SageMaker Processing Job

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An AWS SageMaker Processing Job is a managed resource that lets you run data processing and model evaluation workloads at scale. It provides a fully managed environment to preprocess data, perform feature engineering, evaluate models, or run custom scripts using containerized code. The job runs on specified compute instances, automatically handles resource provisioning, and stores outputs in Amazon S3.

aws.sagemaker_processing_job

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
app_specification	core	json	Configures the processing job to run a specified container image.
auto_ml_job_arn	core	string	The ARN of an AutoML job associated with this processing job.
creation_time	core	timestamp	The time at which the processing job was created.
environment	core	hstore	The environment variables set in the Docker container.
exit_message	core	string	An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
experiment_config	core	json	The configuration information used to create an experiment.
failure_reason	core	string	A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
last_modified_time	core	timestamp	The time at which the processing job was last modified.
monitoring_schedule_arn	core	string	The ARN of a monitoring schedule for an endpoint associated with this processing job.
network_config	core	json	Networking options for a processing job.
processing_end_time	core	timestamp	The time at which the processing job completed.
processing_inputs	core	json	The inputs for a processing job.
processing_job_arn	core	string	The Amazon Resource Name (ARN) of the processing job.
processing_job_name	core	string	The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
processing_job_status	core	string	Provides the status of a processing job.
processing_output_config	core	json	Output configuration for the processing job.
processing_resources	core	json	Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
processing_start_time	core	timestamp	The time at which the processing job started.
role_arn	core	string	The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
stopping_condition	core	json	The time limit for how long the processing job is allowed to run.
tags	core	hstore
training_job_arn	core	string	The ARN of a training job associated with this processing job.