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training_job.pb.go
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training_job.pb.go
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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: flyteidl/plugins/sagemaker/training_job.proto
package plugins
import (
fmt "fmt"
proto "github.com/golang/protobuf/proto"
_ "github.com/golang/protobuf/ptypes/duration"
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 InputMode_Value int32
const (
InputMode_FILE InputMode_Value = 0
InputMode_PIPE InputMode_Value = 1
)
var InputMode_Value_name = map[int32]string{
0: "FILE",
1: "PIPE",
}
var InputMode_Value_value = map[string]int32{
"FILE": 0,
"PIPE": 1,
}
func (x InputMode_Value) String() string {
return proto.EnumName(InputMode_Value_name, int32(x))
}
func (InputMode_Value) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{0, 0}
}
type AlgorithmName_Value int32
const (
AlgorithmName_CUSTOM AlgorithmName_Value = 0
AlgorithmName_XGBOOST AlgorithmName_Value = 1
)
var AlgorithmName_Value_name = map[int32]string{
0: "CUSTOM",
1: "XGBOOST",
}
var AlgorithmName_Value_value = map[string]int32{
"CUSTOM": 0,
"XGBOOST": 1,
}
func (x AlgorithmName_Value) String() string {
return proto.EnumName(AlgorithmName_Value_name, int32(x))
}
func (AlgorithmName_Value) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{1, 0}
}
type InputContentType_Value int32
const (
InputContentType_TEXT_CSV InputContentType_Value = 0
)
var InputContentType_Value_name = map[int32]string{
0: "TEXT_CSV",
}
var InputContentType_Value_value = map[string]int32{
"TEXT_CSV": 0,
}
func (x InputContentType_Value) String() string {
return proto.EnumName(InputContentType_Value_name, int32(x))
}
func (InputContentType_Value) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{2, 0}
}
type DistributedProtocol_Value int32
const (
// Use this value if the user wishes to use framework-native distributed training interfaces.
// If this value is used, Flyte won't configure SageMaker to initialize unnecessary components such as
// OpenMPI or Parameter Server.
DistributedProtocol_UNSPECIFIED DistributedProtocol_Value = 0
// Use this value if the user wishes to use MPI as the underlying protocol for her distributed training job
// MPI is a framework-agnostic distributed protocol. It has multiple implementations. Currently, we have only
// tested the OpenMPI implementation, which is the recommended implementation for Horovod.
DistributedProtocol_MPI DistributedProtocol_Value = 1
)
var DistributedProtocol_Value_name = map[int32]string{
0: "UNSPECIFIED",
1: "MPI",
}
var DistributedProtocol_Value_value = map[string]int32{
"UNSPECIFIED": 0,
"MPI": 1,
}
func (x DistributedProtocol_Value) String() string {
return proto.EnumName(DistributedProtocol_Value_name, int32(x))
}
func (DistributedProtocol_Value) EnumDescriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{5, 0}
}
// The input mode that the algorithm supports. When using the File input mode, SageMaker downloads
// the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker
// volume for training container. When using Pipe input mode, Amazon SageMaker streams data directly
// from S3 to the container.
// See: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AlgorithmSpecification.html
// For the input modes that different SageMaker algorithms support, see:
// https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
type InputMode struct {
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *InputMode) Reset() { *m = InputMode{} }
func (m *InputMode) String() string { return proto.CompactTextString(m) }
func (*InputMode) ProtoMessage() {}
func (*InputMode) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{0}
}
func (m *InputMode) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_InputMode.Unmarshal(m, b)
}
func (m *InputMode) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_InputMode.Marshal(b, m, deterministic)
}
func (m *InputMode) XXX_Merge(src proto.Message) {
xxx_messageInfo_InputMode.Merge(m, src)
}
func (m *InputMode) XXX_Size() int {
return xxx_messageInfo_InputMode.Size(m)
}
func (m *InputMode) XXX_DiscardUnknown() {
xxx_messageInfo_InputMode.DiscardUnknown(m)
}
var xxx_messageInfo_InputMode proto.InternalMessageInfo
// The algorithm name is used for deciding which pre-built image to point to.
// This is only required for use cases where SageMaker's built-in algorithm mode is used.
// While we currently only support a subset of the algorithms, more will be added to the list.
// See: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
type AlgorithmName struct {
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *AlgorithmName) Reset() { *m = AlgorithmName{} }
func (m *AlgorithmName) String() string { return proto.CompactTextString(m) }
func (*AlgorithmName) ProtoMessage() {}
func (*AlgorithmName) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{1}
}
func (m *AlgorithmName) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_AlgorithmName.Unmarshal(m, b)
}
func (m *AlgorithmName) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_AlgorithmName.Marshal(b, m, deterministic)
}
func (m *AlgorithmName) XXX_Merge(src proto.Message) {
xxx_messageInfo_AlgorithmName.Merge(m, src)
}
func (m *AlgorithmName) XXX_Size() int {
return xxx_messageInfo_AlgorithmName.Size(m)
}
func (m *AlgorithmName) XXX_DiscardUnknown() {
xxx_messageInfo_AlgorithmName.DiscardUnknown(m)
}
var xxx_messageInfo_AlgorithmName proto.InternalMessageInfo
// Specifies the type of file for input data. Different SageMaker built-in algorithms require different file types of input data
// See https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
// https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
type InputContentType struct {
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *InputContentType) Reset() { *m = InputContentType{} }
func (m *InputContentType) String() string { return proto.CompactTextString(m) }
func (*InputContentType) ProtoMessage() {}
func (*InputContentType) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{2}
}
func (m *InputContentType) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_InputContentType.Unmarshal(m, b)
}
func (m *InputContentType) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_InputContentType.Marshal(b, m, deterministic)
}
func (m *InputContentType) XXX_Merge(src proto.Message) {
xxx_messageInfo_InputContentType.Merge(m, src)
}
func (m *InputContentType) XXX_Size() int {
return xxx_messageInfo_InputContentType.Size(m)
}
func (m *InputContentType) XXX_DiscardUnknown() {
xxx_messageInfo_InputContentType.DiscardUnknown(m)
}
var xxx_messageInfo_InputContentType proto.InternalMessageInfo
// Specifies a metric that the training algorithm writes to stderr or stdout.
// This object is a pass-through.
// See this for details: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_MetricDefinition.html
type MetricDefinition struct {
// User-defined name of the metric
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// SageMaker hyperparameter tuning parses your algorithm’s stdout and stderr streams to find algorithm metrics
Regex string `protobuf:"bytes,2,opt,name=regex,proto3" json:"regex,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *MetricDefinition) Reset() { *m = MetricDefinition{} }
func (m *MetricDefinition) String() string { return proto.CompactTextString(m) }
func (*MetricDefinition) ProtoMessage() {}
func (*MetricDefinition) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{3}
}
func (m *MetricDefinition) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_MetricDefinition.Unmarshal(m, b)
}
func (m *MetricDefinition) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_MetricDefinition.Marshal(b, m, deterministic)
}
func (m *MetricDefinition) XXX_Merge(src proto.Message) {
xxx_messageInfo_MetricDefinition.Merge(m, src)
}
func (m *MetricDefinition) XXX_Size() int {
return xxx_messageInfo_MetricDefinition.Size(m)
}
func (m *MetricDefinition) XXX_DiscardUnknown() {
xxx_messageInfo_MetricDefinition.DiscardUnknown(m)
}
var xxx_messageInfo_MetricDefinition proto.InternalMessageInfo
func (m *MetricDefinition) GetName() string {
if m != nil {
return m.Name
}
return ""
}
func (m *MetricDefinition) GetRegex() string {
if m != nil {
return m.Regex
}
return ""
}
// Specifies the training algorithm to be used in the training job
// This object is mostly a pass-through, with a couple of exceptions include: (1) in Flyte, users don't need to specify
// TrainingImage; either use the built-in algorithm mode by using Flytekit's Simple Training Job and specifying an algorithm
// name and an algorithm version or (2) when users want to supply custom algorithms they should set algorithm_name field to
// CUSTOM. In this case, the value of the algorithm_version field has no effect
// For pass-through use cases: refer to this AWS official document for more details
// https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AlgorithmSpecification.html
type AlgorithmSpecification struct {
// The input mode can be either PIPE or FILE
InputMode InputMode_Value `protobuf:"varint,1,opt,name=input_mode,json=inputMode,proto3,enum=flyteidl.plugins.sagemaker.InputMode_Value" json:"input_mode,omitempty"`
// The algorithm name is used for deciding which pre-built image to point to
AlgorithmName AlgorithmName_Value `protobuf:"varint,2,opt,name=algorithm_name,json=algorithmName,proto3,enum=flyteidl.plugins.sagemaker.AlgorithmName_Value" json:"algorithm_name,omitempty"`
// The algorithm version field is used for deciding which pre-built image to point to
// This is only needed for use cases where SageMaker's built-in algorithm mode is chosen
AlgorithmVersion string `protobuf:"bytes,3,opt,name=algorithm_version,json=algorithmVersion,proto3" json:"algorithm_version,omitempty"`
// A list of metric definitions for SageMaker to evaluate/track on the progress of the training job
// See this: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AlgorithmSpecification.html
// and this: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics.html
MetricDefinitions []*MetricDefinition `protobuf:"bytes,4,rep,name=metric_definitions,json=metricDefinitions,proto3" json:"metric_definitions,omitempty"`
// The content type of the input
// See https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
// https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
InputContentType InputContentType_Value `protobuf:"varint,5,opt,name=input_content_type,json=inputContentType,proto3,enum=flyteidl.plugins.sagemaker.InputContentType_Value" json:"input_content_type,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *AlgorithmSpecification) Reset() { *m = AlgorithmSpecification{} }
func (m *AlgorithmSpecification) String() string { return proto.CompactTextString(m) }
func (*AlgorithmSpecification) ProtoMessage() {}
func (*AlgorithmSpecification) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{4}
}
func (m *AlgorithmSpecification) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_AlgorithmSpecification.Unmarshal(m, b)
}
func (m *AlgorithmSpecification) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_AlgorithmSpecification.Marshal(b, m, deterministic)
}
func (m *AlgorithmSpecification) XXX_Merge(src proto.Message) {
xxx_messageInfo_AlgorithmSpecification.Merge(m, src)
}
func (m *AlgorithmSpecification) XXX_Size() int {
return xxx_messageInfo_AlgorithmSpecification.Size(m)
}
func (m *AlgorithmSpecification) XXX_DiscardUnknown() {
xxx_messageInfo_AlgorithmSpecification.DiscardUnknown(m)
}
var xxx_messageInfo_AlgorithmSpecification proto.InternalMessageInfo
func (m *AlgorithmSpecification) GetInputMode() InputMode_Value {
if m != nil {
return m.InputMode
}
return InputMode_FILE
}
func (m *AlgorithmSpecification) GetAlgorithmName() AlgorithmName_Value {
if m != nil {
return m.AlgorithmName
}
return AlgorithmName_CUSTOM
}
func (m *AlgorithmSpecification) GetAlgorithmVersion() string {
if m != nil {
return m.AlgorithmVersion
}
return ""
}
func (m *AlgorithmSpecification) GetMetricDefinitions() []*MetricDefinition {
if m != nil {
return m.MetricDefinitions
}
return nil
}
func (m *AlgorithmSpecification) GetInputContentType() InputContentType_Value {
if m != nil {
return m.InputContentType
}
return InputContentType_TEXT_CSV
}
// When enabling distributed training on a training job, the user should use this message to tell Flyte and SageMaker
// what kind of distributed protocol he/she wants to use to distribute the work.
type DistributedProtocol struct {
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *DistributedProtocol) Reset() { *m = DistributedProtocol{} }
func (m *DistributedProtocol) String() string { return proto.CompactTextString(m) }
func (*DistributedProtocol) ProtoMessage() {}
func (*DistributedProtocol) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{5}
}
func (m *DistributedProtocol) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_DistributedProtocol.Unmarshal(m, b)
}
func (m *DistributedProtocol) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_DistributedProtocol.Marshal(b, m, deterministic)
}
func (m *DistributedProtocol) XXX_Merge(src proto.Message) {
xxx_messageInfo_DistributedProtocol.Merge(m, src)
}
func (m *DistributedProtocol) XXX_Size() int {
return xxx_messageInfo_DistributedProtocol.Size(m)
}
func (m *DistributedProtocol) XXX_DiscardUnknown() {
xxx_messageInfo_DistributedProtocol.DiscardUnknown(m)
}
var xxx_messageInfo_DistributedProtocol proto.InternalMessageInfo
// TrainingJobResourceConfig is a pass-through, specifying the instance type to use for the training job, the
// number of instances to launch, and the size of the ML storage volume the user wants to provision
// Refer to SageMaker official doc for more details: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html
type TrainingJobResourceConfig struct {
// The number of ML compute instances to use. For distributed training, provide a value greater than 1.
InstanceCount int64 `protobuf:"varint,1,opt,name=instance_count,json=instanceCount,proto3" json:"instance_count,omitempty"`
// The ML compute instance type
InstanceType string `protobuf:"bytes,2,opt,name=instance_type,json=instanceType,proto3" json:"instance_type,omitempty"`
// The size of the ML storage volume that you want to provision.
VolumeSizeInGb int64 `protobuf:"varint,3,opt,name=volume_size_in_gb,json=volumeSizeInGb,proto3" json:"volume_size_in_gb,omitempty"`
// When users specify an instance_count > 1, Flyte will try to configure SageMaker to enable distributed training.
// If the users wish to use framework-agnostic distributed protocol such as MPI or Parameter Server, this
// field should be set to the corresponding enum value
DistributedProtocol DistributedProtocol_Value `protobuf:"varint,4,opt,name=distributed_protocol,json=distributedProtocol,proto3,enum=flyteidl.plugins.sagemaker.DistributedProtocol_Value" json:"distributed_protocol,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *TrainingJobResourceConfig) Reset() { *m = TrainingJobResourceConfig{} }
func (m *TrainingJobResourceConfig) String() string { return proto.CompactTextString(m) }
func (*TrainingJobResourceConfig) ProtoMessage() {}
func (*TrainingJobResourceConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{6}
}
func (m *TrainingJobResourceConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_TrainingJobResourceConfig.Unmarshal(m, b)
}
func (m *TrainingJobResourceConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_TrainingJobResourceConfig.Marshal(b, m, deterministic)
}
func (m *TrainingJobResourceConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_TrainingJobResourceConfig.Merge(m, src)
}
func (m *TrainingJobResourceConfig) XXX_Size() int {
return xxx_messageInfo_TrainingJobResourceConfig.Size(m)
}
func (m *TrainingJobResourceConfig) XXX_DiscardUnknown() {
xxx_messageInfo_TrainingJobResourceConfig.DiscardUnknown(m)
}
var xxx_messageInfo_TrainingJobResourceConfig proto.InternalMessageInfo
func (m *TrainingJobResourceConfig) GetInstanceCount() int64 {
if m != nil {
return m.InstanceCount
}
return 0
}
func (m *TrainingJobResourceConfig) GetInstanceType() string {
if m != nil {
return m.InstanceType
}
return ""
}
func (m *TrainingJobResourceConfig) GetVolumeSizeInGb() int64 {
if m != nil {
return m.VolumeSizeInGb
}
return 0
}
func (m *TrainingJobResourceConfig) GetDistributedProtocol() DistributedProtocol_Value {
if m != nil {
return m.DistributedProtocol
}
return DistributedProtocol_UNSPECIFIED
}
// The spec of a training job. This is mostly a pass-through object
// https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html
type TrainingJob struct {
AlgorithmSpecification *AlgorithmSpecification `protobuf:"bytes,1,opt,name=algorithm_specification,json=algorithmSpecification,proto3" json:"algorithm_specification,omitempty"`
TrainingJobResourceConfig *TrainingJobResourceConfig `protobuf:"bytes,2,opt,name=training_job_resource_config,json=trainingJobResourceConfig,proto3" json:"training_job_resource_config,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *TrainingJob) Reset() { *m = TrainingJob{} }
func (m *TrainingJob) String() string { return proto.CompactTextString(m) }
func (*TrainingJob) ProtoMessage() {}
func (*TrainingJob) Descriptor() ([]byte, []int) {
return fileDescriptor_6a68f64d8fd9fe30, []int{7}
}
func (m *TrainingJob) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_TrainingJob.Unmarshal(m, b)
}
func (m *TrainingJob) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_TrainingJob.Marshal(b, m, deterministic)
}
func (m *TrainingJob) XXX_Merge(src proto.Message) {
xxx_messageInfo_TrainingJob.Merge(m, src)
}
func (m *TrainingJob) XXX_Size() int {
return xxx_messageInfo_TrainingJob.Size(m)
}
func (m *TrainingJob) XXX_DiscardUnknown() {
xxx_messageInfo_TrainingJob.DiscardUnknown(m)
}
var xxx_messageInfo_TrainingJob proto.InternalMessageInfo
func (m *TrainingJob) GetAlgorithmSpecification() *AlgorithmSpecification {
if m != nil {
return m.AlgorithmSpecification
}
return nil
}
func (m *TrainingJob) GetTrainingJobResourceConfig() *TrainingJobResourceConfig {
if m != nil {
return m.TrainingJobResourceConfig
}
return nil
}
func init() {
proto.RegisterEnum("flyteidl.plugins.sagemaker.InputMode_Value", InputMode_Value_name, InputMode_Value_value)
proto.RegisterEnum("flyteidl.plugins.sagemaker.AlgorithmName_Value", AlgorithmName_Value_name, AlgorithmName_Value_value)
proto.RegisterEnum("flyteidl.plugins.sagemaker.InputContentType_Value", InputContentType_Value_name, InputContentType_Value_value)
proto.RegisterEnum("flyteidl.plugins.sagemaker.DistributedProtocol_Value", DistributedProtocol_Value_name, DistributedProtocol_Value_value)
proto.RegisterType((*InputMode)(nil), "flyteidl.plugins.sagemaker.InputMode")
proto.RegisterType((*AlgorithmName)(nil), "flyteidl.plugins.sagemaker.AlgorithmName")
proto.RegisterType((*InputContentType)(nil), "flyteidl.plugins.sagemaker.InputContentType")
proto.RegisterType((*MetricDefinition)(nil), "flyteidl.plugins.sagemaker.MetricDefinition")
proto.RegisterType((*AlgorithmSpecification)(nil), "flyteidl.plugins.sagemaker.AlgorithmSpecification")
proto.RegisterType((*DistributedProtocol)(nil), "flyteidl.plugins.sagemaker.DistributedProtocol")
proto.RegisterType((*TrainingJobResourceConfig)(nil), "flyteidl.plugins.sagemaker.TrainingJobResourceConfig")
proto.RegisterType((*TrainingJob)(nil), "flyteidl.plugins.sagemaker.TrainingJob")
}
func init() {
proto.RegisterFile("flyteidl/plugins/sagemaker/training_job.proto", fileDescriptor_6a68f64d8fd9fe30)
}
var fileDescriptor_6a68f64d8fd9fe30 = []byte{
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