[ aws . sagemaker ]

describe-monitoring-schedule

Description

Describes the schedule for a monitoring job.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  describe-monitoring-schedule
--monitoring-schedule-name <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options

--monitoring-schedule-name (string)

Name of a previously created monitoring schedule.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.

Output

MonitoringScheduleArn -> (string)

The Amazon Resource Name (ARN) of the monitoring schedule.

MonitoringScheduleName -> (string)

Name of the monitoring schedule.

MonitoringScheduleStatus -> (string)

The status of an monitoring job.

FailureReason -> (string)

A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.

CreationTime -> (timestamp)

The time at which the monitoring job was created.

LastModifiedTime -> (timestamp)

The time at which the monitoring job was last modified.

MonitoringScheduleConfig -> (structure)

The configuration object that specifies the monitoring schedule and defines the monitoring job.

ScheduleConfig -> (structure)

Configures the monitoring schedule.

ScheduleExpression -> (string)

A cron expression that describes details about the monitoring schedule.

Currently the only supported cron expressions are:

• If you want to set the job to start every hour, please use the following: Hourly: cron(0 * ? * * *)

• If you want to start the job daily: cron(0 [00-23] ? * * *)

For example, the following are valid cron expressions:

• Daily at noon UTC: cron(0 12 ? * * *)

• Daily at midnight UTC: cron(0 0 ? * * *)

To support running every 6, 12 hours, the following are also supported:

cron(0 [00-23]/[01-24] ? * * *)

For example, the following are valid cron expressions:

• Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)

• Every two hours starting at midnight: cron(0 0/2 ? * * *)

Note

• Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.

• We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.

MonitoringJobDefinition -> (structure)

Defines the monitoring job.

BaselineConfig -> (structure)

Baseline configuration used to validate that the data conforms to the specified constraints and statistics

ConstraintsResource -> (structure)

The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

S3Uri -> (string)

The Amazon S3 URI for the constraints resource.

StatisticsResource -> (structure)

The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

S3Uri -> (string)

The Amazon S3 URI for the statistics resource.

MonitoringInputs -> (list)

The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.

(structure)

The inputs for a monitoring job.

EndpointInput -> (structure)

The endpoint for a monitoring job.

EndpointName -> (string)

An endpoint in customer’s account which has enabled DataCaptureConfig enabled.

LocalPath -> (string)

Path to the filesystem where the endpoint data is available to the container.

S3InputMode -> (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 .

S3DataDistributionType -> (string)

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defauts to FullyReplicated

MonitoringOutputConfig -> (structure)

The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).

MonitoringOutputs -> (list)

Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

(structure)

The output object for a monitoring job.

S3Output -> (structure)

The Amazon S3 storage location where the results of a monitoring job are saved.

S3Uri -> (string)

A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

LocalPath -> (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.

S3UploadMode -> (string)

Whether to upload the results of the monitoring job continuously or after the job completes.

KmsKeyId -> (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.

MonitoringResources -> (structure)

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.

ClusterConfig -> (structure)

The configuration for the cluster resources used to run the processing job.

InstanceCount -> (integer)

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.

InstanceType -> (string)

The ML compute instance type for the processing job.

VolumeSizeInGB -> (integer)

The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

VolumeKmsKeyId -> (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.

MonitoringAppSpecification -> (structure)

Configures the monitoring job to run a specified Docker container image.

ImageUri -> (string)

The container image to be run by the monitoring job.

ContainerEntrypoint -> (list)

Specifies the entrypoint for a container used to run the monitoring job.

(string)

ContainerArguments -> (list)

An array of arguments for the container used to run the monitoring job.

(string)

RecordPreprocessorSourceUri -> (string)

An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

PostAnalyticsProcessorSourceUri -> (string)

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

StoppingCondition -> (structure)

Specifies a time limit for how long the monitoring job is allowed to run.

MaxRuntimeInSeconds -> (integer)

The maximum runtime allowed in seconds.

Environment -> (map)

Sets the environment variables in the Docker container.

key -> (string)

value -> (string)

NetworkConfig -> (structure)

Specifies networking options for an monitoring job.

EnableInterContainerTrafficEncryption -> (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.

EnableNetworkIsolation -> (boolean)

Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

VpcConfig -> (structure)

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. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .

SecurityGroupIds -> (list)

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

(string)

Subnets -> (list)

The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

(string)

RoleArn -> (string)

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

EndpointName -> (string)

The name of the endpoint for the monitoring job.

LastMonitoringExecutionSummary -> (structure)

Describes metadata on the last execution to run, if there was one.

MonitoringScheduleName -> (string)

The name of the monitoring schedule.

ScheduledTime -> (timestamp)

The time the monitoring job was scheduled.

CreationTime -> (timestamp)

The time at which the monitoring job was created.

LastModifiedTime -> (timestamp)

A timestamp that indicates the last time the monitoring job was modified.

MonitoringExecutionStatus -> (string)

The status of the monitoring job.

ProcessingJobArn -> (string)

The Amazon Resource Name (ARN) of the monitoring job.

EndpointName -> (string)

The name of teh endpoint used to run the monitoring job.

FailureReason -> (string)

Contains the reason a monitoring job failed, if it failed.