create_processing_job(**kwargs)¶Creates a processing job.
See also: AWS API Documentation
Request Syntax
response = client.create_processing_job(
ProcessingInputs=[
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
ProcessingOutputConfig={
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
ProcessingJobName='string',
ProcessingResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
AppSpecification={
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
Environment={
'string': 'string'
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
}
)
An array of inputs configuring the data to download into the processing container.
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
The name for the processing job input.
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
Configuration for downloading input data from Amazon S3 into the processing container.
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
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.
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
Configuration for a Dataset Definition input.
Configuration for Athena Dataset Definition input.
The name of the data catalog used in Athena query execution.
The name of the database used in the Athena query execution.
The SQL query statements, to be executed.
The name of the workgroup in which the Athena query is being started.
The location in Amazon S3 where Athena query results are stored.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
The data storage format for Athena query results.
The compression used for Athena query results.
Configuration for Redshift Dataset Definition input.
The Redshift cluster Identifier.
The name of the Redshift database used in Redshift query execution.
The database user name used in Redshift query execution.
The SQL query statements to be executed.
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
The location in Amazon S3 where the Redshift query results are stored.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
The data storage format for Redshift query results.
The compression used for Redshift query results.
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
Whether to use File or Pipe input mode. In File (default) 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.
Output configuration for the processing job.
An array of outputs configuring the data to upload from the processing container.
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
The name for the processing job output.
Configuration for processing job outputs in Amazon S3.
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
Whether to upload the results of the processing job continuously or after the job completes.
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
The Amazon Web Services Key Management Service (Amazon Web Services 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.
[REQUIRED]
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
[REQUIRED]
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.
The configuration for the resources in a cluster used to run the processing job.
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.
The ML compute instance type for the processing job.
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.
The Amazon Web Services Key Management Service (Amazon Web Services 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.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
The time limit for how long the processing job is allowed to run.
Specifies the maximum runtime in seconds.
[REQUIRED]
Configures the processing job to run a specified Docker container image.
The container image to be run by the processing job.
The entrypoint for a container used to run a processing job.
The arguments for a container used to run a processing job.
The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
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.
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
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.
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
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.
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
The tag key. Tag keys must be unique per resource.
The tag value.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
The name of an existing experiment to associate with the trial component.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
The name of the experiment run to associate with the trial component.
dict
Response Syntax
{
'ProcessingJobArn': 'string'
}
Response Structure
(dict) --
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the processing job.
Exceptions
SageMaker.Client.exceptions.ResourceInUseSageMaker.Client.exceptions.ResourceLimitExceededSageMaker.Client.exceptions.ResourceNotFound