AWS SDK for C++  1.8.95
AWS SDK for C++
Public Member Functions | List of all members
Aws::SageMaker::Model::ContainerDefinition Class Reference

#include <ContainerDefinition.h>

Public Member Functions

 ContainerDefinition ()
 
 ContainerDefinition (Aws::Utils::Json::JsonView jsonValue)
 
ContainerDefinitionoperator= (Aws::Utils::Json::JsonView jsonValue)
 
Aws::Utils::Json::JsonValue Jsonize () const
 
const Aws::StringGetContainerHostname () const
 
bool ContainerHostnameHasBeenSet () const
 
void SetContainerHostname (const Aws::String &value)
 
void SetContainerHostname (Aws::String &&value)
 
void SetContainerHostname (const char *value)
 
ContainerDefinitionWithContainerHostname (const Aws::String &value)
 
ContainerDefinitionWithContainerHostname (Aws::String &&value)
 
ContainerDefinitionWithContainerHostname (const char *value)
 
const Aws::StringGetImage () const
 
bool ImageHasBeenSet () const
 
void SetImage (const Aws::String &value)
 
void SetImage (Aws::String &&value)
 
void SetImage (const char *value)
 
ContainerDefinitionWithImage (const Aws::String &value)
 
ContainerDefinitionWithImage (Aws::String &&value)
 
ContainerDefinitionWithImage (const char *value)
 
const ImageConfigGetImageConfig () const
 
bool ImageConfigHasBeenSet () const
 
void SetImageConfig (const ImageConfig &value)
 
void SetImageConfig (ImageConfig &&value)
 
ContainerDefinitionWithImageConfig (const ImageConfig &value)
 
ContainerDefinitionWithImageConfig (ImageConfig &&value)
 
const ContainerModeGetMode () const
 
bool ModeHasBeenSet () const
 
void SetMode (const ContainerMode &value)
 
void SetMode (ContainerMode &&value)
 
ContainerDefinitionWithMode (const ContainerMode &value)
 
ContainerDefinitionWithMode (ContainerMode &&value)
 
const Aws::StringGetModelDataUrl () const
 
bool ModelDataUrlHasBeenSet () const
 
void SetModelDataUrl (const Aws::String &value)
 
void SetModelDataUrl (Aws::String &&value)
 
void SetModelDataUrl (const char *value)
 
ContainerDefinitionWithModelDataUrl (const Aws::String &value)
 
ContainerDefinitionWithModelDataUrl (Aws::String &&value)
 
ContainerDefinitionWithModelDataUrl (const char *value)
 
const Aws::Map< Aws::String, Aws::String > & GetEnvironment () const
 
bool EnvironmentHasBeenSet () const
 
void SetEnvironment (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetEnvironment (Aws::Map< Aws::String, Aws::String > &&value)
 
ContainerDefinitionWithEnvironment (const Aws::Map< Aws::String, Aws::String > &value)
 
ContainerDefinitionWithEnvironment (Aws::Map< Aws::String, Aws::String > &&value)
 
ContainerDefinitionAddEnvironment (const Aws::String &key, const Aws::String &value)
 
ContainerDefinitionAddEnvironment (Aws::String &&key, const Aws::String &value)
 
ContainerDefinitionAddEnvironment (const Aws::String &key, Aws::String &&value)
 
ContainerDefinitionAddEnvironment (Aws::String &&key, Aws::String &&value)
 
ContainerDefinitionAddEnvironment (const char *key, Aws::String &&value)
 
ContainerDefinitionAddEnvironment (Aws::String &&key, const char *value)
 
ContainerDefinitionAddEnvironment (const char *key, const char *value)
 
const Aws::StringGetModelPackageName () const
 
bool ModelPackageNameHasBeenSet () const
 
void SetModelPackageName (const Aws::String &value)
 
void SetModelPackageName (Aws::String &&value)
 
void SetModelPackageName (const char *value)
 
ContainerDefinitionWithModelPackageName (const Aws::String &value)
 
ContainerDefinitionWithModelPackageName (Aws::String &&value)
 
ContainerDefinitionWithModelPackageName (const char *value)
 

Detailed Description

Describes the container, as part of model definition.

See Also:

AWS API Reference

Definition at line 35 of file ContainerDefinition.h.

Constructor & Destructor Documentation

◆ ContainerDefinition() [1/2]

Aws::SageMaker::Model::ContainerDefinition::ContainerDefinition ( )

◆ ContainerDefinition() [2/2]

Aws::SageMaker::Model::ContainerDefinition::ContainerDefinition ( Aws::Utils::Json::JsonView  jsonValue)

Member Function Documentation

◆ AddEnvironment() [1/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( const Aws::String key,
const Aws::String value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 604 of file ContainerDefinition.h.

◆ AddEnvironment() [2/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( Aws::String &&  key,
const Aws::String value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 611 of file ContainerDefinition.h.

◆ AddEnvironment() [3/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( const Aws::String key,
Aws::String &&  value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 618 of file ContainerDefinition.h.

◆ AddEnvironment() [4/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( Aws::String &&  key,
Aws::String &&  value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 625 of file ContainerDefinition.h.

◆ AddEnvironment() [5/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( const char *  key,
Aws::String &&  value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 632 of file ContainerDefinition.h.

◆ AddEnvironment() [6/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( Aws::String &&  key,
const char *  value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 639 of file ContainerDefinition.h.

◆ AddEnvironment() [7/7]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::AddEnvironment ( const char *  key,
const char *  value 
)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 646 of file ContainerDefinition.h.

◆ ContainerHostnameHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::ContainerHostnameHasBeenSet ( ) const
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 76 of file ContainerDefinition.h.

◆ EnvironmentHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::EnvironmentHasBeenSet ( ) const
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 569 of file ContainerDefinition.h.

◆ GetContainerHostname()

const Aws::String& Aws::SageMaker::Model::ContainerDefinition::GetContainerHostname ( ) const
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 59 of file ContainerDefinition.h.

◆ GetEnvironment()

const Aws::Map<Aws::String, Aws::String>& Aws::SageMaker::Model::ContainerDefinition::GetEnvironment ( ) const
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 562 of file ContainerDefinition.h.

◆ GetImage()

const Aws::String& Aws::SageMaker::Model::ContainerDefinition::GetImage ( ) const
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 193 of file ContainerDefinition.h.

◆ GetImageConfig()

const ImageConfig& Aws::SageMaker::Model::ContainerDefinition::GetImageConfig ( ) const
inline

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

Definition at line 301 of file ContainerDefinition.h.

◆ GetMode()

const ContainerMode& Aws::SageMaker::Model::ContainerDefinition::GetMode ( ) const
inline

Whether the container hosts a single model or multiple models.

Definition at line 352 of file ContainerDefinition.h.

◆ GetModelDataUrl()

const Aws::String& Aws::SageMaker::Model::ContainerDefinition::GetModelDataUrl ( ) const
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 400 of file ContainerDefinition.h.

◆ GetModelPackageName()

const Aws::String& Aws::SageMaker::Model::ContainerDefinition::GetModelPackageName ( ) const
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 653 of file ContainerDefinition.h.

◆ ImageConfigHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::ImageConfigHasBeenSet ( ) const
inline

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

Definition at line 310 of file ContainerDefinition.h.

◆ ImageHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::ImageHasBeenSet ( ) const
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 207 of file ContainerDefinition.h.

◆ Jsonize()

Aws::Utils::Json::JsonValue Aws::SageMaker::Model::ContainerDefinition::Jsonize ( ) const

◆ ModeHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::ModeHasBeenSet ( ) const
inline

Whether the container hosts a single model or multiple models.

Definition at line 357 of file ContainerDefinition.h.

◆ ModelDataUrlHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::ModelDataUrlHasBeenSet ( ) const
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 422 of file ContainerDefinition.h.

◆ ModelPackageNameHasBeenSet()

bool Aws::SageMaker::Model::ContainerDefinition::ModelPackageNameHasBeenSet ( ) const
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 659 of file ContainerDefinition.h.

◆ operator=()

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::operator= ( Aws::Utils::Json::JsonView  jsonValue)

◆ SetContainerHostname() [1/3]

void Aws::SageMaker::Model::ContainerDefinition::SetContainerHostname ( const Aws::String value)
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 93 of file ContainerDefinition.h.

◆ SetContainerHostname() [2/3]

void Aws::SageMaker::Model::ContainerDefinition::SetContainerHostname ( Aws::String &&  value)
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 110 of file ContainerDefinition.h.

◆ SetContainerHostname() [3/3]

void Aws::SageMaker::Model::ContainerDefinition::SetContainerHostname ( const char *  value)
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 127 of file ContainerDefinition.h.

◆ SetEnvironment() [1/2]

void Aws::SageMaker::Model::ContainerDefinition::SetEnvironment ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 576 of file ContainerDefinition.h.

◆ SetEnvironment() [2/2]

void Aws::SageMaker::Model::ContainerDefinition::SetEnvironment ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 583 of file ContainerDefinition.h.

◆ SetImage() [1/3]

void Aws::SageMaker::Model::ContainerDefinition::SetImage ( const Aws::String value)
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 221 of file ContainerDefinition.h.

◆ SetImage() [2/3]

void Aws::SageMaker::Model::ContainerDefinition::SetImage ( Aws::String &&  value)
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 235 of file ContainerDefinition.h.

◆ SetImage() [3/3]

void Aws::SageMaker::Model::ContainerDefinition::SetImage ( const char *  value)
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 249 of file ContainerDefinition.h.

◆ SetImageConfig() [1/2]

void Aws::SageMaker::Model::ContainerDefinition::SetImageConfig ( const ImageConfig value)
inline

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

Definition at line 319 of file ContainerDefinition.h.

◆ SetImageConfig() [2/2]

void Aws::SageMaker::Model::ContainerDefinition::SetImageConfig ( ImageConfig &&  value)
inline

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

Definition at line 328 of file ContainerDefinition.h.

◆ SetMode() [1/2]

void Aws::SageMaker::Model::ContainerDefinition::SetMode ( const ContainerMode value)
inline

Whether the container hosts a single model or multiple models.

Definition at line 362 of file ContainerDefinition.h.

◆ SetMode() [2/2]

void Aws::SageMaker::Model::ContainerDefinition::SetMode ( ContainerMode &&  value)
inline

Whether the container hosts a single model or multiple models.

Definition at line 367 of file ContainerDefinition.h.

◆ SetModelDataUrl() [1/3]

void Aws::SageMaker::Model::ContainerDefinition::SetModelDataUrl ( const Aws::String value)
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 444 of file ContainerDefinition.h.

◆ SetModelDataUrl() [2/3]

void Aws::SageMaker::Model::ContainerDefinition::SetModelDataUrl ( Aws::String &&  value)
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 466 of file ContainerDefinition.h.

◆ SetModelDataUrl() [3/3]

void Aws::SageMaker::Model::ContainerDefinition::SetModelDataUrl ( const char *  value)
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 488 of file ContainerDefinition.h.

◆ SetModelPackageName() [1/3]

void Aws::SageMaker::Model::ContainerDefinition::SetModelPackageName ( const Aws::String value)
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 665 of file ContainerDefinition.h.

◆ SetModelPackageName() [2/3]

void Aws::SageMaker::Model::ContainerDefinition::SetModelPackageName ( Aws::String &&  value)
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 671 of file ContainerDefinition.h.

◆ SetModelPackageName() [3/3]

void Aws::SageMaker::Model::ContainerDefinition::SetModelPackageName ( const char *  value)
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 677 of file ContainerDefinition.h.

◆ WithContainerHostname() [1/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithContainerHostname ( const Aws::String value)
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 144 of file ContainerDefinition.h.

◆ WithContainerHostname() [2/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithContainerHostname ( Aws::String &&  value)
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 161 of file ContainerDefinition.h.

◆ WithContainerHostname() [3/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithContainerHostname ( const char *  value)
inline

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

Definition at line 178 of file ContainerDefinition.h.

◆ WithEnvironment() [1/2]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithEnvironment ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 590 of file ContainerDefinition.h.

◆ WithEnvironment() [2/2]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithEnvironment ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to

  1. We support up to 16 entries in the map.

Definition at line 597 of file ContainerDefinition.h.

◆ WithImage() [1/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithImage ( const Aws::String value)
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 263 of file ContainerDefinition.h.

◆ WithImage() [2/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithImage ( Aws::String &&  value)
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 277 of file ContainerDefinition.h.

◆ WithImage() [3/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithImage ( const char *  value)
inline

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

Definition at line 291 of file ContainerDefinition.h.

◆ WithImageConfig() [1/2]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithImageConfig ( const ImageConfig value)
inline

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

Definition at line 337 of file ContainerDefinition.h.

◆ WithImageConfig() [2/2]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithImageConfig ( ImageConfig &&  value)
inline

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

Definition at line 346 of file ContainerDefinition.h.

◆ WithMode() [1/2]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithMode ( const ContainerMode value)
inline

Whether the container hosts a single model or multiple models.

Definition at line 372 of file ContainerDefinition.h.

◆ WithMode() [2/2]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithMode ( ContainerMode &&  value)
inline

Whether the container hosts a single model or multiple models.

Definition at line 377 of file ContainerDefinition.h.

◆ WithModelDataUrl() [1/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithModelDataUrl ( const Aws::String value)
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 510 of file ContainerDefinition.h.

◆ WithModelDataUrl() [2/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithModelDataUrl ( Aws::String &&  value)
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 532 of file ContainerDefinition.h.

◆ WithModelDataUrl() [3/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithModelDataUrl ( const char *  value)
inline

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

Definition at line 554 of file ContainerDefinition.h.

◆ WithModelPackageName() [1/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithModelPackageName ( const Aws::String value)
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 683 of file ContainerDefinition.h.

◆ WithModelPackageName() [2/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithModelPackageName ( Aws::String &&  value)
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 689 of file ContainerDefinition.h.

◆ WithModelPackageName() [3/3]

ContainerDefinition& Aws::SageMaker::Model::ContainerDefinition::WithModelPackageName ( const char *  value)
inline

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Definition at line 695 of file ContainerDefinition.h.


The documentation for this class was generated from the following file: