hyperparameter_tuning_job.pb.go

// Code generated by protoc-gen-go. DO NOT EDIT.
// source: flyteidl/plugins/sagemaker/hyperparameter_tuning_job.proto

package plugins

import (
	fmt "fmt"
	proto "github.com/golang/protobuf/proto"
	math "math"
)

// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf

// This is a compile-time assertion to ensure that this generated file
// is compatible with the proto package it is being compiled against.
// A compilation error at this line likely means your copy of the
// proto package needs to be updated.
const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package

type HyperparameterTuningObjectiveType_Value int32

const (
	HyperparameterTuningObjectiveType_MINIMIZE HyperparameterTuningObjectiveType_Value = 0
	HyperparameterTuningObjectiveType_MAXIMIZE HyperparameterTuningObjectiveType_Value = 1
)

var HyperparameterTuningObjectiveType_Value_name = map[int32]string{
	0: "MINIMIZE",
	1: "MAXIMIZE",
}

var HyperparameterTuningObjectiveType_Value_value = map[string]int32{
	"MINIMIZE": 0,
	"MAXIMIZE": 1,
}

func (x HyperparameterTuningObjectiveType_Value) String() string {
	return proto.EnumName(HyperparameterTuningObjectiveType_Value_name, int32(x))
}

func (HyperparameterTuningObjectiveType_Value) EnumDescriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{1, 0}
}

type HyperparameterTuningStrategy_Value int32

const (
	HyperparameterTuningStrategy_BAYESIAN HyperparameterTuningStrategy_Value = 0
	HyperparameterTuningStrategy_RANDOM   HyperparameterTuningStrategy_Value = 1
)

var HyperparameterTuningStrategy_Value_name = map[int32]string{
	0: "BAYESIAN",
	1: "RANDOM",
}

var HyperparameterTuningStrategy_Value_value = map[string]int32{
	"BAYESIAN": 0,
	"RANDOM":   1,
}

func (x HyperparameterTuningStrategy_Value) String() string {
	return proto.EnumName(HyperparameterTuningStrategy_Value_name, int32(x))
}

func (HyperparameterTuningStrategy_Value) EnumDescriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{3, 0}
}

type TrainingJobEarlyStoppingType_Value int32

const (
	TrainingJobEarlyStoppingType_OFF  TrainingJobEarlyStoppingType_Value = 0
	TrainingJobEarlyStoppingType_AUTO TrainingJobEarlyStoppingType_Value = 1
)

var TrainingJobEarlyStoppingType_Value_name = map[int32]string{
	0: "OFF",
	1: "AUTO",
}

var TrainingJobEarlyStoppingType_Value_value = map[string]int32{
	"OFF":  0,
	"AUTO": 1,
}

func (x TrainingJobEarlyStoppingType_Value) String() string {
	return proto.EnumName(TrainingJobEarlyStoppingType_Value_name, int32(x))
}

func (TrainingJobEarlyStoppingType_Value) EnumDescriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{4, 0}
}

// A pass-through for SageMaker's hyperparameter tuning job
type HyperparameterTuningJob struct {
	// The underlying training job that the hyperparameter tuning job will launch during the process
	TrainingJob *TrainingJob `protobuf:"bytes,1,opt,name=training_job,json=trainingJob,proto3" json:"training_job,omitempty"`
	// The maximum number of training jobs that an hpo job can launch. For resource limit purpose.
	// https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html
	MaxNumberOfTrainingJobs int64 `protobuf:"varint,2,opt,name=max_number_of_training_jobs,json=maxNumberOfTrainingJobs,proto3" json:"max_number_of_training_jobs,omitempty"`
	// The maximum number of concurrent training job that an hpo job can launch
	// https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html
	MaxParallelTrainingJobs int64    `protobuf:"varint,3,opt,name=max_parallel_training_jobs,json=maxParallelTrainingJobs,proto3" json:"max_parallel_training_jobs,omitempty"`
	XXX_NoUnkeyedLiteral    struct{} `json:"-"`
	XXX_unrecognized        []byte   `json:"-"`
	XXX_sizecache           int32    `json:"-"`
}

func (m *HyperparameterTuningJob) Reset()         { *m = HyperparameterTuningJob{} }
func (m *HyperparameterTuningJob) String() string { return proto.CompactTextString(m) }
func (*HyperparameterTuningJob) ProtoMessage()    {}
func (*HyperparameterTuningJob) Descriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{0}
}

func (m *HyperparameterTuningJob) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_HyperparameterTuningJob.Unmarshal(m, b)
}
func (m *HyperparameterTuningJob) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_HyperparameterTuningJob.Marshal(b, m, deterministic)
}
func (m *HyperparameterTuningJob) XXX_Merge(src proto.Message) {
	xxx_messageInfo_HyperparameterTuningJob.Merge(m, src)
}
func (m *HyperparameterTuningJob) XXX_Size() int {
	return xxx_messageInfo_HyperparameterTuningJob.Size(m)
}
func (m *HyperparameterTuningJob) XXX_DiscardUnknown() {
	xxx_messageInfo_HyperparameterTuningJob.DiscardUnknown(m)
}

var xxx_messageInfo_HyperparameterTuningJob proto.InternalMessageInfo

func (m *HyperparameterTuningJob) GetTrainingJob() *TrainingJob {
	if m != nil {
		return m.TrainingJob
	}
	return nil
}

func (m *HyperparameterTuningJob) GetMaxNumberOfTrainingJobs() int64 {
	if m != nil {
		return m.MaxNumberOfTrainingJobs
	}
	return 0
}

func (m *HyperparameterTuningJob) GetMaxParallelTrainingJobs() int64 {
	if m != nil {
		return m.MaxParallelTrainingJobs
	}
	return 0
}

// HyperparameterTuningObjectiveType determines the direction of the tuning of the Hyperparameter Tuning Job
// with respect to the specified metric.
type HyperparameterTuningObjectiveType struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *HyperparameterTuningObjectiveType) Reset()         { *m = HyperparameterTuningObjectiveType{} }
func (m *HyperparameterTuningObjectiveType) String() string { return proto.CompactTextString(m) }
func (*HyperparameterTuningObjectiveType) ProtoMessage()    {}
func (*HyperparameterTuningObjectiveType) Descriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{1}
}

func (m *HyperparameterTuningObjectiveType) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_HyperparameterTuningObjectiveType.Unmarshal(m, b)
}
func (m *HyperparameterTuningObjectiveType) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_HyperparameterTuningObjectiveType.Marshal(b, m, deterministic)
}
func (m *HyperparameterTuningObjectiveType) XXX_Merge(src proto.Message) {
	xxx_messageInfo_HyperparameterTuningObjectiveType.Merge(m, src)
}
func (m *HyperparameterTuningObjectiveType) XXX_Size() int {
	return xxx_messageInfo_HyperparameterTuningObjectiveType.Size(m)
}
func (m *HyperparameterTuningObjectiveType) XXX_DiscardUnknown() {
	xxx_messageInfo_HyperparameterTuningObjectiveType.DiscardUnknown(m)
}

var xxx_messageInfo_HyperparameterTuningObjectiveType proto.InternalMessageInfo

// The target metric and the objective of the hyperparameter tuning.
// https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics.html
type HyperparameterTuningObjective struct {
	// HyperparameterTuningObjectiveType determines the direction of the tuning of the Hyperparameter Tuning Job
	// with respect to the specified metric.
	ObjectiveType HyperparameterTuningObjectiveType_Value `protobuf:"varint,1,opt,name=objective_type,json=objectiveType,proto3,enum=flyteidl.plugins.sagemaker.HyperparameterTuningObjectiveType_Value" json:"objective_type,omitempty"`
	// The target metric name, which is the user-defined name of the metric specified in the
	// training job's algorithm specification
	MetricName           string   `protobuf:"bytes,2,opt,name=metric_name,json=metricName,proto3" json:"metric_name,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *HyperparameterTuningObjective) Reset()         { *m = HyperparameterTuningObjective{} }
func (m *HyperparameterTuningObjective) String() string { return proto.CompactTextString(m) }
func (*HyperparameterTuningObjective) ProtoMessage()    {}
func (*HyperparameterTuningObjective) Descriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{2}
}

func (m *HyperparameterTuningObjective) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_HyperparameterTuningObjective.Unmarshal(m, b)
}
func (m *HyperparameterTuningObjective) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_HyperparameterTuningObjective.Marshal(b, m, deterministic)
}
func (m *HyperparameterTuningObjective) XXX_Merge(src proto.Message) {
	xxx_messageInfo_HyperparameterTuningObjective.Merge(m, src)
}
func (m *HyperparameterTuningObjective) XXX_Size() int {
	return xxx_messageInfo_HyperparameterTuningObjective.Size(m)
}
func (m *HyperparameterTuningObjective) XXX_DiscardUnknown() {
	xxx_messageInfo_HyperparameterTuningObjective.DiscardUnknown(m)
}

var xxx_messageInfo_HyperparameterTuningObjective proto.InternalMessageInfo

func (m *HyperparameterTuningObjective) GetObjectiveType() HyperparameterTuningObjectiveType_Value {
	if m != nil {
		return m.ObjectiveType
	}
	return HyperparameterTuningObjectiveType_MINIMIZE
}

func (m *HyperparameterTuningObjective) GetMetricName() string {
	if m != nil {
		return m.MetricName
	}
	return ""
}

// Setting the strategy used when searching in the hyperparameter space
// Refer this doc for more details:
// https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-now-supports-random-search-and-hyperparameter-scaling/
type HyperparameterTuningStrategy struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *HyperparameterTuningStrategy) Reset()         { *m = HyperparameterTuningStrategy{} }
func (m *HyperparameterTuningStrategy) String() string { return proto.CompactTextString(m) }
func (*HyperparameterTuningStrategy) ProtoMessage()    {}
func (*HyperparameterTuningStrategy) Descriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{3}
}

func (m *HyperparameterTuningStrategy) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_HyperparameterTuningStrategy.Unmarshal(m, b)
}
func (m *HyperparameterTuningStrategy) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_HyperparameterTuningStrategy.Marshal(b, m, deterministic)
}
func (m *HyperparameterTuningStrategy) XXX_Merge(src proto.Message) {
	xxx_messageInfo_HyperparameterTuningStrategy.Merge(m, src)
}
func (m *HyperparameterTuningStrategy) XXX_Size() int {
	return xxx_messageInfo_HyperparameterTuningStrategy.Size(m)
}
func (m *HyperparameterTuningStrategy) XXX_DiscardUnknown() {
	xxx_messageInfo_HyperparameterTuningStrategy.DiscardUnknown(m)
}

var xxx_messageInfo_HyperparameterTuningStrategy proto.InternalMessageInfo

// When the training jobs launched by the hyperparameter tuning job are not improving significantly,
// a hyperparameter tuning job can be stopping early.
// Note that there's only a subset of built-in algorithms that supports early stopping.
// see: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html
type TrainingJobEarlyStoppingType struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *TrainingJobEarlyStoppingType) Reset()         { *m = TrainingJobEarlyStoppingType{} }
func (m *TrainingJobEarlyStoppingType) String() string { return proto.CompactTextString(m) }
func (*TrainingJobEarlyStoppingType) ProtoMessage()    {}
func (*TrainingJobEarlyStoppingType) Descriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{4}
}

func (m *TrainingJobEarlyStoppingType) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TrainingJobEarlyStoppingType.Unmarshal(m, b)
}
func (m *TrainingJobEarlyStoppingType) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TrainingJobEarlyStoppingType.Marshal(b, m, deterministic)
}
func (m *TrainingJobEarlyStoppingType) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TrainingJobEarlyStoppingType.Merge(m, src)
}
func (m *TrainingJobEarlyStoppingType) XXX_Size() int {
	return xxx_messageInfo_TrainingJobEarlyStoppingType.Size(m)
}
func (m *TrainingJobEarlyStoppingType) XXX_DiscardUnknown() {
	xxx_messageInfo_TrainingJobEarlyStoppingType.DiscardUnknown(m)
}

var xxx_messageInfo_TrainingJobEarlyStoppingType proto.InternalMessageInfo

// The specification of the hyperparameter tuning process
// https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex-tuning-job.html#automatic-model-tuning-ex-low-tuning-config
type HyperparameterTuningJobConfig struct {
	// ParameterRanges is a map that maps hyperparameter name to the corresponding hyperparameter range
	HyperparameterRanges *ParameterRanges `protobuf:"bytes,1,opt,name=hyperparameter_ranges,json=hyperparameterRanges,proto3" json:"hyperparameter_ranges,omitempty"`
	// Setting the strategy used when searching in the hyperparameter space
	TuningStrategy HyperparameterTuningStrategy_Value `protobuf:"varint,2,opt,name=tuning_strategy,json=tuningStrategy,proto3,enum=flyteidl.plugins.sagemaker.HyperparameterTuningStrategy_Value" json:"tuning_strategy,omitempty"`
	// The target metric and the objective of the hyperparameter tuning.
	TuningObjective *HyperparameterTuningObjective `protobuf:"bytes,3,opt,name=tuning_objective,json=tuningObjective,proto3" json:"tuning_objective,omitempty"`
	// When the training jobs launched by the hyperparameter tuning job are not improving significantly,
	// a hyperparameter tuning job can be stopping early.
	TrainingJobEarlyStoppingType TrainingJobEarlyStoppingType_Value `protobuf:"varint,4,opt,name=training_job_early_stopping_type,json=trainingJobEarlyStoppingType,proto3,enum=flyteidl.plugins.sagemaker.TrainingJobEarlyStoppingType_Value" json:"training_job_early_stopping_type,omitempty"`
	XXX_NoUnkeyedLiteral         struct{}                           `json:"-"`
	XXX_unrecognized             []byte                             `json:"-"`
	XXX_sizecache                int32                              `json:"-"`
}

func (m *HyperparameterTuningJobConfig) Reset()         { *m = HyperparameterTuningJobConfig{} }
func (m *HyperparameterTuningJobConfig) String() string { return proto.CompactTextString(m) }
func (*HyperparameterTuningJobConfig) ProtoMessage()    {}
func (*HyperparameterTuningJobConfig) Descriptor() ([]byte, []int) {
	return fileDescriptor_84374f4d1322c4ba, []int{5}
}

func (m *HyperparameterTuningJobConfig) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_HyperparameterTuningJobConfig.Unmarshal(m, b)
}
func (m *HyperparameterTuningJobConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_HyperparameterTuningJobConfig.Marshal(b, m, deterministic)
}
func (m *HyperparameterTuningJobConfig) XXX_Merge(src proto.Message) {
	xxx_messageInfo_HyperparameterTuningJobConfig.Merge(m, src)
}
func (m *HyperparameterTuningJobConfig) XXX_Size() int {
	return xxx_messageInfo_HyperparameterTuningJobConfig.Size(m)
}
func (m *HyperparameterTuningJobConfig) XXX_DiscardUnknown() {
	xxx_messageInfo_HyperparameterTuningJobConfig.DiscardUnknown(m)
}

var xxx_messageInfo_HyperparameterTuningJobConfig proto.InternalMessageInfo

func (m *HyperparameterTuningJobConfig) GetHyperparameterRanges() *ParameterRanges {
	if m != nil {
		return m.HyperparameterRanges
	}
	return nil
}

func (m *HyperparameterTuningJobConfig) GetTuningStrategy() HyperparameterTuningStrategy_Value {
	if m != nil {
		return m.TuningStrategy
	}
	return HyperparameterTuningStrategy_BAYESIAN
}

func (m *HyperparameterTuningJobConfig) GetTuningObjective() *HyperparameterTuningObjective {
	if m != nil {
		return m.TuningObjective
	}
	return nil
}

func (m *HyperparameterTuningJobConfig) GetTrainingJobEarlyStoppingType() TrainingJobEarlyStoppingType_Value {
	if m != nil {
		return m.TrainingJobEarlyStoppingType
	}
	return TrainingJobEarlyStoppingType_OFF
}

func init() {
	proto.RegisterEnum("flyteidl.plugins.sagemaker.HyperparameterTuningObjectiveType_Value", HyperparameterTuningObjectiveType_Value_name, HyperparameterTuningObjectiveType_Value_value)
	proto.RegisterEnum("flyteidl.plugins.sagemaker.HyperparameterTuningStrategy_Value", HyperparameterTuningStrategy_Value_name, HyperparameterTuningStrategy_Value_value)
	proto.RegisterEnum("flyteidl.plugins.sagemaker.TrainingJobEarlyStoppingType_Value", TrainingJobEarlyStoppingType_Value_name, TrainingJobEarlyStoppingType_Value_value)
	proto.RegisterType((*HyperparameterTuningJob)(nil), "flyteidl.plugins.sagemaker.HyperparameterTuningJob")
	proto.RegisterType((*HyperparameterTuningObjectiveType)(nil), "flyteidl.plugins.sagemaker.HyperparameterTuningObjectiveType")
	proto.RegisterType((*HyperparameterTuningObjective)(nil), "flyteidl.plugins.sagemaker.HyperparameterTuningObjective")
	proto.RegisterType((*HyperparameterTuningStrategy)(nil), "flyteidl.plugins.sagemaker.HyperparameterTuningStrategy")
	proto.RegisterType((*TrainingJobEarlyStoppingType)(nil), "flyteidl.plugins.sagemaker.TrainingJobEarlyStoppingType")
	proto.RegisterType((*HyperparameterTuningJobConfig)(nil), "flyteidl.plugins.sagemaker.HyperparameterTuningJobConfig")
}

func init() {
	proto.RegisterFile("flyteidl/plugins/sagemaker/hyperparameter_tuning_job.proto", fileDescriptor_84374f4d1322c4ba)
}

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