Struct rusoto_sagemaker::CreateTrainingJobRequest

pub struct CreateTrainingJobRequest {
    pub algorithm_specification: AlgorithmSpecification,
    pub enable_inter_container_traffic_encryption: Option<bool>,
    pub enable_network_isolation: Option<bool>,
    pub hyper_parameters: Option<HashMap<String, String>>,
    pub input_data_config: Option<Vec<Channel>>,
    pub output_data_config: OutputDataConfig,
    pub resource_config: ResourceConfig,
    pub role_arn: String,
    pub stopping_condition: StoppingCondition,
    pub tags: Option<Vec<Tag>>,
    pub training_job_name: String,
    pub vpc_config: Option<VpcConfig>,
}

Fields

algorithm_specification: AlgorithmSpecification

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

enable_inter_container_traffic_encryption: Option<bool>

To encrypt all communications between ML compute instances in distributed training, choose True,. Encryption provides greater security for distributed training, but training can take longer because of additional communications between ML compute instances.

enable_network_isolation: Option<bool>

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

hyper_parameters: Option<HashMap<String, String>>

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

input_data_config: Option<Vec<Channel>>

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

output_data_config: OutputDataConfig

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

resource_config: ResourceConfig

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

role_arn: String

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

stopping_condition: StoppingCondition

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

tags: Option<Vec<Tag>>

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

training_job_name: String

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

vpc_config: Option<VpcConfig>

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Trait Implementations

impl PartialEq<CreateTrainingJobRequest> for CreateTrainingJobRequest

fn eq(&self, other: &CreateTrainingJobRequest) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &CreateTrainingJobRequest) -> bool

This method tests for !=.

impl Default for CreateTrainingJobRequest

fn default() -> CreateTrainingJobRequest

Returns the "default value" for a type.

impl Clone for CreateTrainingJobRequest

fn clone(&self) -> CreateTrainingJobRequest

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateTrainingJobRequest

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Serialize for CreateTrainingJobRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.