Pre-Deployment Validation

Validates code changes are ready for deployment. Covers local tests, Docker builds, CI/CD verification, and server deployment steps.

Validation Workflow

1. Local Validation     → Tests pass? Docker builds?
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2. CI/CD Verification   → GitHub Actions green?
        ↓
3. Pre-Deploy Checklist → Git clean? Docs updated?
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4. Server Deployment    → Pull images, test, verify
        ↓
5. Post-Deploy Check    → Workflow runs successfully?

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Prerequisites: Environment Setup

Before running modules locally, set the path to your organization-specific config file.

# Set path to your .env configuration file (adjust path and org config as needed)
export ieasyhydroforecast_env_file_path=/path/to/config/.env_develop

See apps/config/ for available configuration files. For detailed environment documentation, Claude can read the Serena memory suggested_commands.

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Phase 1: Local Validation

1.1 Run All Tests

cd apps
bash run_tests.sh

Expected: All modules pass. If any fail, fix before proceeding.

For specific modules (faster iteration):

# uv modules (Python 3.12)
cd apps/<module_name>
SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest test*/ -v

# Example: preprocessing_runoff
cd apps/preprocessing_runoff
SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest test/ -v

1.2 Build Docker Images Locally

Build base image first, then module images:

# From repository root

# Build base image
docker build -f apps/docker_base_image/Dockerfile \
  -t mabesa/sapphire-pythonbaseimage:latest .

# Build specific module (replace <module>)
docker build -f apps/<module>/Dockerfile \
  -t mabesa/sapphire-<shortname>:test .

Common module builds:

# Preprocessing runoff
docker build -f apps/preprocessing_runoff/Dockerfile \
  -t mabesa/sapphire-preprunoff:test .

# Linear regression
docker build -f apps/linear_regression/Dockerfile \
  -t mabesa/sapphire-linreg:test .

# Pipeline
docker build -f apps/pipeline/Dockerfile \
  -t mabesa/sapphire-pipeline:test .

1.3 Verify Docker Images Work

# Check Python version
docker run --rm mabesa/sapphire-<shortname>:test python --version

# Verify imports
docker run --rm mabesa/sapphire-<shortname>:test \
  python -c "import pandas; import numpy; print('OK')"

1.4 Local Validation Checklist

• [ ] bash run_tests.sh passes for all relevant modules
• [ ] Docker base image builds successfully
• [ ] Docker module images build successfully
• [ ] Docker images run basic verification commands

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Phase 2: CI/CD Verification

2.1 Check Git Status

git status

Expected: Working tree clean, or only expected uncommitted changes.

2.2 Push and Monitor CI

git push origin <branch-name>

Monitor: Go to GitHub Actions

2.3 CI/CD Checklist

• [ ] All commits pushed to remote
• [ ] GitHub Actions workflow started
• [ ] All test jobs pass (green checkmarks)
• [ ] All Docker build jobs pass
• [ ] No security warnings or failed attestations

If CI fails:

1. Check the failed job logs
2. Fix locally
3. Push again
4. Do NOT proceed to server deployment until CI is green

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Phase 3: Pre-Deploy Checklist

3.1 Code Review

• [ ] Changes are intentional and complete
• [ ] No debug code left in (print statements, hardcoded paths)
• [ ] No secrets or credentials in code
• [ ] Error handling is appropriate

3.2 Documentation

• [ ] doc/plans/phase7_package_upgrades.md updated if package-upgrade-related
• [ ] doc/plans/module_issues.md updated if fixing known issues
• [ ] README updated if user-facing changes

3.3 Dependencies

• [ ] pyproject.toml updated if dependencies changed
• [ ] uv.lock regenerated with uv lock
• [ ] Both files committed together

3.4 Git State

# Verify branch is up to date
git fetch origin
git status

# Check what will be deployed
git log origin/main..HEAD --oneline  # For main deployments
git log origin/implementation_planning..HEAD --oneline  # For feature branch

• [ ] All intended changes are committed
• [ ] Branch is pushed to remote
• [ ] No unintended commits included

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Phase 4: Server Deployment

Note: These steps are performed manually on the server.

4.1 SSH to Server

ssh <user>@<server-address>

4.2 Navigate to Deployment Directory

cd /path/to/deployment  # e.g., /kyg_data_forecast_tools

4.3 Pull Latest Images

# Pull all images
docker compose pull

# Or pull specific image
docker pull mabesa/sapphire-preprunoff:py312
docker pull mabesa/sapphire-linreg:py312
# etc.

4.4 Verify Images Updated

docker images | grep sapphire

Check that image timestamps are recent (just pushed from CI/CD).

4.5 Test Individual Module (Optional but Recommended)

Before running full workflow, test a single module:

# Run preprocessing to verify data access
docker compose run --rm preprocessing-runoff

# Check logs
docker compose logs --tail=50 preprocessing-runoff

4.6 Server Deployment Checklist

• [ ] SSH connection established
• [ ] Docker images pulled successfully
• [ ] Image timestamps are recent
• [ ] Test module runs without errors

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Phase 5: Post-Deployment Verification

5.1 Run Full Workflow

Trigger a complete forecast cycle:

# On server - run the pipeline
docker compose up pipeline
# or
./run_forecast.sh  # if using custom script

5.2 Monitor Execution

# Watch logs in real-time
docker compose logs -f

# Check specific module
docker compose logs --tail=100 preprocessing-runoff
docker compose logs --tail=100 linear-regression

5.3 Verify Outputs

• [ ] No errors in logs
• [ ] Output files created/updated (check timestamps)
• [ ] Data looks reasonable (spot check values)
• [ ] Dashboard accessible and showing new data (if applicable)

5.4 Known Issues Check

After deployment, verify known issues are resolved:

• [ ] P-001: Marker files cleaned up (if fix deployed)
• [ ] PR-001: Preprocessing runoff updates data (if fix deployed)

5.5 Post-Deploy Checklist

• [ ] Full workflow completes without errors
• [ ] Output files have correct timestamps
• [ ] Dashboard displays updated forecasts
• [ ] No new errors in logs

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Troubleshooting

Tests Fail Locally

1. Check error messages carefully
2. Ensure virtual environment is activated/synced: uv sync --all-extras
3. Check environment variables: SAPPHIRE_TEST_ENV=True
4. For integration tests, check if external services are available

Docker Build Fails

1. Ensure base image is built first
2. Check for syntax errors in Dockerfile
3. Verify all COPY sources exist
4. Check network connectivity for pip/uv installs

CI/CD Fails

1. Check which job failed in GitHub Actions
2. Compare local vs CI environment
3. Common issues:
   • Missing test dependencies
   • Path differences
   • Environment variable differences

Server Deployment Issues

1. Permission denied: Check file ownership on mounted volumes
2. Container exits immediately: Check logs for error messages
3. Data not updated: Check data source connectivity, date handling
4. iEasyHydro HF connection: Verify SSH tunnel is running

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Quick Reference: Module Test Commands

| Module | Test Command | |--------|--------------| | iEasyHydroForecast | cd apps/iEasyHydroForecast && SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest tests/ -v | | preprocessing_runoff | cd apps/preprocessing_runoff && SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest test/ -v | | linear_regression | cd apps/linear_regression && SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest test/ -v | | pipeline | cd apps/pipeline && SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest tests/ -v | | forecast_dashboard | cd apps/forecast_dashboard && SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest tests/ -v | | postprocessing_forecasts | cd apps/postprocessing_forecasts && SAPPHIRE_TEST_ENV=True .venv/bin/python -m pytest test/ -v |

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Related Skills

• issue-planning: If deployment reveals issues, use this skill to analyze and plan fixes
• software-architecture: For code design decisions during fixes