Metrics

Custom user metrics

It is possible to export custom user metrics by adding the metrics_client argument to the predictor constructor.

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"})

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