD == 'true' }}
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steps:
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- name: Clone repository
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uses: actions/checkout@v4
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- name: Lint Python app
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uses: swiss-ai-center/common-code/.github/actions/lint-python-app@main
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with:
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python-app-path: ./model-serving
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train:
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needs: review
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runs-on: ubuntu-latest
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if: ${{ vars.RUN_CICD == 'true' }}
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steps:
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- name: Clone repository
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uses: actions/checkout@v4
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- name: Run ML experiment with DVC
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uses: swiss-ai-center/common-code/.github/actions/run-ml-experiment-with-dvc@main
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with:
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ml-experiment-path: ./model-creation
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s3-access-key-id: ${{ env.S3_ACCESS_KEY_ID }}
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s3-secret-access-key: ${{ env.S3_SECRET_ACCESS_KEY }}
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- name: Upload model
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uses: actions/upload-artifact@v4
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with:
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name: model
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path: ${{ env.MODEL_PATH }}
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retention-days: 5
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test:
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needs: train
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runs-on: ubuntu-latest
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if: ${{ vars.RUN_CICD == 'true' }}
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steps:
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- name: Clone repository
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uses: actions/checkout@v4
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- name: Download model
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uses: actions/download-artifact@v3
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with:
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name: model
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path: ./model-serving
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- name: Test Python app
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uses: swiss-ai-center/common-code/.github/actions/test-python-app@main
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with:
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python-app-path: ./model-serving
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token: ${{ secrets.GITHUB_TOKEN }}
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release:
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needs: test
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runs-on: ubuntu-latest
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if: ${{ vars.RUN_CICD == 'true' && success() && github.ref == 'refs/heads/main' && (vars.DEPLOY_DEV == 'true' || vars.DEPLOY_PROD == 'true') }}
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steps:
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- name: Clone repository
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uses: actions/checkout@v4
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- name: Download model
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uses: actions/download-artifact@v4
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with:
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name: model
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path: ./model-serving
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- name: Build and push Docker image to GitHub
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id: build-and-push-docker-image-to-github
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uses: swiss-ai-center/common-code/.github/actions/build-and-push-docker-image-to-github@main
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with:
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docker-registry-username: ${{ github.actor }}
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docker-registry-password: ${{ secrets.GITHUB_TOKEN }}
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docker-image-name: ${{ github.repository }}
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docker-image-context: ./model-serving
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outputs:
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docker-image-tags: ${{ steps.build-and-push-docker-image-to-github.outputs.docker-image-tags }}
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deploy-dev:
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needs: release
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runs-on: ubuntu-latest
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if: ${{ vars.RUN_CICD == 'true' && success() && github.ref == 'refs/heads/main' && vars.DEPLOY_DEV == 'true' }}
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steps:
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- name: Clone repository
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uses: actions/checkout@v4
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- name: Get service Docker image SHA tag
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shell: bash
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run: |
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docker_image_tags=(${{ needs.release.outputs.docker-image-tags }})
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docker_image_sha_tag="${docker_image_tags[1]}"
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echo "SERVICE_DOCKER_IMAGE_SHA_TAG=$docker_image_sha_tag" >> "$GITHUB_ENV"
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- name: Prepare configuration files
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uses: swiss-ai-center/common-code/.github/actions/prepare-kubernetes-configuration-files@main
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with:
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service-name: ${{ env.SERVICE_NAME }}
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service-url: ${{ env.DEV_SERVICE_URL }}
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service-docker-image-tag: ${{ env.SERVICE_DOCKER_IMAGE_SHA_TAG }}
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configuration-files-location: ./model-serving/kubernetes
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environment: development
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log-level: ${{ env.DEV_LOG_LEVEL }}
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engine-urls: ${{ env.DEV_CORE_ENGINE_URLS }}
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max-tasks: ${{ env.DEV_MAX_TASKS }}
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engine-announce-retries: ${{ env.DEV_ENGINE_ANNOUNCE_RETRIES }}
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engine-announce-retry-delay: ${{ env.DEV_ENGINE_ANNOUNCE_RETRY_DELAY }}
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- name: Remove unnecessary keys from configuration files
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uses: swiss-ai-center/common-code/.github/actions/remove-unnecessary-keys-from-kubernetes-configuration-files@main
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with:
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configuration-files-location: ./kubernetes
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- name: Deploy service on the Kubernetes cluster
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uses: swiss-ai-center/common-code/.github/actions/execute-command-on-kubernetes-cluster@main
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with:
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kube-config: ${{ env.DEV_KUBE_CONFIG }}
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kube-namespace: ${{ env.DEV_NAMESPACE }}
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kubectl-context: ./model-serving/kubernetes
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kubectl-args: |
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apply \
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-f config-map.yml \
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-f stateful.yml \
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-f service.yml \
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-f ingress.yml
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deploy-prod:
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needs: release
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runs-on: ubuntu-latest
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if: ${{ vars.RUN_CICD == 'true' && success() && github.ref == 'refs/heads/main' && vars.DEPLOY_PROD == 'true' }}
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steps:
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- name: Clone repository
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uses: actions/checkout@v4
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- name: Get service Docker image SHA tag
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shell: bash
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run: |
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docker_image_tags=(${{ needs.release.outputs.docker-image-tags }})
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docker_image_sha_tag="${docker_image_tags[1]}"
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echo "SERVICE_DOCKER_IMAGE_SHA_TAG=$docker_image_sha_tag" >> "$GITHUB_ENV"
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- name: Prepare configuration files
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uses: swiss-ai-center/common-code/.github/actions/prepare-kubernetes-configuration-files@main
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with:
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service-name: ${{ env.SERVICE_NAME }}
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service-url: ${{ env.PROD_SERVICE_URL }}
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service-docker-image-tag: ${{ env.SERVICE_DOCKER_IMAGE_SHA_TAG }}
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configuration-files-location: ./model-serving/kubernetes
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environment: production
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log-level: ${{ env.PROD_LOG_LEVEL }}
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engine-urls: ${{ env.PROD_CORE_ENGINE_URLS }}
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max-tasks: ${{ env.PROD_MAX_TASKS }}
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engine-announce-retries: ${{ env.PROD_ENGINE_ANNOUNCE_RETRIES }}
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engine-announce-retry-delay: ${{ env.PROD_ENGINE_ANNOUNCE_RETRY_DELAY }}
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- name: Deploy service on the Kubernetes cluster
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uses: swiss-ai-center/common-code/.github/actions/execute-command-on-kubernetes-cluster@main
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with:
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kube-config: ${{ env.PROD_KUBE_CONFIG }}
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kube-namespace: ${{ env.PROD_NAMESPACE }}
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kubectl-context: ./model-serving/kubernetes
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kubectl-args: |
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apply \
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-f config-map.yml \
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-f stateful.yml \
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-f service.yml \
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-f ingress.yml

‎.gitignore

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## Python
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# Environments
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.venv
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venv
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# Byte-compiled / optimized / DLL files
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__pycache__/
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# Pytest cache
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.pytest_cache
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# Pytest Coverage
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.coverage
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## IntelliJ's IDEs
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.idea
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## Visual Studio Code
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.vscode
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## macOS
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.DS_Store

‎README.md

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# Create a new service (model from scratch) template
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This repository contains the Python + FastAPI template to create a service with
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a model built from scratch compatible with the Core engine.
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Please read the documentation at
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<https://docs.swiss-ai-center.ch/how-to-guides/how-to-create-a-new-service> to
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understand how to use this template.
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## Guidelines
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TODO: Add instructions on how to edit this template.
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### Publishing and deploying using a CI/CD pipeline
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This is the recommended way to publish and deploy your service if you have
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access to GitHub Actions or GitLab CI.
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TODO
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### Publishing and deploying manually
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This is the recommended way to publish and deploy your service if you do not
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have access to GitHub Actions or GitLab CI or do not want to use these services.
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TODO
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## Checklist
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These checklists allow you to ensure everything is set up correctly.
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### Common tasks
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- [ ] Rename the project in the [`pyproject.toml`](./pyproject.toml) file
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- [x] Add files that must be ignored to the [`.gitignore`](.gitignore) configuration file
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- [ ] TODO
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### Publishing and deploying using a CI/CD pipeline
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> [!NOTE]
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> This checklist is specific to the _Publishing and deploying using a CI/CD
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> pipeline_ section.
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- [x] Add the environment variables
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- [ ] TODO
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### Publishing and deploying manually
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> [!NOTE]
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> This checklist is specific to the _Publishing and deploying manually_ section.
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- [x] Edit the [`.env`](.env) configuration file
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- [ ] TODO

‎model-creation/README.md

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# model-creation
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<!-- TODO 1: Update the description of the service and the link to the documentation. -->
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_Check the [related documentation](https://docs.swiss-ai-center.ch/reference/core-concepts/service/) for more information._

‎model-serving/.env

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# Log level
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LOG_LEVEL=debug
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# Environment
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ENVIRONMENT=development
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# The Core engine URLs
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ENGINE_URLS=["http://localhost:8080"]
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# The Service Port
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SERVICE_PORT=9090
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# The Service URL+Port
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SERVICE_URL="http://host.docker.internal:${SERVICE_PORT}"
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# The maximum of tasks the service can process
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MAX_TASKS=50
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# The number of times the service tries to announce itself to the engine
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ENGINE_ANNOUNCE_RETRIES=5
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# The number of seconds between each retry
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ENGINE_ANNOUNCE_RETRY_DELAY=3

‎model-serving/.gitignore

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# Check parent `.gitignore` file for complete ignored files

‎model-serving/Dockerfile

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# Base image
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FROM python:3.11
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# Install all required packages to run the model
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# TODO: 1. Add any additional packages required to run your model
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# RUN apt update && apt install --yes package1 package2 ...
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# Work directory
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WORKDIR /app
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# Copy requirements file
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COPY ./requirements.txt .
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COPY ./requirements-all.txt .
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# Install dependencies
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RUN pip install --requirement requirements.txt --requirement requirements-all.txt
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# Copy sources
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COPY src src
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# Copy model
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# TODO: 2. Change the name of the model file to match the name of your model file
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COPY mnist_model.h5 .
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# Environment variables
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ENV ENVIRONMENT=${ENVIRONMENT}
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ENV LOG_LEVEL=${LOG_LEVEL}
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ENV ENGINE_URL=${ENGINE_URL}
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ENV MAX_TASKS=${MAX_TASKS}
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ENV ENGINE_ANNOUNCE_RETRIES=${ENGINE_ANNOUNCE_RETRIES}
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ENV ENGINE_ANNOUNCE_RETRY_DELAY=${ENGINE_ANNOUNCE_RETRY_DELAY}
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# Exposed ports
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EXPOSE 80
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# Switch to src directory
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WORKDIR "/app/src"
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# Command to run on start
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]

‎model-serving/README.md

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# model-serving
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<!-- TODO 1: Update the description of the service and the link to the documentation. -->
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_Check the [related documentation](https://docs.swiss-ai-center.ch/reference/core-concepts/service/) for more information._

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