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[ aws . sagemaker ]

describe-processing-job

Description

Returns a description of a processing job.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.

Synopsis

  describe-processing-job
--processing-job-name <value>
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]

Options

--processing-job-name (string)

The name of the processing job. The name must be unique within an AWS Region in the AWS account.

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI 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.

--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. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See 'aws help' for descriptions of global parameters.

Output

ProcessingInputs -> (list)

The inputs for a processing job.

(structure)

The inputs for a processing job.

InputName -> (string)

The name of the inputs for the processing job.

S3Input -> (structure)

The S3 inputs for the processing job.

S3Uri -> (string)

The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.

LocalPath -> (string)

The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing job. LocalPath is an absolute path to the input data.

S3DataType -> (string)

Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.

S3InputMode -> (string)

Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

S3DataDistributionType -> (string)

Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key .

S3CompressionType -> (string)

Whether to use Gzip compression for Amazon S3 storage.

ProcessingOutputConfig -> (structure)

Output configuration for the processing job.

Outputs -> (list)

Output configuration information for a processing job.

(structure)

Describes the results of a processing job.

OutputName -> (string)

The name for the processing job output.

S3Output -> (structure)

Configuration for processing job outputs in Amazon S3.

S3Uri -> (string)

A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.

LocalPath -> (string)

The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job. LocalPath is an absolute path to the input data.

S3UploadMode -> (string)

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

KmsKeyId -> (string)

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.

ProcessingJobName -> (string)

The name of the processing job. The name must be unique within an AWS Region in the AWS account.

ProcessingResources -> (structure)

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.

ClusterConfig -> (structure)

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

InstanceCount -> (integer)

The number of ML compute instances to use in the processing 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 processing job.

StoppingCondition -> (structure)

The time limit for how long the processing job is allowed to run.

MaxRuntimeInSeconds -> (integer)

Specifies the maximum runtime in seconds.

AppSpecification -> (structure)

Configures the processing job to run a specified container image.

ImageUri -> (string)

The container image to be run by the processing job.

ContainerEntrypoint -> (list)

The entrypoint for a container used to run a processing job.

(string)

ContainerArguments -> (list)

The arguments for a container used to run a processing job.

(string)

Environment -> (map)

The environment variables set in the Docker container.

key -> (string)

value -> (string)

NetworkConfig -> (structure)

Networking options for a processing 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.

ExperimentConfig -> (structure)

The configuration information used to create an experiment.

ExperimentName -> (string)

The name of an existing experiment to associate the trial component with.

TrialName -> (string)

The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

TrialComponentDisplayName -> (string)

The display name for the trial component. If this key isn't specified, the display name is the trial component name.

ProcessingJobArn -> (string)

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

ProcessingJobStatus -> (string)

Provides the status of a processing job.

ExitMessage -> (string)

An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

FailureReason -> (string)

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

ProcessingEndTime -> (timestamp)

The time at which the processing job completed.

ProcessingStartTime -> (timestamp)

The time at which the processing job started.

LastModifiedTime -> (timestamp)

The time at which the processing job was last modified.

CreationTime -> (timestamp)

The time at which the processing job was created.

MonitoringScheduleArn -> (string)

The ARN of a monitoring schedule for an endpoint associated with this processing job.

AutoMLJobArn -> (string)

The ARN of an AutoML job associated with this processing job.

TrainingJobArn -> (string)

The ARN of a training job associated with this processing job.