Resource Type definition for AWS::SageMaker::ModelExplainabilityJobDefinition
job_resources
(Attributes) Identifies the resources to deploy for a monitoring job. (see below for nested schema)model_explainability_app_specification
(Attributes) Container image configuration object for the monitoring job. (see below for nested schema)model_explainability_job_input
(Attributes) The inputs for a monitoring job. (see below for nested schema)model_explainability_job_output_config
(Attributes) The output configuration for monitoring jobs. (see below for nested schema)role_arn
(String) The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.endpoint_name
(String) The name of the endpoint used to run the monitoring job.job_definition_name
(String) The name of the job definition.model_explainability_baseline_config
(Attributes) Baseline configuration used to validate that the data conforms to the specified constraints and statistics. (see below for nested schema)network_config
(Attributes) Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs. (see below for nested schema)stopping_condition
(Attributes) Specifies a time limit for how long the monitoring job is allowed to run. (see below for nested schema)tags
(Attributes List) An array of key-value pairs to apply to this resource. (see below for nested schema)creation_time
(String) The time at which the job definition was created.id
(String) Uniquely identifies the resource.job_definition_arn
(String) The Amazon Resource Name (ARN) of job definition.job_resources
Required:
cluster_config
(Attributes) Configuration for the cluster used to run model monitoring jobs. (see below for nested schema)job_resources.cluster_config
Required:
instance_count
(Number) The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.instance_type
(String) The ML compute instance type for the processing job.volume_size_in_gb
(Number) The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.Optional:
volume_kms_key_id
(String) The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.model_explainability_app_specification
Required:
config_uri
(String) The S3 URI to an analysis configuration fileimage_uri
(String) The container image to be run by the monitoring job.Optional:
environment
(Map of String) Sets the environment variables in the Docker containermodel_explainability_job_input
Optional:
batch_transform_input
(Attributes) The batch transform input for a monitoring job. (see below for nested schema)endpoint_input
(Attributes) The endpoint for a monitoring job. (see below for nested schema)model_explainability_job_input.batch_transform_input
Required:
data_captured_destination_s3_uri
(String) A URI that identifies the Amazon S3 storage location where Batch Transform Job captures data.dataset_format
(Attributes) The dataset format of the data to monitor (see below for nested schema)local_path
(String) Path to the filesystem where the endpoint data is available to the container.Optional:
features_attribute
(String) JSONpath to locate features in JSONlines datasetinference_attribute
(String) Index or JSONpath to locate predicted label(s)probability_attribute
(String) Index or JSONpath to locate probabilitiess3_data_distribution_type
(String) Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defauts to FullyReplicateds3_input_mode
(String) Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.model_explainability_job_input.batch_transform_input.dataset_format
Optional:
csv
(Attributes) The CSV format (see below for nested schema)json
(Attributes) The Json format (see below for nested schema)parquet
(Boolean) A flag indicating if the dataset format is Parquetmodel_explainability_job_input.batch_transform_input.dataset_format.csv
Optional:
header
(Boolean) A boolean flag indicating if given CSV has headermodel_explainability_job_input.batch_transform_input.dataset_format.json
Optional:
line
(Boolean) A boolean flag indicating if it is JSON line formatmodel_explainability_job_input.endpoint_input
Required:
endpoint_name
(String) The name of the endpoint used to run the monitoring job.local_path
(String) Path to the filesystem where the endpoint data is available to the container.Optional:
features_attribute
(String) JSONpath to locate features in JSONlines datasetinference_attribute
(String) Index or JSONpath to locate predicted label(s)probability_attribute
(String) Index or JSONpath to locate probabilitiess3_data_distribution_type
(String) Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defauts to FullyReplicateds3_input_mode
(String) Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.model_explainability_job_output_config
Required:
monitoring_outputs
(Attributes List) Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded. (see below for nested schema)Optional:
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.model_explainability_job_output_config.monitoring_outputs
Required:
s3_output
(Attributes) Information about where and how to store the results of a monitoring job. (see below for nested schema)model_explainability_job_output_config.monitoring_outputs.s3_output
Required:
local_path
(String) The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.s3_uri
(String) A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.Optional:
s3_upload_mode
(String) Whether to upload the results of the monitoring job continuously or after the job completes.model_explainability_baseline_config
Optional:
baselining_job_name
(String) The name of a processing jobconstraints_resource
(Attributes) The baseline constraints resource for a monitoring job. (see below for nested schema)model_explainability_baseline_config.constraints_resource
Optional:
s3_uri
(String) The Amazon S3 URI for baseline constraint file in Amazon S3 that the current monitoring job should validated against.network_config
Optional:
enable_inter_container_traffic_encryption
(Boolean) Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.enable_network_isolation
(Boolean) Whether to allow inbound and outbound network calls to and from the containers used for the processing job.vpc_config
(Attributes) Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. (see below for nested schema)network_config.vpc_config
Required:
security_group_ids
(List of String) The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.subnets
(List of String) The ID of the subnets in the VPC to which you want to connect to your monitoring jobs.stopping_condition
Required:
max_runtime_in_seconds
(Number) The maximum runtime allowed in seconds.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 -.Import is supported using the following syntax:
$ terraform import awscc_sagemaker_model_explainability_job_definition.example <resource ID>