| Column Name | Column Data Type |
|---|
model_package_name Required Input Column
The name of the model package being described. | VARCHAR |
_aws_profile Input Column
The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role. | STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"account_id" VARCHAR,
"via_profile_name" VARCHAR,
"assumed_role_arn" VARCHAR,
"organization" STRUCT(
"account_name" VARCHAR,
"id" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[],
"master_account" STRUCT(
"id" VARCHAR,
"email" VARCHAR
),
"parents" STRUCT(
"type" VARCHAR,
"id" VARCHAR,
"name" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[]
)[]
)
) |
Show child fields- _aws_profile.account_id
The AWS account id - _aws_profile.assumed_role_arn
The ARN of the assumed role - _aws_profile.name
The unique name of the profile. - _aws_profile.organization
Information about this profile's membership in the AWS organization. Show child fields- _aws_profile.organization.account_name
The name of account speciifed by the organization - _aws_profile.organization.id
The organization id - _aws_profile.organization.master_account
Show child fields- _aws_profile.organization.master_account.email
The organization master account email address - _aws_profile.organization.master_account.id
The organization master account id
- _aws_profile.organization.parents[]
Show child fields- _aws_profile.organization.parents[].id
The id of the parent - _aws_profile.organization.parents[].name
The name of the parent - _aws_profile.organization.parents[].tags[]
Show child fields- _aws_profile.organization.parents[].tags[].key
- _aws_profile.organization.parents[].tags[].value
- _aws_profile.organization.parents[].type
The type of parent can be an organization unit or a root
- _aws_profile.organization.tags[]
Show child fields- _aws_profile.organization.tags[].key
- _aws_profile.organization.tags[].value
- _aws_profile.type
The type of profile, either 'credentials' or 'assumed_role' - _aws_profile.via_profile_name
This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.
|
additional_inference_specifications
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts. | STRUCT(
"name" VARCHAR,
"description" VARCHAR,
"containers" STRUCT(
"container_hostname" VARCHAR,
"image" VARCHAR,
"image_digest" VARCHAR,
"model_data_url" VARCHAR,
"model_data_source" STRUCT(
"s3_data_source" STRUCT(
"s3_uri" VARCHAR,
"s3_data_type" VARCHAR,
"compression_type" VARCHAR,
"model_access_config" STRUCT(
"accept_eula" BOOLEAN
),
"hub_access_config" STRUCT(
"hub_content_arn" VARCHAR
)
)
),
"product_id" VARCHAR,
"environment" MAP(VARCHAR, VARCHAR),
"model_input" STRUCT(
"data_input_config" VARCHAR
),
"framework" VARCHAR,
"framework_version" VARCHAR,
"nearest_model_name" VARCHAR,
"additional_s3_data_source" STRUCT(
"s3_data_type" VARCHAR,
"s3_uri" VARCHAR,
"compression_type" VARCHAR
)
)[],
"supported_transform_instance_types" VARCHAR[],
"supported_realtime_inference_instance_types" VARCHAR[],
"supported_content_types" VARCHAR[],
"supported_response_mime_types" VARCHAR[]
)[] |
Show child fields- additional_inference_specifications[]
Show child fields- additional_inference_specifications[].containers[]
Show child fields- additional_inference_specifications[].containers[].additional_s3_data_source
The additional data source that is used during inference in the Docker container for your model package. Show child fields- additional_inference_specifications[].containers[].additional_s3_data_source.compression_type
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed. - additional_inference_specifications[].containers[].additional_s3_data_source.s3_data_type
The data type of the additional data source that you specify for use in inference or training. - additional_inference_specifications[].containers[].additional_s3_data_source.s3_uri
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
- additional_inference_specifications[].containers[].container_hostname
The DNS host name for the Docker container. - additional_inference_specifications[].containers[].environment
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. - additional_inference_specifications[].containers[].framework
The machine learning framework of the model package container image. - additional_inference_specifications[].containers[].framework_version
The framework version of the Model Package Container Image. - additional_inference_specifications[].containers[].image
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. - additional_inference_specifications[].containers[].image_digest
An MD5 hash of the training algorithm that identifies the Docker image used for training. - additional_inference_specifications[].containers[].model_data_source
Specifies the location of ML model data to deploy during endpoint creation. Show child fields- additional_inference_specifications[].containers[].model_data_source.s3_data_source
Specifies the S3 location of ML model data to deploy. Show child fields- additional_inference_specifications[].containers[].model_data_source.s3_data_source.compression_type
Specifies how the ML model data is prepared. If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: -
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names: -
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. - additional_inference_specifications[].containers[].model_data_source.s3_data_source.hub_access_config
Configuration information for hub access. Show child fields- additional_inference_specifications[].containers[].model_data_source.s3_data_source.hub_access_config.hub_content_arn
The ARN of the hub content for which deployment access is allowed.
- additional_inference_specifications[].containers[].model_data_source.s3_data_source.model_access_config
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. Show child fields- additional_inference_specifications[].containers[].model_data_source.s3_data_source.model_access_config.accept_eula
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
- additional_inference_specifications[].containers[].model_data_source.s3_data_source.s3_data_type
Specifies the type of ML model data to deploy. If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/). If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy. - additional_inference_specifications[].containers[].model_data_source.s3_data_source.s3_uri
Specifies the S3 path of ML model data to deploy.
- additional_inference_specifications[].containers[].model_data_url
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. - additional_inference_specifications[].containers[].model_input
A structure with Model Input details. Show child fields- additional_inference_specifications[].containers[].model_input.data_input_config
The input configuration object for the model.
- additional_inference_specifications[].containers[].nearest_model_name
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. - additional_inference_specifications[].containers[].product_id
The Amazon Web Services Marketplace product ID of the model package.
- additional_inference_specifications[].description
A description of the additional Inference specification - additional_inference_specifications[].name
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. - additional_inference_specifications[].supported_content_types[]
- additional_inference_specifications[].supported_realtime_inference_instance_types[]
- additional_inference_specifications[].supported_response_mime_types[]
- additional_inference_specifications[].supported_transform_instance_types[]
|
approval_description
A description provided for the model approval. | VARCHAR |
certify_for_marketplace
Whether the model package is certified for listing on Amazon Web Services Marketplace. | BOOLEAN |
created_by
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card. | STRUCT(
"user_profile_arn" VARCHAR,
"user_profile_name" VARCHAR,
"domain_id" VARCHAR,
"iam_identity" STRUCT(
"arn" VARCHAR,
"principal_id" VARCHAR,
"source_identity" VARCHAR
)
) |
Show child fields- created_by.domain_id
The domain associated with the user. - created_by.iam_identity
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. Show child fields- created_by.iam_identity.arn
The Amazon Resource Name (ARN) of the IAM identity. - created_by.iam_identity.principal_id
The ID of the principal that assumes the IAM identity. - created_by.iam_identity.source_identity
The person or application which assumes the IAM identity.
- created_by.user_profile_arn
The Amazon Resource Name (ARN) of the user's profile. - created_by.user_profile_name
The name of the user's profile.
|
creation_time
A timestamp specifying when the model package was created. | TIMESTAMP_S |
customer_metadata_properties
The metadata properties associated with the model package versions. | MAP(VARCHAR, VARCHAR) |
domain
The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing. | VARCHAR |
drift_check_baselines
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide. | STRUCT(
"bias" STRUCT(
"config_file" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"pre_training_constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"post_training_constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
),
"explainability" STRUCT(
"constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"config_file" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
),
"model_quality" STRUCT(
"statistics" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
),
"model_data_quality" STRUCT(
"statistics" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
)
) |
Show child fields- drift_check_baselines.bias
Represents the drift check bias baselines that can be used when the model monitor is set using the model package. Show child fields- drift_check_baselines.bias.config_file
The bias config file for a model. Show child fields- drift_check_baselines.bias.config_file.content_digest
The digest of the file source. - drift_check_baselines.bias.config_file.content_type
The type of content stored in the file source. - drift_check_baselines.bias.config_file.s3_uri
The Amazon S3 URI for the file source.
- drift_check_baselines.bias.post_training_constraints
The post-training constraints. Show child fields- drift_check_baselines.bias.post_training_constraints.content_digest
The hash key used for the metrics source. - drift_check_baselines.bias.post_training_constraints.content_type
The metric source content type. - drift_check_baselines.bias.post_training_constraints.s3_uri
The S3 URI for the metrics source.
- drift_check_baselines.bias.pre_training_constraints
The pre-training constraints. Show child fields- drift_check_baselines.bias.pre_training_constraints.content_digest
The hash key used for the metrics source. - drift_check_baselines.bias.pre_training_constraints.content_type
The metric source content type. - drift_check_baselines.bias.pre_training_constraints.s3_uri
The S3 URI for the metrics source.
- drift_check_baselines.explainability
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package. Show child fields- drift_check_baselines.explainability.config_file
The explainability config file for the model. Show child fields- drift_check_baselines.explainability.config_file.content_digest
The digest of the file source. - drift_check_baselines.explainability.config_file.content_type
The type of content stored in the file source. - drift_check_baselines.explainability.config_file.s3_uri
The Amazon S3 URI for the file source.
- drift_check_baselines.explainability.constraints
The drift check explainability constraints. Show child fields- drift_check_baselines.explainability.constraints.content_digest
The hash key used for the metrics source. - drift_check_baselines.explainability.constraints.content_type
The metric source content type. - drift_check_baselines.explainability.constraints.s3_uri
The S3 URI for the metrics source.
- drift_check_baselines.model_data_quality
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package. Show child fields- drift_check_baselines.model_data_quality.constraints
The drift check model data quality constraints. Show child fields- drift_check_baselines.model_data_quality.constraints.content_digest
The hash key used for the metrics source. - drift_check_baselines.model_data_quality.constraints.content_type
The metric source content type. - drift_check_baselines.model_data_quality.constraints.s3_uri
The S3 URI for the metrics source.
- drift_check_baselines.model_data_quality.statistics
The drift check model data quality statistics. Show child fields- drift_check_baselines.model_data_quality.statistics.content_digest
The hash key used for the metrics source. - drift_check_baselines.model_data_quality.statistics.content_type
The metric source content type. - drift_check_baselines.model_data_quality.statistics.s3_uri
The S3 URI for the metrics source.
- drift_check_baselines.model_quality
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package. Show child fields- drift_check_baselines.model_quality.constraints
The drift check model quality constraints. Show child fields- drift_check_baselines.model_quality.constraints.content_digest
The hash key used for the metrics source. - drift_check_baselines.model_quality.constraints.content_type
The metric source content type. - drift_check_baselines.model_quality.constraints.s3_uri
The S3 URI for the metrics source.
- drift_check_baselines.model_quality.statistics
The drift check model quality statistics. Show child fields- drift_check_baselines.model_quality.statistics.content_digest
The hash key used for the metrics source. - drift_check_baselines.model_quality.statistics.content_type
The metric source content type. - drift_check_baselines.model_quality.statistics.s3_uri
The S3 URI for the metrics source.
|
inference_specification
Details about inference jobs that you can run with models based on this model package. | STRUCT(
"containers" STRUCT(
"container_hostname" VARCHAR,
"image" VARCHAR,
"image_digest" VARCHAR,
"model_data_url" VARCHAR,
"model_data_source" STRUCT(
"s3_data_source" STRUCT(
"s3_uri" VARCHAR,
"s3_data_type" VARCHAR,
"compression_type" VARCHAR,
"model_access_config" STRUCT(
"accept_eula" BOOLEAN
),
"hub_access_config" STRUCT(
"hub_content_arn" VARCHAR
)
)
),
"product_id" VARCHAR,
"environment" MAP(VARCHAR, VARCHAR),
"model_input" STRUCT(
"data_input_config" VARCHAR
),
"framework" VARCHAR,
"framework_version" VARCHAR,
"nearest_model_name" VARCHAR,
"additional_s3_data_source" STRUCT(
"s3_data_type" VARCHAR,
"s3_uri" VARCHAR,
"compression_type" VARCHAR
)
)[],
"supported_transform_instance_types" VARCHAR[],
"supported_realtime_inference_instance_types" VARCHAR[],
"supported_content_types" VARCHAR[],
"supported_response_mime_types" VARCHAR[]
) |
Show child fields- inference_specification.containers[]
Show child fields- inference_specification.containers[].additional_s3_data_source
The additional data source that is used during inference in the Docker container for your model package. Show child fields- inference_specification.containers[].additional_s3_data_source.compression_type
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed. - inference_specification.containers[].additional_s3_data_source.s3_data_type
The data type of the additional data source that you specify for use in inference or training. - inference_specification.containers[].additional_s3_data_source.s3_uri
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
- inference_specification.containers[].container_hostname
The DNS host name for the Docker container. - inference_specification.containers[].environment
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. - inference_specification.containers[].framework
The machine learning framework of the model package container image. - inference_specification.containers[].framework_version
The framework version of the Model Package Container Image. - inference_specification.containers[].image
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. - inference_specification.containers[].image_digest
An MD5 hash of the training algorithm that identifies the Docker image used for training. - inference_specification.containers[].model_data_source
Specifies the location of ML model data to deploy during endpoint creation. Show child fields- inference_specification.containers[].model_data_source.s3_data_source
Specifies the S3 location of ML model data to deploy. Show child fields- inference_specification.containers[].model_data_source.s3_data_source.compression_type
Specifies how the ML model data is prepared. If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: -
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names: -
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. - inference_specification.containers[].model_data_source.s3_data_source.hub_access_config
Configuration information for hub access. Show child fields- inference_specification.containers[].model_data_source.s3_data_source.hub_access_config.hub_content_arn
The ARN of the hub content for which deployment access is allowed.
- inference_specification.containers[].model_data_source.s3_data_source.model_access_config
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. Show child fields- inference_specification.containers[].model_data_source.s3_data_source.model_access_config.accept_eula
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
- inference_specification.containers[].model_data_source.s3_data_source.s3_data_type
Specifies the type of ML model data to deploy. If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/). If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy. - inference_specification.containers[].model_data_source.s3_data_source.s3_uri
Specifies the S3 path of ML model data to deploy.
- inference_specification.containers[].model_data_url
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. - inference_specification.containers[].model_input
A structure with Model Input details. Show child fields- inference_specification.containers[].model_input.data_input_config
The input configuration object for the model.
- inference_specification.containers[].nearest_model_name
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. - inference_specification.containers[].product_id
The Amazon Web Services Marketplace product ID of the model package.
- inference_specification.supported_content_types[]
- inference_specification.supported_realtime_inference_instance_types[]
- inference_specification.supported_response_mime_types[]
- inference_specification.supported_transform_instance_types[]
|
last_modified_by
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card. | STRUCT(
"user_profile_arn" VARCHAR,
"user_profile_name" VARCHAR,
"domain_id" VARCHAR,
"iam_identity" STRUCT(
"arn" VARCHAR,
"principal_id" VARCHAR,
"source_identity" VARCHAR
)
) |
Show child fields- last_modified_by.domain_id
The domain associated with the user. - last_modified_by.iam_identity
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. Show child fields- last_modified_by.iam_identity.arn
The Amazon Resource Name (ARN) of the IAM identity. - last_modified_by.iam_identity.principal_id
The ID of the principal that assumes the IAM identity. - last_modified_by.iam_identity.source_identity
The person or application which assumes the IAM identity.
- last_modified_by.user_profile_arn
The Amazon Resource Name (ARN) of the user's profile. - last_modified_by.user_profile_name
The name of the user's profile.
|
last_modified_time
The last time that the model package was modified. | TIMESTAMP_S |
metadata_properties
Metadata properties of the tracking entity, trial, or trial component. | STRUCT(
"commit_id" VARCHAR,
"repository" VARCHAR,
"generated_by" VARCHAR,
"project_id" VARCHAR
) |
Show child fields- metadata_properties.commit_id
The commit ID. - metadata_properties.generated_by
The entity this entity was generated by. - metadata_properties.project_id
The project ID. - metadata_properties.repository
The repository.
|
model_approval_status
The approval status of the model package. | VARCHAR |
model_card
The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version. | STRUCT(
"model_card_content" VARCHAR,
"model_card_status" VARCHAR
) |
Show child fields- model_card.model_card_content
The content of the model card. The content must follow the schema described in Model Package Model Card Schema. - model_card.model_card_status
The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. -
Draft: The model card is a work in progress. -
PendingReview: The model card is pending review. -
Approved: The model card is approved. -
Archived: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the message Model Card is in Archived state.
|
model_metrics
Metrics for the model. | STRUCT(
"model_quality" STRUCT(
"statistics" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
),
"model_data_quality" STRUCT(
"statistics" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"constraints" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
),
"bias" STRUCT(
"report" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"pre_training_report" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
),
"post_training_report" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
),
"explainability" STRUCT(
"report" STRUCT(
"content_type" VARCHAR,
"content_digest" VARCHAR,
"s3_uri" VARCHAR
)
)
) |
Show child fields- model_metrics.bias
Metrics that measure bias in a model. Show child fields- model_metrics.bias.post_training_report
The post-training bias report for a model. Show child fields- model_metrics.bias.post_training_report.content_digest
The hash key used for the metrics source. - model_metrics.bias.post_training_report.content_type
The metric source content type. - model_metrics.bias.post_training_report.s3_uri
The S3 URI for the metrics source.
- model_metrics.bias.pre_training_report
The pre-training bias report for a model. Show child fields- model_metrics.bias.pre_training_report.content_digest
The hash key used for the metrics source. - model_metrics.bias.pre_training_report.content_type
The metric source content type. - model_metrics.bias.pre_training_report.s3_uri
The S3 URI for the metrics source.
- model_metrics.bias.report
The bias report for a model Show child fields- model_metrics.bias.report.content_digest
The hash key used for the metrics source. - model_metrics.bias.report.content_type
The metric source content type. - model_metrics.bias.report.s3_uri
The S3 URI for the metrics source.
- model_metrics.explainability
Metrics that help explain a model. Show child fields- model_metrics.explainability.report
The explainability report for a model. Show child fields- model_metrics.explainability.report.content_digest
The hash key used for the metrics source. - model_metrics.explainability.report.content_type
The metric source content type. - model_metrics.explainability.report.s3_uri
The S3 URI for the metrics source.
- model_metrics.model_data_quality
Metrics that measure the quality of the input data for a model. Show child fields- model_metrics.model_data_quality.constraints
Data quality constraints for a model. Show child fields- model_metrics.model_data_quality.constraints.content_digest
The hash key used for the metrics source. - model_metrics.model_data_quality.constraints.content_type
The metric source content type. - model_metrics.model_data_quality.constraints.s3_uri
The S3 URI for the metrics source.
- model_metrics.model_data_quality.statistics
Data quality statistics for a model. Show child fields- model_metrics.model_data_quality.statistics.content_digest
The hash key used for the metrics source. - model_metrics.model_data_quality.statistics.content_type
The metric source content type. - model_metrics.model_data_quality.statistics.s3_uri
The S3 URI for the metrics source.
- model_metrics.model_quality
Metrics that measure the quality of a model. Show child fields- model_metrics.model_quality.constraints
Model quality constraints. Show child fields- model_metrics.model_quality.constraints.content_digest
The hash key used for the metrics source. - model_metrics.model_quality.constraints.content_type
The metric source content type. - model_metrics.model_quality.constraints.s3_uri
The S3 URI for the metrics source.
- model_metrics.model_quality.statistics
Model quality statistics. Show child fields- model_metrics.model_quality.statistics.content_digest
The hash key used for the metrics source. - model_metrics.model_quality.statistics.content_type
The metric source content type. - model_metrics.model_quality.statistics.s3_uri
The S3 URI for the metrics source.
|
model_package_arn
The Amazon Resource Name (ARN) of the model package. | VARCHAR |
model_package_description
A brief summary of the model package. | VARCHAR |
model_package_group_name
If the model is a versioned model, the name of the model group that the versioned model belongs to. | VARCHAR |
model_package_status
The current status of the model package. | VARCHAR |
model_package_status_details
Details about the current status of the model package. | STRUCT(
"validation_statuses" STRUCT(
"name" VARCHAR,
"status" VARCHAR,
"failure_reason" VARCHAR
)[],
"image_scan_statuses" STRUCT(
"name" VARCHAR,
"status" VARCHAR,
"failure_reason" VARCHAR
)[]
) |
Show child fields- model_package_status_details.image_scan_statuses[]
Show child fields- model_package_status_details.image_scan_statuses[].failure_reason
if the overall status is Failed, the reason for the failure. - model_package_status_details.image_scan_statuses[].name
The name of the model package for which the overall status is being reported. - model_package_status_details.image_scan_statuses[].status
The current status.
- model_package_status_details.validation_statuses[]
Show child fields- model_package_status_details.validation_statuses[].failure_reason
if the overall status is Failed, the reason for the failure. - model_package_status_details.validation_statuses[].name
The name of the model package for which the overall status is being reported. - model_package_status_details.validation_statuses[].status
The current status.
|
model_package_version
The version of the model package. | BIGINT |
sample_payload_url
The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix). | VARCHAR |
security_config
The KMS Key ID (KMSKeyId) used for encryption of model package information. | STRUCT(
"kms_key_id" VARCHAR
) |
Show child fields- security_config.kms_key_id
The KMS Key ID (KMSKeyId) used for encryption of model package information.
|
skip_model_validation
Indicates if you want to skip model validation. | VARCHAR |
source_algorithm_specification
Details about the algorithm that was used to create the model package. | STRUCT(
"source_algorithms" STRUCT(
"model_data_url" VARCHAR,
"model_data_source" STRUCT(
"s3_data_source" STRUCT(
"s3_uri" VARCHAR,
"s3_data_type" VARCHAR,
"compression_type" VARCHAR,
"model_access_config" STRUCT(
"accept_eula" BOOLEAN
),
"hub_access_config" STRUCT(
"hub_content_arn" VARCHAR
)
)
),
"algorithm_name" VARCHAR
)[]
) |
Show child fields- source_algorithm_specification.source_algorithms[]
Show child fields- source_algorithm_specification.source_algorithms[].algorithm_name
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to. - source_algorithm_specification.source_algorithms[].model_data_source
Specifies the location of ML model data to deploy during endpoint creation. Show child fields- source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source
Specifies the S3 location of ML model data to deploy. Show child fields- source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.compression_type
Specifies how the ML model data is prepared. If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: -
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names: -
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. - source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.hub_access_config
Configuration information for hub access. Show child fields- source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.hub_access_config.hub_content_arn
The ARN of the hub content for which deployment access is allowed.
- source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.model_access_config
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. Show child fields- source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.model_access_config.accept_eula
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
- source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.s3_data_type
Specifies the type of ML model data to deploy. If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/). If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy. - source_algorithm_specification.source_algorithms[].model_data_source.s3_data_source.s3_uri
Specifies the S3 path of ML model data to deploy.
- source_algorithm_specification.source_algorithms[].model_data_url
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 Amazon Web Services region as the algorithm.
|
source_uri
The URI of the source for the model package. | VARCHAR |
task
The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification. | VARCHAR |
validation_specification
Configurations for one or more transform jobs that SageMaker runs to test the model package. | STRUCT(
"validation_role" VARCHAR,
"validation_profiles" STRUCT(
"profile_name" VARCHAR,
"transform_job_definition" STRUCT(
"max_concurrent_transforms" BIGINT,
"max_payload_in_mb" BIGINT,
"batch_strategy" VARCHAR,
"environment" MAP(VARCHAR, VARCHAR),
"transform_input" STRUCT(
"data_source" STRUCT(
"s3_data_source" STRUCT(
"s3_data_type" VARCHAR,
"s3_uri" VARCHAR
)
),
"content_type" VARCHAR,
"compression_type" VARCHAR,
"split_type" VARCHAR
),
"transform_output" STRUCT(
"s3_output_path" VARCHAR,
"accept" VARCHAR,
"assemble_with" VARCHAR,
"kms_key_id" VARCHAR
),
"transform_resources" STRUCT(
"instance_type" VARCHAR,
"instance_count" BIGINT,
"volume_kms_key_id" VARCHAR
)
)
)[]
) |
Show child fields- validation_specification.validation_profiles[]
Show child fields- validation_specification.validation_profiles[].profile_name
The name of the profile for the model package. - validation_specification.validation_profiles[].transform_job_definition
The TransformJobDefinition object that describes the transform job used for the validation of the model package. Show child fields- validation_specification.validation_profiles[].transform_job_definition.batch_strategy
A string that determines the number of records included in a single mini-batch. SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit. - validation_specification.validation_profiles[].transform_job_definition.environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map. - validation_specification.validation_profiles[].transform_job_definition.max_concurrent_transforms
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1. - validation_specification.validation_profiles[].transform_job_definition.max_payload_in_mb
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
A description of the input source and the way the transform job consumes it. Show child fields- validation_specification.validation_profiles[].transform_job_definition.transform_input.compression_type
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. - validation_specification.validation_profiles[].transform_job_definition.transform_input.content_type
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. - validation_specification.validation_profiles[].transform_job_definition.transform_input.data_source
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume. Show child fields- validation_specification.validation_profiles[].transform_job_definition.transform_input.data_source.s3_data_source
The S3 location of the data source that is associated with a channel. Show child fields- validation_specification.validation_profiles[].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform. The following values are compatible: ManifestFile, S3Prefix The following value is not compatible: AugmentedManifestFile - validation_specification.validation_profiles[].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri
Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example: -
A key name prefix might look like this: s3://bucketname/exampleprefix/. -
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
- validation_specification.validation_profiles[].transform_job_definition.transform_input.split_type
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are: When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request. Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord. For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
- validation_specification.validation_profiles[].transform_job_definition.transform_output
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. Show child fields- validation_specification.validation_profiles[].transform_job_definition.transform_output.accept
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. - validation_specification.validation_profiles[].transform_job_definition.transform_output.assemble_with
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line. - validation_specification.validation_profiles[].transform_job_definition.transform_output.kms_key_id
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats: -
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab -
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab -
Alias name: alias/ExampleAlias -
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide. - validation_specification.validation_profiles[].transform_job_definition.transform_output.s3_output_path
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix. For every S3 object used as input for the transform job, batch transform stores the transformed data with an .out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
- validation_specification.validation_profiles[].transform_job_definition.transform_resources
Identifies the ML compute instances for the transform job. Show child fields- validation_specification.validation_profiles[].transform_job_definition.transform_resources.instance_count
The number of ML compute instances to use in the transform job. The default value is 1, and the maximum is 100. For distributed transform jobs, specify a value greater than 1. - validation_specification.validation_profiles[].transform_job_definition.transform_resources.instance_type
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.largeinstance types. - validation_specification.validation_profiles[].transform_job_definition.transform_resources.volume_kms_key_id
The Amazon Web Services Key Management Service (Amazon Web Services 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. Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes. The VolumeKmsKeyId can be any of the following formats: -
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab -
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab -
Alias name: alias/ExampleAlias -
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
- validation_specification.validation_role
The IAM roles to be used for the validation of the model package.
|