Dataset Source: Moro,S., Rita,P., and Cortez,P.. (2012). Bank Marketing. UCI Machine Learning Repository.
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
From within the trainer/ directory, we will first build our custom trainer docker image
export REGION=<your project region>
export PROJECT_ID=<your project id>
export REPO_NAME=<your image artifactory repository name>
export IMAGE_NAME=<name to give your trainer image>
export IMAGE_TAG=<tag to give your trainer image>
export IMAGE_URI={REGION}-docker.pkg.dev/${PROJECT_ID}/${REPO_NAME}/${IMAGE_NAME}:${IMAGE_TAG}
docker build -f Dockerfile -t ${IMAGE_URI} ./
Let's verify the container image by running it as a container locally
docker run ${IMAGE_URI}
If the local run works, you can push the container to Artifact Registry.
docker push ${IMAGE_URI}
Next, from inside the predictor/ directory, run the Dockerfile.ipynb python notebook that will essentially also push the predictor container to artifact registry.
With both the trainer as well as predictor container images in place, you can import the model to model registry. To do so, you can follow the steps described at official Vertex AI documentation. Remember to select "import an existing custom container" for both training and prediction containers.
Upon doing this successfully, Vertex AI will import the model to the registry and will also start the training job.
When the training job finishes, you can follow the steps described at official Vertex AI documentation to deploy a model to an endpoint. Deploying the model may take some minutes.
One final thing - we would also want to log the requests and the responses that we send to the endpoint. To do so, we need to enable Request-response logging. This can be done by running the following commands:
export REGION=<your project region>
export PROJECT_ID=<your project id>
export ENDPOINT_ID=<the endpoint id>
curl -X PATCH -H "Authorization: Bearer $(gcloud auth print-access-token)" -H "Content-Type: application/json" "https://$REGION-aiplatform.googleapis.com/v1/projects/$PROJECT_ID/locations/$REGION/endpoints/$ENDPOINT_ID" -d '{"predict_request_response_logging_config": {"enabled": true,"sampling_rate": 1,"bigquery_destination": {"output_uri": "bq://<project id>.<bigquery dataset to store the logging>.<bigquery table name to store the logging>"}}}'
Important note that the table passed for store the logs should have the following schema:
| Field name | Type | Mode |
|---|---|---|
| endpoint | STRING | NULLABLE |
| deployed_ model_ id | STRING | NULLABLE |
| logging_ time | TIMESTAMP | NULLABLE |
| request_ id | NUMERIC | NULLABLE |
| request_ payload | STRING | REPEATED |
| response_ payload | STRING | REPEATED |
Once the model is deployed, then note the endpoint id and run the ping_endpoint.ipynb python notebook to send a payload to the endpoint and receive predictions.