You are viewing documentation for version 2 of the AWS SDK for Ruby. Version 3 documentation can be found here.
Class: Aws::SageMaker::Types::CreateProcessingJobRequest
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::CreateProcessingJobRequest
- Defined in:
- (unknown)
Overview
When passing CreateProcessingJobRequest as input to an Aws::Client method, you can use a vanilla Hash:
{
processing_inputs: [
{
input_name: "String", # required
s3_input: { # required
s3_uri: "S3Uri", # required
local_path: "ProcessingLocalPath", # required
s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
s3_input_mode: "Pipe", # required, accepts Pipe, File
s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
s3_compression_type: "None", # accepts None, Gzip
},
},
],
processing_output_config: {
outputs: [ # required
{
output_name: "String", # required
s3_output: { # required
s3_uri: "S3Uri", # required
local_path: "ProcessingLocalPath", # required
s3_upload_mode: "Continuous", # required, accepts Continuous, EndOfJob
},
},
],
kms_key_id: "KmsKeyId",
},
processing_job_name: "ProcessingJobName", # required
processing_resources: { # required
cluster_config: { # required
instance_count: 1, # required
instance_type: "ml.t3.medium", # required, accepts 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
volume_size_in_gb: 1, # required
volume_kms_key_id: "KmsKeyId",
},
},
stopping_condition: {
max_runtime_in_seconds: 1, # required
},
app_specification: { # required
image_uri: "ImageUri", # required
container_entrypoint: ["ContainerEntrypointString"],
container_arguments: ["ContainerArgument"],
},
environment: {
"ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
},
network_config: {
enable_inter_container_traffic_encryption: false,
enable_network_isolation: false,
vpc_config: {
security_group_ids: ["SecurityGroupId"], # required
subnets: ["SubnetId"], # required
},
},
role_arn: "RoleArn", # required
tags: [
{
key: "TagKey", # required
value: "TagValue", # required
},
],
experiment_config: {
experiment_name: "ExperimentEntityName",
trial_name: "ExperimentEntityName",
trial_component_display_name: "ExperimentEntityName",
},
}
Instance Attribute Summary collapse
-
#app_specification ⇒ Types::AppSpecification
Configures the processing job to run a specified Docker container image.
-
#environment ⇒ Hash<String,String>
Sets the environment variables in the Docker container.
-
#experiment_config ⇒ Types::ExperimentConfig
Associates a SageMaker job as a trial component with an experiment and trial.
-
#network_config ⇒ Types::NetworkConfig
Networking options for a processing job.
-
#processing_inputs ⇒ Array<Types::ProcessingInput>
For each input, data is downloaded from S3 into the processing container before the processing job begins running if \"S3InputMode\" is set to
File
. -
#processing_job_name ⇒ String
The name of the processing job.
-
#processing_output_config ⇒ Types::ProcessingOutputConfig
Output configuration for the processing job.
-
#processing_resources ⇒ Types::ProcessingResources
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job.
-
#role_arn ⇒ String
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
-
#stopping_condition ⇒ Types::ProcessingStoppingCondition
The time limit for how long the processing job is allowed to run.
-
#tags ⇒ Array<Types::Tag>
(Optional) An array of key-value pairs.
Instance Attribute Details
#app_specification ⇒ Types::AppSpecification
Configures the processing job to run a specified Docker container image.
#environment ⇒ Hash<String,String>
Sets the environment variables in the Docker container.
#experiment_config ⇒ Types::ExperimentConfig
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
#network_config ⇒ Types::NetworkConfig
Networking options for a processing job.
#processing_inputs ⇒ Array<Types::ProcessingInput>
For each input, data is downloaded from S3 into the processing container
before the processing job begins running if \"S3InputMode\" is set to
File
.
#processing_job_name ⇒ String
The name of the processing job. The name must be unique within an AWS Region in the AWS account.
#processing_output_config ⇒ Types::ProcessingOutputConfig
Output configuration for the processing job.
#processing_resources ⇒ Types::ProcessingResources
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.
#role_arn ⇒ String
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
#stopping_condition ⇒ Types::ProcessingStoppingCondition
The time limit for how long the processing job is allowed to run.
#tags ⇒ Array<Types::Tag>
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.