a66b996cc1
This commit refactors the MLFlow activities to focus on model training rather than prediction operations. It removes prediction-related activities and metrics, and updates the MLFlow activity descriptions to reflect the change in focus. The README is also updated to reflect these changes.
27 lines
796 B
Python
27 lines
796 B
Python
"""
|
|
Model Manager Metrics Module
|
|
|
|
This module defines all Prometheus metrics used by the Sientia DataOps Model Manager system
|
|
for monitoring and observability. The metrics provide insights into system performance,
|
|
training operations, and operational health.
|
|
|
|
The metrics are designed to be scraped by Prometheus and can be visualized in
|
|
Grafana or other monitoring dashboards to provide real-time visibility into
|
|
the system's operation.
|
|
|
|
Key Metric Categories:
|
|
- Application Health: Overall system status and availability
|
|
|
|
Metric Labels:
|
|
- pod_id: Kubernetes pod identifier for multi-instance deployments
|
|
"""
|
|
|
|
from prometheus_client import Gauge
|
|
|
|
# Application health metric
|
|
APP_UP = Gauge(
|
|
'app_up',
|
|
'Indicates if the application is running (1) or shutting down (0)',
|
|
['pod_id'],
|
|
)
|