Container Contract Inputs

The Amazon SageMaker Model Monitor platform invokes your container code according to a specified schedule. If you choose to write your own container code, the following environment variables are available. In this context, you can analyze the current dataset or evaluate the constraints if you choose and emit metrics, if applicable.

The available environment variables are the same for real-time endpoints and batch transform jobs, except for the dataset_format variable. If you are using a real-time endpoint, the dataset_format variable supports the following options:

{\"sagemakerCaptureJson\": {\"captureIndexNames\": [\"endpointInput\",\"endpointOutput\"]}}

If you are using a batch transform job, the dataset_format supports the following options:

{\"csv\": {\"header\": [\"true\",\"false\"]}}

{\"json\": {\"line\": [\"true\",\"false\"]}}

{\"parquet\": {}}

The following code sample shows the complete set of environment variables available for your container code (and uses the dataset_format format for a real-time endpoint).

"Environment": {
 "dataset_format": "{\"sagemakerCaptureJson\": {\"captureIndexNames\": [\"endpointInput\",\"endpointOutput\"]}}",
 "dataset_source": "/opt/ml/processing/endpointdata",
 "end_time": "2019-12-01T16: 20: 00Z",
 "output_path": "/opt/ml/processing/resultdata",
 "publish_cloudwatch_metrics": "Disabled",
 "sagemaker_endpoint_name": "endpoint-name",
 "sagemaker_monitoring_schedule_name": "schedule-name",
 "start_time": "2019-12-01T15: 20: 00Z"
}

Parameters

Parameter Name	Description
dataset_format	For a job started from a MonitoringSchedule backed by an Endpoint, this is sageMakerCaptureJson with the capture indices endpointInput,or endpointOutput, or both. For a batch transform job, this specifies the data format, whether CSV, JSON, or Parquet.
dataset_source	If you are using a real-time endpoint, the local path in which the data corresponding to the monitoring period, as specified by start_time and end_time, are available. At this path, the data is available in /{endpoint-name}/{variant-name}/yyyy/mm/dd/hh. We sometimes download more than what is specified by the start and end times. It is up to the container code to parse the data as required.
output_path	The local path to write output reports and other files. You specify this parameter in the CreateMonitoringSchedule request as MonitoringOutputConfig.MonitoringOutput[0].LocalPath. It is uploaded to the S3Uri path specified in MonitoringOutputConfig.MonitoringOutput[0].S3Uri.
publish_cloudwatch_metrics	For a job launched by CreateMonitoringSchedule, this parameter is set to Enabled. The container can choose to write the Amazon CloudWatch output file at [filepath].
sagemaker_endpoint_name	If you are using a real-time endpoint, the name of the Endpoint that this scheduled job was launched for.
sagemaker_monitoring_schedule_name	The name of the MonitoringSchedule that launched this job.
*sagemaker_endpoint_datacapture_prefix*	If you are using a real-time endpoint, the prefix specified in the DataCaptureConfig parameter of the Endpoint. The container can use this if it needs to directly access more data than already downloaded by SageMaker AI at the dataset_source path.
start_time, end_time	The time window for this analysis run. For example, for a job scheduled to run at 05:00 UTC and a job that runs on 20/02/2020, start_time: is 2020-02-19T06:00:00Z and end_time: is 2020-02-20T05:00:00Z
baseline_constraints:	The local path of the baseline constraint file specified in BaselineConfig.ConstraintResource.S3Uri. This is available only if this parameter was specified in the CreateMonitoringSchedule request.
baseline_statistics	The local path to the baseline statistics file specified in BaselineConfig.StatisticsResource.S3Uri. This is available only if this parameter was specified in the CreateMonitoringSchedule request.: