Struct aws_sdk_sagemaker::types::builders::TextGenerationJobConfigBuilder

#[non_exhaustive]
pub struct TextGenerationJobConfigBuilder { /* private fields */ }

A builder for TextGenerationJobConfig.

Implementations

impl TextGenerationJobConfigBuilder

pub fn completion_criteria(self, input: AutoMlJobCompletionCriteria) -> Self

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

pub fn set_completion_criteria( self, input: Option<AutoMlJobCompletionCriteria>, ) -> Self

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

pub fn get_completion_criteria(&self) -> &Option<AutoMlJobCompletionCriteria>

How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

pub fn base_model_name(self, input: impl Into<String>) -> Self

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

pub fn set_base_model_name(self, input: Option<String>) -> Self

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

pub fn get_base_model_name(&self) -> &Option<String>

The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

pub fn text_generation_hyper_parameters( self, k: impl Into<String>, v: impl Into<String>, ) -> Self

Adds a key-value pair to text_generation_hyper_parameters.

To override the contents of this collection use set_text_generation_hyper_parameters.

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

pub fn set_text_generation_hyper_parameters( self, input: Option<HashMap<String, String>>, ) -> Self

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

pub fn get_text_generation_hyper_parameters( &self, ) -> &Option<HashMap<String, String>>

The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

• "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

• "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

• "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

• "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

Here is an example where all four hyperparameters are configured.

{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

pub fn model_access_config(self, input: ModelAccessConfig) -> Self

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.

• If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.

• If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

pub fn set_model_access_config(self, input: Option<ModelAccessConfig>) -> Self

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.

• If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.

• If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

pub fn get_model_access_config(&self) -> &Option<ModelAccessConfig>

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.

• If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.

• If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

pub fn build(self) -> TextGenerationJobConfig

Consumes the builder and constructs a TextGenerationJobConfig.

Trait Implementations

impl Clone for TextGenerationJobConfigBuilder

fn clone(&self) -> TextGenerationJobConfigBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TextGenerationJobConfigBuilder

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

Formats the value using the given formatter.

impl Default for TextGenerationJobConfigBuilder

fn default() -> TextGenerationJobConfigBuilder

Returns the “default value” for a type.

impl PartialEq for TextGenerationJobConfigBuilder

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

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

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 TextGenerationJobConfigBuilder