Class: Aws::SageMaker::Types::ModelPackageContainerDefinition

Inherits:

Struct

Object
Struct
Aws::SageMaker::Types::ModelPackageContainerDefinition

show all

Defined in:: gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

Describes the Docker container for the model package.

Constant Summary collapse

SENSITIVE =

[]

Instance Attribute Summary collapse

#additional_model_data_sources ⇒ Array<Types::AdditionalModelDataSource>
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.
#additional_s3_data_source ⇒ Types::AdditionalS3DataSource
The additional data source that is used during inference in the Docker container for your model package.
#base_model ⇒ Types::BaseModel
Identifies the foundation model that was used as the starting point for model customization.
#container_hostname ⇒ String
The DNS host name for the Docker container.
#environment ⇒ Hash<String,String>
The environment variables to set in the Docker container.
#framework ⇒ String
The machine learning framework of the model package container image.
#framework_version ⇒ String
The framework version of the Model Package Container Image.
#image ⇒ String
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
#image_digest ⇒ String
An MD5 hash of the training algorithm that identifies the Docker image used for training.
#is_checkpoint ⇒ Boolean
Specifies whether the model data is a training checkpoint.
#model_data_etag ⇒ String
The ETag associated with Model Data URL.
#model_data_source ⇒ Types::ModelDataSource
Specifies the location of ML model data to deploy during endpoint creation.
#model_data_url ⇒ String
The Amazon S3 path where the model artifacts, which result from model training, are stored.
#model_input ⇒ Types::ModelInput
A structure with Model Input details.
#nearest_model_name ⇒ String
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.
#product_id ⇒ String
The Amazon Web Services Marketplace product ID of the model package.

Instance Attribute Details

#additional_model_data_sources ⇒ `Array<Types::AdditionalModelDataSource>`

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.

Returns:

(Array<Types::AdditionalModelDataSource>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#additional_s3_data_source ⇒ `Types::AdditionalS3DataSource`

The additional data source that is used during inference in the Docker container for your model package.

Returns:

(Types::AdditionalS3DataSource)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#base_model ⇒ `Types::BaseModel`

Identifies the foundation model that was used as the starting point for model customization.

Returns:

(Types::BaseModel)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#container_hostname ⇒ `String`

The DNS host name for the Docker container.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#environment ⇒ `Hash<String,String>`

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 1024. We support up to 16 entries in the map.

Returns:

(Hash<String,String>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#framework ⇒ `String`

The machine learning framework of the model package container image.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#framework_version ⇒ `String`

The framework version of the Model Package Container Image.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#image ⇒ `String`

The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#image_digest ⇒ `String`

An MD5 hash of the training algorithm that identifies the Docker image used for training.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#is_checkpoint ⇒ `Boolean`

Specifies whether the model data is a training checkpoint.

Returns:

(Boolean)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#model_data_etag ⇒ `String`

The ETag associated with Model Data URL.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#model_data_source ⇒ `Types::ModelDataSource`

Specifies the location of ML model data to deploy during endpoint creation.

Returns:

(Types::ModelDataSource)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#model_data_url ⇒ `String`

The Amazon 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 model artifacts must be in an S3 bucket that is in the same region as the model package.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#model_input ⇒ `Types::ModelInput`

A structure with Model Input details.

Returns:

(Types::ModelInput)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#nearest_model_name ⇒ `String`

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

#product_id ⇒ `String`

The Amazon Web Services Marketplace product ID of the model package.

Returns:

(String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42701

class ModelPackageContainerDefinition < Struct.new(
  :container_hostname,
  :image,
  :image_digest,
  :model_data_url,
  :model_data_source,
  :product_id,
  :environment,
  :model_input,
  :framework,
  :framework_version,
  :nearest_model_name,
  :additional_model_data_sources,
  :additional_s3_data_source,
  :model_data_etag,
  :is_checkpoint,
  :base_model)
  SENSITIVE = []
  include Aws::Structure
end

Class: Aws::SageMaker::Types::ModelPackageContainerDefinition

Overview

Constant Summary collapse

Instance Attribute Summary collapse

Instance Attribute Details

#additional_model_data_sources ⇒ Array<Types::AdditionalModelDataSource>

#additional_s3_data_source ⇒ Types::AdditionalS3DataSource

#base_model ⇒ Types::BaseModel

#container_hostname ⇒ String

#environment ⇒ Hash<String,String>

#framework ⇒ String

#framework_version ⇒ String

#image ⇒ String

#image_digest ⇒ String

#is_checkpoint ⇒ Boolean

#model_data_etag ⇒ String

#model_data_source ⇒ Types::ModelDataSource

#model_data_url ⇒ String

#model_input ⇒ Types::ModelInput

#nearest_model_name ⇒ String

#product_id ⇒ String

#additional_model_data_sources ⇒ `Array<Types::AdditionalModelDataSource>`

#additional_s3_data_source ⇒ `Types::AdditionalS3DataSource`

#base_model ⇒ `Types::BaseModel`

#container_hostname ⇒ `String`

#environment ⇒ `Hash<String,String>`

#framework ⇒ `String`

#framework_version ⇒ `String`

#image ⇒ `String`

#image_digest ⇒ `String`

#is_checkpoint ⇒ `Boolean`

#model_data_etag ⇒ `String`

#model_data_source ⇒ `Types::ModelDataSource`

#model_data_url ⇒ `String`

#model_input ⇒ `Types::ModelInput`

#nearest_model_name ⇒ `String`

#product_id ⇒ `String`