runtimev2

type Info struct {
	// Labels and Annotations to add to the RuntimeJobTemplate.
	Labels      map[string]string
	Annotations map[string]string
	// Original policy values from the runtime.
	RuntimePolicy RuntimePolicy
	// Trainer parameters to add to the RuntimeJobTemplate.
	Trainer
	// Scheduler parameters to add to the RuntimeJobTemplate.
	*Scheduler
}

type InfoOption func(options *InfoOptions)

type InfoOptions struct {
	// contains filtered or unexported fields
}

type ReconcilerBuilder func(*builder.Builder, client.Client, cache.Cache) *builder.Builder

type Runtime interface {
	NewObjects(ctx context.Context, trainJob *kubeflowv2.TrainJob) ([]client.Object, error)
	TerminalCondition(ctx context.Context, trainJob *kubeflowv2.TrainJob) (*metav1.Condition, error)
	EventHandlerRegistrars() []ReconcilerBuilder
	ValidateObjects(ctx context.Context, old, new *kubeflowv2.TrainJob) (admission.Warnings, field.ErrorList)
}

type RuntimePolicy struct {
	MLPolicy       *kubeflowv2.MLPolicy
	PodGroupPolicy *kubeflowv2.PodGroupPolicy
}

type Scheduler struct {
	PodLabels     map[string]string
	TotalRequests map[string]TotalResourceRequest
}

type TotalResourceRequest struct {
	Replicas    int32
	PodRequests corev1.ResourceList
}

type Trainer struct {
	NumNodes *int32
	// TODO (andreyvelich). Potentially, we can use map for env and sort it to improve code.
	// Context: https://github.com/kubeflow/training-operator/pull/2308#discussion_r1823267183
	Env           []corev1.EnvVar
	ContainerPort *corev1.ContainerPort
}