Struct CreateAutoMlJobV2Input

#[non_exhaustive]
pub struct CreateAutoMlJobV2Input {
    pub auto_ml_job_name: Option<String>,
    pub auto_ml_job_input_data_config: Option<Vec<AutoMlJobChannel>>,
    pub output_data_config: Option<AutoMlOutputDataConfig>,
    pub auto_ml_problem_type_config: Option<AutoMlProblemTypeConfig>,
    pub role_arn: Option<String>,
    pub tags: Option<Vec<Tag>>,
    pub security_config: Option<AutoMlSecurityConfig>,
    pub auto_ml_job_objective: Option<AutoMlJobObjective>,
    pub model_deploy_config: Option<ModelDeployConfig>,
    pub data_split_config: Option<AutoMlDataSplitConfig>,
    pub auto_ml_compute_config: Option<AutoMlComputeConfig>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

auto_ml_job_name: Option<String>

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

auto_ml_job_input_data_config: Option<Vec<AutoMlJobChannel>>

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

• For tabular problem types: S3Prefix, ManifestFile.

• For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

• For text classification: S3Prefix.

• For time-series forecasting: S3Prefix.

• For text generation (LLMs fine-tuning): S3Prefix.

output_data_config: Option<AutoMlOutputDataConfig>

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

auto_ml_problem_type_config: Option<AutoMlProblemTypeConfig>

Defines the configuration settings of one of the supported problem types.

role_arn: Option<String>

The ARN of the role that is used to access the data.

tags: Option<Vec<Tag>>

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

security_config: Option<AutoMlSecurityConfig>

The security configuration for traffic encryption or Amazon VPC settings.

auto_ml_job_objective: Option<AutoMlJobObjective>

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

• For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

model_deploy_config: Option<ModelDeployConfig>

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

data_split_config: Option<AutoMlDataSplitConfig>

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

auto_ml_compute_config: Option<AutoMlComputeConfig>

Specifies the compute configuration for the AutoML job V2.

Implementations

impl CreateAutoMlJobV2Input

pub fn auto_ml_job_name(&self) -> Option<&str>

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

pub fn auto_ml_job_input_data_config(&self) -> &[AutoMlJobChannel]

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

• For tabular problem types: S3Prefix, ManifestFile.

• For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

• For text classification: S3Prefix.

• For time-series forecasting: S3Prefix.

• For text generation (LLMs fine-tuning): S3Prefix.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .auto_ml_job_input_data_config.is_none().

pub fn output_data_config(&self) -> Option<&AutoMlOutputDataConfig>

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

pub fn auto_ml_problem_type_config(&self) -> Option<&AutoMlProblemTypeConfig>

Defines the configuration settings of one of the supported problem types.

pub fn role_arn(&self) -> Option<&str>

The ARN of the role that is used to access the data.

pub fn tags(&self) -> &[Tag]

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

pub fn security_config(&self) -> Option<&AutoMlSecurityConfig>

The security configuration for traffic encryption or Amazon VPC settings.

pub fn auto_ml_job_objective(&self) -> Option<&AutoMlJobObjective>

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

• For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

pub fn model_deploy_config(&self) -> Option<&ModelDeployConfig>

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

pub fn data_split_config(&self) -> Option<&AutoMlDataSplitConfig>

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

pub fn auto_ml_compute_config(&self) -> Option<&AutoMlComputeConfig>

Specifies the compute configuration for the AutoML job V2.

impl CreateAutoMlJobV2Input

pub fn builder() -> CreateAutoMlJobV2InputBuilder

Creates a new builder-style object to manufacture CreateAutoMlJobV2Input.

Trait Implementations

impl Clone for CreateAutoMlJobV2Input

fn clone(&self) -> CreateAutoMlJobV2Input

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMlJobV2Input

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

Formats the value using the given formatter.

impl PartialEq for CreateAutoMlJobV2Input

fn eq(&self, other: &CreateAutoMlJobV2Input) -> bool

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateAutoMlJobV2Input