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

Inherits:
Struct
  • Object
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

Instance Attribute Details

#additional_s3_data_sourceTypes::AdditionalS3DataSource

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#container_hostnameString

The DNS host name for the Docker container.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#environmentHash<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>)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#frameworkString

The machine learning framework of the model package container image.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#framework_versionString

The framework version of the Model Package Container Image.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#imageString

The Amazon EC2 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)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#image_digestString

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

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#model_data_sourceTypes::ModelDataSource

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#model_data_urlString

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)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#model_inputTypes::ModelInput

A structure with Model Input details.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#nearest_model_nameString

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)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end

#product_idString

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

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 31584

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)
  SENSITIVE = []
  include Aws::Structure
end