Get Started with Kepler Model Server

Step 1: Learn about Pipeline

Step 2: Learn how to obtain power model

There are two ways to obtain power model: static and dynamic.

Static configuration

A static way is to download the model directly from INIT_URL. It can be set via environment variable directly or via kepler-cfm Kepler config map. For example,

export NODE_COMPONENTS_INIT_URL= < Static URL >

or

apiVersion: v1
kind: ConfigMap
metadata:
name: kepler-cfm
namespace: system
data:
    MODEL_CONFIG: |
        NODE_COMPONENTS_INIT_URL= < Static URL >

Dynamic via server API

A dynamic way is to enable the model server to auto select the power model which has the best accuracy and supported the running cluster environment. Similarly, It can be set via the environment variable or set it via Kepler config map.

export MODEL_SERVER_ENABLE=true

or

apiVersion: v1
kind: ConfigMap
metadata:
name: kepler-cfm
namespace: system
data:
    MODEL_CONFIG: |
        MODEL_SERVER_ENABLE: "true"

Step 3: Learn how to use the power model

There are two ways to use the models regarding the model format. If the model format can be processed directly inside the Kepler exporter such as Linear Regression weight in json format. There is no extra cofiguration. However, if the model is in the general format archived in zip, It is needed to enable the estimator sidecar via environment variable or Kepler config map.

export NODE_COMPONENTS_ESTIMATOR=true

or

apiVersion: v1
kind: ConfigMap
metadata:
name: kepler-cfm
namespace: system
data:
    MODEL_CONFIG: |
        NODE_COMPONENTS_ESTIMATOR=true

See more in Kepler Power Estimation Deployment

Step 4: Learn how to train the power model and give back to the community