Gopi

.dvc/.gitignore

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+/cache
+/config.local
+/tmp

.dvc/config

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+[core]
+    remote = myremote
+    autostage = true
+['remote "myremote"']
+    url = gs://stock_price_prediction_dataset/DVC
+['remote "gcs_remote"']
+    url = gs://stock_price_prediction_dataset/Data

.dvcignore

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+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore
+

.github/workflows/cloudbuild.yaml

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+steps:
+  # Step 1: Clone the repository
+  - name: 'gcr.io/cloud-builders/git'
+    entrypoint: 'bash'
+    args:
+      - '-c'
+      - |
+        git clone https://github.com/IE7374-MachineLearningOperations/StockPricePrediction.git &&
+        cd StockPricePrediction &&
+        echo "Repository cloned successfully"
+
+  # Step 2: Upload specific files to GCP Bucket
+  - name: 'gcr.io/cloud-builders/gsutil'
+    args:
+      - '-m'
+      - 'cp'
+      - '-r'
+      - 'StockPricePrediction/*.py'
+      - 'StockPricePrediction/*.ipynb'
+      - 'StockPricePrediction/*.pkl'
+      - 'gs://stock_price_prediction_dataset/'
+
+  # Step 3: Install dependencies
+  - name: 'gcr.io/cloud-builders/pip'
+    args:
+      - 'install'
+      - '-r'
+      - 'StockPricePrediction/requirements.txt'
+
+#   # Step 4: Train the model
+#   - name: 'gcr.io/cloud-builders/python'
+#     args:
+#       - 'StockPricePrediction/train.py'
+
+#   # Step 5: Validate the model
+#   - name: 'gcr.io/cloud-builders/python'
+#     args:
+#       - 'StockPricePrediction/validate.py'
+
+#   # Step 6: Conditional deployment if validation is successful
+#   - name: 'gcr.io/cloud-builders/bash'
+#     id: 'Check Validation'
+#     args:
+#       - '-c'
+#       - |
+#         ACCURACY=$(python StockPricePrediction/validate.py --get_accuracy) &&
+#         if (( $(echo "$ACCURACY > 0.70" | bc -l) )); then
+#           echo "Model accuracy is sufficient, proceeding with deployment";
+#         else
+#           echo "Model accuracy is insufficient, stopping deployment";
+#           exit 1;
+#         fi
+
+#   # Step 7: Save the trained model to GCP Bucket
+#   - name: 'gcr.io/cloud-builders/gsutil'
+#     args:
+#       - 'cp'
+#       - 'StockPricePrediction/models/*.h5'
+#       - 'gs://stock_price_prediction_dataset/trained_models/'
+
+#   # Step 8: Run Unit Tests
+#   - name: 'gcr.io/cloud-builders/python'
+#     args:
+#       - '-m'
+#       - 'unittest'
+#       - 'discover'
+#       - '-s'
+#       - 'StockPricePrediction/tests'
+
+# artifacts:
+#   objects:
+#     location: 'gs://stock_price_prediction_dataset/artifacts/'
+#     paths:
+#       - 'StockPricePrediction/*.py'
+#       - 'StockPricePrediction/*.ipynb'
+#       - 'StockPricePrediction/*.h5'
+
+# timeout: '1200s'

.github/workflows/model.yml

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+name: Model Training
+
+on:
+  push:
+    branches:
+      - main  
+
+jobs:
+  train_model:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Step 1: Checkout repository
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      # Step 2: Set up Python
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.10.5'
+
+      # Step 3: Install all dependencies
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      # Step 4: Run Py Tests
+      - name: Run Tests
+        run: |
+          pytest StockPricePrediction/pipeline/airflow/tests --maxfail=1 --disable-warnings
+
+      # Step 5: Authenticate with GCP (Main Branch Only)
+      - name: Authenticate to GCP
+        if: ${{ github.ref == 'refs/heads/main' }}  
+        env:
+          GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }}
+          GCP_SERVICE_ACCOUNT_KEY: ${{ secrets.GCP_SERVICE_ACCOUNT_KEY }}
+        run: |
+          echo "${GCP_SERVICE_ACCOUNT_KEY}" | base64 --decode > ${HOME}/gcp-key.json
+          gcloud auth activate-service-account --key-file=${HOME}/gcp-key.json
+          gcloud config set project ${GCP_PROJECT_ID}
+
+      # # Step 6: Conditional Model Training
+      # - name: Trigger Model Training
+      #   env:
+      #     GCP_BUCKET_NAME: ${{ secrets.GCP_BUCKET_NAME }}
+      #   run: |
+      #     if [ "${{ github.ref }}" == "refs/heads/main" ]; then
+      #       # Production Training on Google Cloud AI Platform
+      #       gcloud ai-platform jobs submit training model_training_$(date +%Y%m%d_%H%M%S) \
+      #         --region us-central1 \
+      #         --module-name trainer.task \
+      #         --package-path ./trainer \
+      #         --python-version 3.10 \
+      #         --runtime-version 2.5 \
+      #         --job-dir gs://${GCP_BUCKET_NAME}/models/training_$(date +%Y%m%d_%H%M%S) \
+      #         -- \
+      #         --additional_training_args
+      #     else
+      #       # Test Training Locally for the test branch
+      #       echo "Running model training locally for testing"
+      #       python trainer/task.py --test_data ./data/test_data.csv
+      #     fi
+
+      # # Step 7: Save Training Logs as Artifacts
+      # - name: Upload Training Logs
+      #   if: always()
+      #   uses: actions/upload-artifact@v3
+      #   with:
+      #     name: training-logs
+      #     path: logs/
+
+      # # Step 8: Send notification on failure
+      # - name: Notify on failure
+      #   if: failure()
+      #   uses: actions/github-script@v6
+      #   with:
+      #     script: |
+      #       github.issues.createComment({
+      #         issue_number: context.issue.number,
+      #         body: 'Model training failed on `${{ github.ref }}` branch. Please check logs for details.'
+      #       })