bridgeAI-model-baseimage

Model image creation for deployment

1. Set the environment variables given in the below table.
2. Update the python environment in .env file
3. Install poetry if not already installed
4. Install the dependencies using poetry poetry install
5. Add ./src to the PYTHONPATH - export PYTHONPATH="${PYTHONPATH}:./src"
6. Run python src/main.py or poetry run python src/main.py

Environment Variables

The following environment variables can be set:

Variable	Default Value	Description
MLFLOW_TRACKING_URI	https://mlflow.dc-mlops.co.uk	The URI for the MLFlow tracking server. Use http://mlflow-tracking:80 for kind cluster
MLFLOW_TRACKING_USERNAME	None	MLFlow tracking username. In kind cluster no need to set it as there is no authentication needed, but ensure that you set it on Production cluster.
MLFLOW_TRACKING_PASSWORD	None	MLFlow tracking password. In kind cluster no need to set it as there is no authentication needed, but ensure that you set it on Production cluster.
DEPLOY_MODEL_NAME	house_price_prediction_prod	The name of the model to be deployed
DEPLOY_MODEL_ALIAS	champion	The alias for the deployed model

Running the tests

Ensure that you have the project requirements already set up by following the Model image creation for deployment instructions

• Ensure pytest is installed. poetry install will install it as a dev dependency.
• • For integration tests, set up the dependencies (MLFlow) by running, docker-compose up -d
• Run the tests with poetry run pytest ./tests