InftyAI
Support autoscaling
As the service.Spec describes, we have minReplicas and maxReplicas, what we hope to do is adjust the number based on the traffic, aka. servreless. We can use ray or keda/knative as alternatives, but here we hope we can have a simple implementation, then no need to depend on other libraries.
For the first step, let's integrate with HPA for autoscaling capacities.
/assign
If the service controller needs to be integrated with hpa, I am willing to give it a try. Is it related to service.Spec.WorkloadTemplate.Replicas?
type ElasticConfig struct {
// MinReplicas indicates the minimum number of inference workloads based on the traffic.
// Default to nil means we can scale down the instances to 1.
// If minReplicas set to 0, it requires to install serverless component at first.
// +kubebuilder:default=1
// +optional
MinReplicas *int32 `json:"minReplicas,omitempty"`
// MaxReplicas indicates the maximum number of inference workloads based on the traffic.
// Default to nil means there's no limit for the instance number.
// +optional
MaxReplicas *int32 `json:"maxReplicas,omitempty"`
// Metrics contains the specifications which are used to calculate the
// desired replica count (the maximum replica count across all metrics will
// be used). The desired replica count is calculated with multiplying the
// ratio between the target value and the current value by the current
// number of pods. Ergo, metrics used must decrease as the pod count is
// increased, and vice-versa. See the individual metric source types for
// more information about how each type of metric must respond.
// If not set, the HPA will not be created.
// +optional
Metrics []autoscalingv2.MetricSpec `json:"metrics,omitempty"`
}
@kerthcet Should we integrate hpa metrics so that we can also set the required metrics in ElasticConfig
I will revisit this latter, but in my imagination, I just don't want to copy the fields from HPA to ElasticConfig, I hope it can work with various systems, like HPA, keda, so the fields should be abstract sufficiently.
Indeed,. That is, we only need to abstract the fields. The controller provides a provider-like interface (e.g. HPAProvides) internally. These features are implemented internally. right?
Some related metrics:
- vllm: https://github.com/vllm-project/vllm/issues/5041
- TGI: https://github.com/huggingface/text-generation-inference/issues/1977
@googs1025 would you like to implement the hpa as our first step, I think we have to align with lws right now which only supports hpa only.
But let's not use the autoscalingv2 library directly, let's build our structure instead. And we can break into 2 PRs, one for API and another for implementation. Tell us if you're interested, thanks anyway.