Resource Type definition for AWS::SageMaker::ModelPackage
additional_inference_specifications
(Attributes List) An array of additional Inference Specification objects. (see below for nested schema)additional_inference_specifications_to_add
(Attributes List) An array of additional Inference Specification objects. (see below for nested schema)approval_description
(String) A description provided for the model approval.certify_for_marketplace
(Boolean) Whether to certify the model package for listing on AWS Marketplace.client_token
(String) A unique token that guarantees that the call to this API is idempotent.customer_metadata_properties
(Map of String) The metadata properties associated with the model package versions.domain
(String) The machine learning domain of the model package you specified.drift_check_baselines
(Attributes) Represents the drift check baselines that can be used when the model monitor is set using the model package. (see below for nested schema)inference_specification
(Attributes) Details about inference jobs that can be run with models based on this model package. (see below for nested schema)last_modified_time
(String) The time at which the model package was last modified.metadata_properties
(Attributes) Metadata properties of the tracking entity, trial, or trial component. (see below for nested schema)model_approval_status
(String) The approval status of the model package.model_metrics
(Attributes) A structure that contains model metrics reports. (see below for nested schema)model_package_description
(String) The description of the model package.model_package_group_name
(String) The name of the model package group.model_package_name
(String) The name or arn of the model package.model_package_status_details
(Attributes) Details about the current status of the model package. (see below for nested schema)model_package_version
(Number) The version of the model package.sample_payload_url
(String) The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored pointing to single gzip compressed tar archive.skip_model_validation
(String) Indicates if you want to skip model validation.source_algorithm_specification
(Attributes) Details about the algorithm that was used to create the model package. (see below for nested schema)tags
(Attributes List) An array of key-value pairs to apply to this resource. (see below for nested schema)task
(String) The machine learning task your model package accomplishes.validation_specification
(Attributes) Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package. (see below for nested schema)creation_time
(String) The time at which the model package was created.id
(String) Uniquely identifies the resource.model_package_arn
(String) The Amazon Resource Name (ARN) of the model package group.model_package_status
(String) The current status of the model package.additional_inference_specifications
Required:
containers
(Attributes List) The Amazon ECR registry path of the Docker image that contains the inference code. (see below for nested schema)name
(String) A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.Optional:
description
(String) A description of the additional Inference specification.supported_content_types
(List of String) The supported MIME types for the input data.supported_realtime_inference_instance_types
(List of String) A list of the instance types that are used to generate inferences in real-timesupported_response_mime_types
(List of String) The supported MIME types for the output data.supported_transform_instance_types
(List of String) A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.additional_inference_specifications.containers
Required:
image
(String) The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.Optional:
container_hostname
(String) The DNS host name for the Docker container.environment
(Map of String) Sets the environment variables in the Docker containerframework
(String) The machine learning framework of the model package container image.framework_version
(String) The framework version of the Model Package Container Image.image_digest
(String) An MD5 hash of the training algorithm that identifies the Docker image used for training.model_data_url
(String) A structure with Model Input details.model_input
(Attributes) (see below for nested schema)nearest_model_name
(String) The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.additional_inference_specifications.containers.model_input
Required:
data_input_config
(String) The input configuration object for the model.additional_inference_specifications_to_add
Required:
containers
(Attributes List) The Amazon ECR registry path of the Docker image that contains the inference code. (see below for nested schema)name
(String) A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.Optional:
description
(String) A description of the additional Inference specification.supported_content_types
(List of String) The supported MIME types for the input data.supported_realtime_inference_instance_types
(List of String) A list of the instance types that are used to generate inferences in real-timesupported_response_mime_types
(List of String) The supported MIME types for the output data.supported_transform_instance_types
(List of String) A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.additional_inference_specifications_to_add.containers
Required:
image
(String) The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.Optional:
container_hostname
(String) The DNS host name for the Docker container.environment
(Map of String) Sets the environment variables in the Docker containerframework
(String) The machine learning framework of the model package container image.framework_version
(String) The framework version of the Model Package Container Image.image_digest
(String) An MD5 hash of the training algorithm that identifies the Docker image used for training.model_data_url
(String) A structure with Model Input details.model_input
(Attributes) (see below for nested schema)nearest_model_name
(String) The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.additional_inference_specifications_to_add.containers.model_input
Required:
data_input_config
(String) The input configuration object for the model.drift_check_baselines
Optional:
bias
(Attributes) Represents the drift check bias baselines that can be used when the model monitor is set using the model package. (see below for nested schema)explainability
(Attributes) Contains explainability metrics for a model. (see below for nested schema)model_data_quality
(Attributes) Represents the drift check data quality baselines that can be used when the model monitor is set using the model package. (see below for nested schema)model_quality
(Attributes) Represents the drift check model quality baselines that can be used when the model monitor is set using the model package. (see below for nested schema)drift_check_baselines.bias
Optional:
config_file
(Attributes) Represents a File Source Object. (see below for nested schema)post_training_constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)pre_training_constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)drift_check_baselines.bias.config_file
Required:
s3_uri
(String) The Amazon S3 URI for the file source.Optional:
content_digest
(String) The digest of the file source.content_type
(String) The type of content stored in the file source.drift_check_baselines.bias.post_training_constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.drift_check_baselines.bias.pre_training_constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.drift_check_baselines.explainability
Optional:
config_file
(Attributes) Represents a File Source Object. (see below for nested schema)constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)drift_check_baselines.explainability.config_file
Required:
s3_uri
(String) The Amazon S3 URI for the file source.Optional:
content_digest
(String) The digest of the file source.content_type
(String) The type of content stored in the file source.drift_check_baselines.explainability.constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.drift_check_baselines.model_data_quality
Optional:
constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)statistics
(Attributes) Represents a Metric Source Object. (see below for nested schema)drift_check_baselines.model_data_quality.constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.drift_check_baselines.model_data_quality.statistics
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.drift_check_baselines.model_quality
Optional:
constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)statistics
(Attributes) Represents a Metric Source Object. (see below for nested schema)drift_check_baselines.model_quality.constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.drift_check_baselines.model_quality.statistics
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.inference_specification
Required:
containers
(Attributes List) The Amazon ECR registry path of the Docker image that contains the inference code. (see below for nested schema)supported_content_types
(List of String) The supported MIME types for the input data.supported_response_mime_types
(List of String) The supported MIME types for the output data.Optional:
supported_realtime_inference_instance_types
(List of String) A list of the instance types that are used to generate inferences in real-timesupported_transform_instance_types
(List of String) A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.inference_specification.containers
Required:
image
(String) The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.Optional:
container_hostname
(String) The DNS host name for the Docker container.environment
(Map of String) Sets the environment variables in the Docker containerframework
(String) The machine learning framework of the model package container image.framework_version
(String) The framework version of the Model Package Container Image.image_digest
(String) An MD5 hash of the training algorithm that identifies the Docker image used for training.model_data_url
(String) A structure with Model Input details.model_input
(Attributes) (see below for nested schema)nearest_model_name
(String) The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.inference_specification.containers.model_input
Required:
data_input_config
(String) The input configuration object for the model.metadata_properties
Optional:
commit_id
(String) The commit ID.generated_by
(String) The entity this entity was generated by.project_id
(String) The project ID metadata.repository
(String) The repository metadata.model_metrics
Optional:
bias
(Attributes) Contains bias metrics for a model. (see below for nested schema)explainability
(Attributes) Contains explainability metrics for a model. (see below for nested schema)model_data_quality
(Attributes) Metrics that measure the quality of the input data for a model. (see below for nested schema)model_quality
(Attributes) Metrics that measure the quality of a model. (see below for nested schema)model_metrics.bias
Optional:
post_training_report
(Attributes) Represents a Metric Source Object. (see below for nested schema)pre_training_report
(Attributes) Represents a Metric Source Object. (see below for nested schema)report
(Attributes) Represents a Metric Source Object. (see below for nested schema)model_metrics.bias.post_training_report
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.bias.pre_training_report
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.bias.report
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.explainability
Optional:
report
(Attributes) Represents a Metric Source Object. (see below for nested schema)model_metrics.explainability.report
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.model_data_quality
Optional:
constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)statistics
(Attributes) Represents a Metric Source Object. (see below for nested schema)model_metrics.model_data_quality.constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.model_data_quality.statistics
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.model_quality
Optional:
constraints
(Attributes) Represents a Metric Source Object. (see below for nested schema)statistics
(Attributes) Represents a Metric Source Object. (see below for nested schema)model_metrics.model_quality.constraints
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_metrics.model_quality.statistics
Required:
content_type
(String) The type of content stored in the metric source.s3_uri
(String) The Amazon S3 URI for the metric source.Optional:
content_digest
(String) The digest of the metric source.model_package_status_details
Optional:
validation_statuses
(Attributes List) (see below for nested schema)model_package_status_details.validation_statuses
Required:
name
(String) The name of the model package for which the overall status is being reported.status
(String) The current status.Optional:
failure_reason
(String) If the overall status is Failed, the reason for the failure.source_algorithm_specification
Required:
source_algorithms
(Attributes List) A list of algorithms that were used to create a model package. (see below for nested schema)source_algorithm_specification.source_algorithms
Required:
algorithm_name
(String) The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.Optional:
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).tags
Required:
key
(String) The key name of the tag. You can specify a value that is 1 to 127 Unicode characters in length and cannot be prefixed with aws:. You can use any of the following characters: the set of Unicode letters, digits, whitespace, _, ., /, =, +, and -.value
(String) The value for the tag. You can specify a value that is 1 to 255 Unicode characters in length and cannot be prefixed with aws:. You can use any of the following characters: the set of Unicode letters, digits, whitespace, _, ., /, =, +, and -.validation_specification
Required:
validation_profiles
(Attributes List) (see below for nested schema)validation_role
(String) The IAM roles to be used for the validation of the model package.validation_specification.validation_profiles
Required:
profile_name
(String) The name of the profile for the model package.transform_job_definition
(Attributes) Defines the input needed to run a transform job using the inference specification specified in the algorithm. (see below for nested schema)validation_specification.validation_profiles.transform_job_definition
Required:
transform_input
(Attributes) Describes the input source of a transform job and the way the transform job consumes it. (see below for nested schema)transform_output
(Attributes) Describes the results of a transform job. (see below for nested schema)transform_resources
(Attributes) Describes the resources, including ML instance types and ML instance count, to use for transform job. (see below for nested schema)Optional:
batch_strategy
(String) A string that determines the number of records included in a single mini-batch.environment
(Map of String) Sets the environment variables in the Docker containermax_concurrent_transforms
(Number) The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.max_payload_in_mb
(Number) The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).validation_specification.validation_profiles.transform_job_definition.transform_input
Required:
data_source
(Attributes) Describes the input source of a transform job and the way the transform job consumes it. (see below for nested schema)Optional:
compression_type
(String) If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.content_type
(String) The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.split_type
(String) The method to use to split the transform job's data files into smaller batches.validation_specification.validation_profiles.transform_job_definition.max_payload_in_mb.data_source
Required:
s3_data_source
(Attributes) Describes the S3 data source. (see below for nested schema)validation_specification.validation_profiles.transform_job_definition.max_payload_in_mb.data_source.s3_data_source
Required:
s3_data_type
(String) The S3 Data Source Types3_uri
(String) Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest.validation_specification.validation_profiles.transform_job_definition.transform_output
Required:
s3_output_path
(String) The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.Optional:
accept
(String) The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.assemble_with
(String) Defines how to assemble the results of the transform job as a single S3 object.kms_key_id
(String) The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.validation_specification.validation_profiles.transform_job_definition.transform_resources
Required:
instance_count
(Number) The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1.instance_type
(String) The ML compute instance type for the transform job.Optional:
volume_kms_key_id
(String) The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.Import is supported using the following syntax:
$ terraform import awscc_sagemaker_model_package.example <resource ID>