Metrics

Custom user metrics

It is possible to export custom user metrics by adding the metrics_client argument to the predictor constructor. Below there is an example of how to use the metrics client with the PythonPredictor type. The implementation would be similar to other predictor types.

class PythonPredictor:
    def __init__(self, config, metrics_client):
        self.metrics = metrics_client

    def predict(self, payload):
        # --- my predict code here ---
        result = ...

        # increment a counter with name "my_metric" and tags model:v1
        self.metrics.increment(metric="my_counter", value=1, tags={"model": "v1"})

        # set the value for a gauge with name "my_gauge" and tags model:v1
        self.metrics.gauge(metric="my_gauge", value=42, tags={"model": "v1"})

        # set the value for an histogram with name "my_histogram" and tags model:v1
        self.metrics.histogram(metric="my_histogram", value=100, tags={"model": "v1"})

Refer to the observability documentation for more information on custom metrics.

Note: The metrics client uses the UDP protocol to push metrics, to be fault tolerant, so if it fails during a metrics push there is no exception thrown.