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# This is an example on how to create your own custom strategy
import pydantic as pd
import robusta_krr
from robusta_krr.api.models import K8sWorkload, MetricsPodData, ResourceRecommendation, ResourceType, RunResult
from robusta_krr.api.strategies import BaseStrategy, StrategySettings
from robusta_krr.core.integrations.prometheus.metrics import MaxMemoryLoader, PercentileCPULoader
# Providing description to the settings will make it available in the CLI help
class CustomStrategySettings(StrategySettings):
param_1: float = pd.Field(99, gt=0, description="First example parameter")
param_2: float = pd.Field(105_000, gt=0, description="Second example parameter")
class CustomStrategy(BaseStrategy[CustomStrategySettings]):
"""
A custom strategy that uses the provided parameters for CPU and memory.
Made only in order to demonstrate how to create a custom strategy.
"""
display_name = "custom" # The name of the strategy
rich_console = True # Whether to use rich console for the CLI
metrics = [PercentileCPULoader(90), MaxMemoryLoader] # The metrics to use for the strategy
def run(self, history_data: MetricsPodData, object_data: K8sWorkload) -> RunResult:
return {
ResourceType.CPU: ResourceRecommendation(request=self.settings.param_1, limit=None),
ResourceType.Memory: ResourceRecommendation(request=self.settings.param_2, limit=self.settings.param_2),
}
# Running this file will register the strategy and make it available to the CLI
# Run it as `python ./custom_strategy.py my_strategy`
if __name__ == "__main__":
robusta_krr.run()
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