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

Inherits:

Struct

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

Defined in:

gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

Describes the Docker container for the model package.

See Also:

• AWS API Documentation

Constant Summary

SENSITIVE =

[]

Instance Attribute Summary

• #additional_s3_data_source ⇒ Types::AdditionalS3DataSource

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

• #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.

• #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_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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  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 38130

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_s3_data_source,
  :model_data_etag)
  SENSITIVE = []
  include Aws::Structure
end