ray.serve.config.AutoscalingConfig#
- pydantic model ray.serve.config.AutoscalingConfig[source]#
Config for the Serve Autoscaler.
- field aggregation_function: str | AggregationFunction = AggregationFunction.MEAN#
Function used to aggregate metrics across a time window.
- field downscale_delay_s: float = 600.0#
How long to wait before scaling down replicas to a value greater than 0.
- Constraints:
ge = 0
- field downscale_smoothing_factor: float | None = None#
[DEPRECATED] Please use
downscaling_factorinstead.
- field downscale_to_zero_delay_s: float | None = None#
How long to wait before scaling down replicas from 1 to 0. If not set, the value of
downscale_delay_swill be used.
- field downscaling_factor: float | None = None#
Multiplicative “gain” factor to limit downscaling decisions.
- field look_back_period_s: float = 30.0#
Time window to average over for metrics.
- Constraints:
gt = 0
- field metrics_interval_s: float = 10.0#
[DEPRECATED] How often to scrape for metrics. Will be replaced by the environment variables
RAY_SERVE_REPLICA_AUTOSCALING_METRIC_PUSH_INTERVAL_SandRAY_SERVE_HANDLE_AUTOSCALING_METRIC_PUSH_INTERVAL_Sin a future release.- Constraints:
gt = 0
- field policy: AutoscalingPolicy [Optional]#
The autoscaling policy for the deployment. This option is experimental.
- field smoothing_factor: float = 1.0#
[DEPRECATED] Smoothing factor for autoscaling decisions.
- Constraints:
gt = 0
- field upscale_delay_s: float = 30.0#
How long to wait before scaling up replicas.
- Constraints:
ge = 0
- field upscale_smoothing_factor: float | None = None#
[DEPRECATED] Please use
upscaling_factorinstead.
- field upscaling_factor: float | None = None#
Multiplicative “gain” factor to limit upscaling decisions.
- validator aggregation_function_valid » aggregation_function[source]#
- validator look_back_period_s_valid » look_back_period_s[source]#
- validator metrics_interval_s_deprecation_warning » metrics_interval_s[source]#