README.md

bridgeAI-regression-model-data-ingestion

Data Ingestion and versioning

1. The data used is available here. Provide an accessible path to the csv file in the config.yaml file in data_url. Please ensure that the file can be downloaded using curl. Or you can provide the url in the environment variable DATA_URL
2. Update the python environment in .env file
3. Install poetry if not already installed
4. Install the dependencies using poetry poetry install
5. update the config and model parameters in the config.yaml file
6. Add ./src to the PYTHONPATH - export PYTHONPATH="${PYTHONPATH}:./src"
7. Run poetry run python src/main.py

The below manual steps are automated using the data ingestion dag in the DAGs repo

1. dvc init from the root of the repo to set the repo as a dvc repo if it is not already done
2. Add dvc remote

   dvc remote add -f <dvc-remote-name> <dvc-remote-path>

3. Add the files that needs to be tracked to dvc

   dvc add artefacts/test_data.csv artefacts/train_data.csv artefacts/val_data.csv

4. Add the dvc files to git

   git add artefacts/test_data.csv.dvc artefacts/train_data.csv.dvc artefacts/val_data.csv.dvc

5. Push the data to dvc remote

   dvc push -r <dvc-remote-name>

6. Git push and tag the repo with version of data for future use

Data ingestion and versioning - using docker

1. Build the docker image - docker build -t data-ingestion .
2. Run the container with the correct DATA_URL and DVC_REMOTE as environment variables. (Refer to the following Environment Variables table for complete list)
   docker run -e DVC_REMOTE=s3:some/remote -e DATA_URL=https://raw.githubusercontent.com/renjith-digicat/random_file_shares/main/HousingData.csv --rm data-ingestion

Data ingestion and versioning - using Airflow DAG (Recommended method)

1. Set up the kubernetes cluster and infrastructure required using Infrastructure repo
2. Access the airflow UI made available using the above infra repo
3. Update the airflow variables accordingly
4. Trigger the data_ingestion_dag

Once the DAG execution completed, the data ingestion repo will be updated with a new data version in the specified branch of the repo.

Environment Variables

The following environment variables can be set to configure the training:

Variable	Default Value	Description
DATA_URL	https://raw.githubusercontent.com/renjith-digicat/random_file_shares/main/HousingData.csv	Url to the raw data CSV data used for training
CONFIG_PATH	./config.yaml	File path to the data cleansing, versioning and other configuration file
LOG_LEVEL	INFO	The logging level for the application. Valid values are DEBUG, INFO, WARNING, ERROR, and CRITICAL.
DVC_REMOTE	/tmp/test-dvc-remote	A DVC remote path
DVC_ENDPOINT_URL	http://minio	The URL endpoint for the DVC storage backend. This is typically the URL of an S3-compatible service, such as MinIO, used to store and manage datasets and model files.
DVC_REMOTE_NAME	regression-model-remote	The name for the dvc remote
DVC_ACCESS_KEY_ID	None	The access key id for dvc remote endpoint url (default value is embedded in the infra repo)
DVC_SECRET_ACCESS_KEY	None	The secret access key for dvc remote endpoint url (default value is embedded in the infra repo)
AWS_DEFAULT_REGION	eu-west-2	The dvc remote s3 bucket region
GITHUB_USERNAME	None	Github username using which new data version files will be pushed to github (default value is embedded in the infra repo)
GITHUB_PASSWORD	None	Github token for the above username (default value is embedded in the infra repo)

Running the tests

Ensure that you have the project requirements already set up by following the Data Ingestion and versioning instructions

• Ensure pytest is installed. poetry install will install it as a dependency.

• Run the tests with poetry run pytest ./tests