src/sagemaker/model_monitor/clarify_model_monitoring.py [245:328]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - constraints=None, enable_cloudwatch_metrics=None, role=None, instance_count=None, instance_type=None, volume_size_in_gb=None, volume_kms_key=None, output_kms_key=None, max_runtime_in_seconds=None, env=None, tags=None, network_config=None, batch_transform_input=None, ): """Build the request for job definition creation API Args: monitoring_schedule_name (str): Monitoring schedule name. job_definition_name (str): Job definition name. If not specified then a default one will be generated. image_uri (str): The uri of the image to use for the jobs started by the Monitor. latest_baselining_job_name (str): name of the last baselining job. latest_baselining_job_config (ClarifyBaseliningConfig): analysis config from last baselining job. existing_job_desc (dict): description of existing job definition. It will be updated by values that were passed in, and then used to create the new job definition. endpoint_input (str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor. This can either be the endpoint name or an EndpointInput. ground_truth_input (str): S3 URI to ground truth dataset. analysis_config (str or BiasAnalysisConfig or ExplainabilityAnalysisConfig): URI to the analysis_config.json for the bias job. If it is None then configuration of latest baselining job config will be reused. If no baselining job then fail the call. output_s3_uri (str): S3 destination of the constraint_violations and analysis result. Default: "s3:////output" constraints (sagemaker.model_monitor.Constraints or str): If provided it will be used for monitoring the endpoint. It can be a Constraints object or an S3 uri pointing to a constraints JSON file. enable_cloudwatch_metrics (bool): Whether to publish cloudwatch metrics as part of the baselining or monitoring jobs. role (str): An AWS IAM role. The Amazon SageMaker jobs use this role. instance_count (int): The number of instances to run the jobs with. instance_type (str): Type of EC2 instance to use for the job, for example, 'ml.m5.xlarge'. volume_size_in_gb (int): Size in GB of the EBS volume to use for storing data during processing (default: 30). volume_kms_key (str): A KMS key for the job's volume. output_kms_key (str): KMS key id for output. max_runtime_in_seconds (int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600 env (dict): Environment variables to be passed to the job. tags (Optional[Tags]): List of tags to be passed to the job. network_config (sagemaker.network.NetworkConfig): A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets. batch_transform_input (sagemaker.model_monitor.BatchTransformInput): Inputs to run the monitoring schedule on the batch transform Returns: dict: request parameters to create job definition. """ if existing_job_desc is not None: app_specification = existing_job_desc[ "{}AppSpecification".format(self.monitoring_type()) ] baseline_config = existing_job_desc.get( "{}BaselineConfig".format(self.monitoring_type()), {} ) job_input = existing_job_desc["{}JobInput".format(self.monitoring_type())] job_output = existing_job_desc["{}JobOutputConfig".format(self.monitoring_type())] cluster_config = existing_job_desc["JobResources"]["ClusterConfig"] if role is None: role = existing_job_desc["RoleArn"] existing_network_config = existing_job_desc.get("NetworkConfig") stop_condition = existing_job_desc.get("StoppingCondition", {}) else: app_specification = {} baseline_config = {} job_input = {} job_output = {} cluster_config = {} existing_network_config = None stop_condition = {} - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - src/sagemaker/model_monitor/model_monitoring.py [2653:2730]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - constraints=None, enable_cloudwatch_metrics=None, role=None, instance_count=None, instance_type=None, volume_size_in_gb=None, volume_kms_key=None, output_kms_key=None, max_runtime_in_seconds=None, env=None, tags=None, network_config=None, batch_transform_input=None, ): """Build the request for job definition creation API Args: monitoring_schedule_name (str): Monitoring schedule name. job_definition_name (str): Job definition name. If not specified then a default one will be generated. image_uri (str): The uri of the image to use for the jobs started by the Monitor. latest_baselining_job_name (str): name of the last baselining job. existing_job_desc (dict): description of existing job definition. It will be updated by values that were passed in, and then used to create the new job definition. endpoint_input (str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor. This can either be the endpoint name or an EndpointInput. output_s3_uri (str): S3 destination of the constraint_violations and analysis result. Default: "s3:////output" constraints (sagemaker.model_monitor.Constraints or str): If provided it will be used for monitoring the endpoint. It can be a Constraints object or an S3 uri pointing to a constraints JSON file. enable_cloudwatch_metrics (bool): Whether to publish cloudwatch metrics as part of the baselining or monitoring jobs. role (str): An AWS IAM role. The Amazon SageMaker jobs use this role. instance_count (int): The number of instances to run the jobs with. instance_type (str): Type of EC2 instance to use for the job, for example, 'ml.m5.xlarge'. volume_size_in_gb (int): Size in GB of the EBS volume to use for storing data during processing (default: 30). volume_kms_key (str): A KMS key for the job's volume. output_kms_key (str): KMS key id for output. max_runtime_in_seconds (int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600 env (dict): Environment variables to be passed to the job. tags (Optional[Tags]): List of tags to be passed to the job. network_config (sagemaker.network.NetworkConfig): A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets. batch_transform_input (sagemaker.model_monitor.BatchTransformInput): Inputs to run the monitoring schedule on the batch transform Returns: dict: request parameters to create job definition. """ if existing_job_desc is not None: app_specification = existing_job_desc[ "{}AppSpecification".format(self.monitoring_type()) ] baseline_config = existing_job_desc.get( "{}BaselineConfig".format(self.monitoring_type()), {} ) job_input = existing_job_desc["{}JobInput".format(self.monitoring_type())] job_output = existing_job_desc["{}JobOutputConfig".format(self.monitoring_type())] cluster_config = existing_job_desc["JobResources"]["ClusterConfig"] if role is None: role = existing_job_desc["RoleArn"] existing_network_config = existing_job_desc.get("NetworkConfig") stop_condition = existing_job_desc.get("StoppingCondition", {}) else: app_specification = {} baseline_config = {} job_input = {} job_output = {} cluster_config = {} existing_network_config = None stop_condition = {} - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -