pub struct CreateOptimizationJobFluentBuilder { /* private fields */ }Expand description
Fluent builder constructing a request to CreateOptimizationJob.
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
Implementations§
Source§impl CreateOptimizationJobFluentBuilder
impl CreateOptimizationJobFluentBuilder
Sourcepub fn as_input(&self) -> &CreateOptimizationJobInputBuilder
pub fn as_input(&self) -> &CreateOptimizationJobInputBuilder
Access the CreateOptimizationJob as a reference.
Sourcepub async fn send(
self,
) -> Result<CreateOptimizationJobOutput, SdkError<CreateOptimizationJobError, HttpResponse>>
pub async fn send( self, ) -> Result<CreateOptimizationJobOutput, SdkError<CreateOptimizationJobError, HttpResponse>>
Sends the request and returns the response.
If an error occurs, an SdkError will be returned with additional details that
can be matched against.
By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.
Sourcepub fn customize(
self,
) -> CustomizableOperation<CreateOptimizationJobOutput, CreateOptimizationJobError, Self>
pub fn customize( self, ) -> CustomizableOperation<CreateOptimizationJobOutput, CreateOptimizationJobError, Self>
Consumes this builder, creating a customizable operation that can be modified before being sent.
Sourcepub fn optimization_job_name(self, input: impl Into<String>) -> Self
pub fn optimization_job_name(self, input: impl Into<String>) -> Self
A custom name for the new optimization job.
Sourcepub fn set_optimization_job_name(self, input: Option<String>) -> Self
pub fn set_optimization_job_name(self, input: Option<String>) -> Self
A custom name for the new optimization job.
Sourcepub fn get_optimization_job_name(&self) -> &Option<String>
pub fn get_optimization_job_name(&self) -> &Option<String>
A custom name for the new optimization job.
Sourcepub fn role_arn(self, input: impl Into<String>) -> Self
pub fn role_arn(self, input: impl Into<String>) -> Self
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
During model optimization, Amazon SageMaker AI needs your permission to:
-
Read input data from an S3 bucket
-
Write model artifacts to an S3 bucket
-
Write logs to Amazon CloudWatch Logs
-
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.
Sourcepub fn set_role_arn(self, input: Option<String>) -> Self
pub fn set_role_arn(self, input: Option<String>) -> Self
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
During model optimization, Amazon SageMaker AI needs your permission to:
-
Read input data from an S3 bucket
-
Write model artifacts to an S3 bucket
-
Write logs to Amazon CloudWatch Logs
-
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.
Sourcepub fn get_role_arn(&self) -> &Option<String>
pub fn get_role_arn(&self) -> &Option<String>
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.
During model optimization, Amazon SageMaker AI needs your permission to:
-
Read input data from an S3 bucket
-
Write model artifacts to an S3 bucket
-
Write logs to Amazon CloudWatch Logs
-
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.
Sourcepub fn model_source(self, input: OptimizationJobModelSource) -> Self
pub fn model_source(self, input: OptimizationJobModelSource) -> Self
The location of the source model to optimize with an optimization job.
Sourcepub fn set_model_source(self, input: Option<OptimizationJobModelSource>) -> Self
pub fn set_model_source(self, input: Option<OptimizationJobModelSource>) -> Self
The location of the source model to optimize with an optimization job.
Sourcepub fn get_model_source(&self) -> &Option<OptimizationJobModelSource>
pub fn get_model_source(&self) -> &Option<OptimizationJobModelSource>
The location of the source model to optimize with an optimization job.
Sourcepub fn deployment_instance_type(
self,
input: OptimizationJobDeploymentInstanceType,
) -> Self
pub fn deployment_instance_type( self, input: OptimizationJobDeploymentInstanceType, ) -> Self
The type of instance that hosts the optimized model that you create with the optimization job.
Sourcepub fn set_deployment_instance_type(
self,
input: Option<OptimizationJobDeploymentInstanceType>,
) -> Self
pub fn set_deployment_instance_type( self, input: Option<OptimizationJobDeploymentInstanceType>, ) -> Self
The type of instance that hosts the optimized model that you create with the optimization job.
Sourcepub fn get_deployment_instance_type(
&self,
) -> &Option<OptimizationJobDeploymentInstanceType>
pub fn get_deployment_instance_type( &self, ) -> &Option<OptimizationJobDeploymentInstanceType>
The type of instance that hosts the optimized model that you create with the optimization job.
Sourcepub fn optimization_environment(
self,
k: impl Into<String>,
v: impl Into<String>,
) -> Self
pub fn optimization_environment( self, k: impl Into<String>, v: impl Into<String>, ) -> Self
Adds a key-value pair to OptimizationEnvironment.
To override the contents of this collection use set_optimization_environment.
The environment variables to set in the model container.
Sourcepub fn set_optimization_environment(
self,
input: Option<HashMap<String, String>>,
) -> Self
pub fn set_optimization_environment( self, input: Option<HashMap<String, String>>, ) -> Self
The environment variables to set in the model container.
Sourcepub fn get_optimization_environment(&self) -> &Option<HashMap<String, String>>
pub fn get_optimization_environment(&self) -> &Option<HashMap<String, String>>
The environment variables to set in the model container.
Sourcepub fn optimization_configs(self, input: OptimizationConfig) -> Self
pub fn optimization_configs(self, input: OptimizationConfig) -> Self
Appends an item to OptimizationConfigs.
To override the contents of this collection use set_optimization_configs.
Settings for each of the optimization techniques that the job applies.
Sourcepub fn set_optimization_configs(
self,
input: Option<Vec<OptimizationConfig>>,
) -> Self
pub fn set_optimization_configs( self, input: Option<Vec<OptimizationConfig>>, ) -> Self
Settings for each of the optimization techniques that the job applies.
Sourcepub fn get_optimization_configs(&self) -> &Option<Vec<OptimizationConfig>>
pub fn get_optimization_configs(&self) -> &Option<Vec<OptimizationConfig>>
Settings for each of the optimization techniques that the job applies.
Sourcepub fn output_config(self, input: OptimizationJobOutputConfig) -> Self
pub fn output_config(self, input: OptimizationJobOutputConfig) -> Self
Details for where to store the optimized model that you create with the optimization job.
Sourcepub fn set_output_config(
self,
input: Option<OptimizationJobOutputConfig>,
) -> Self
pub fn set_output_config( self, input: Option<OptimizationJobOutputConfig>, ) -> Self
Details for where to store the optimized model that you create with the optimization job.
Sourcepub fn get_output_config(&self) -> &Option<OptimizationJobOutputConfig>
pub fn get_output_config(&self) -> &Option<OptimizationJobOutputConfig>
Details for where to store the optimized model that you create with the optimization job.
Sourcepub fn stopping_condition(self, input: StoppingCondition) -> Self
pub fn stopping_condition(self, input: StoppingCondition) -> Self
Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.
The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
Sourcepub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self
pub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self
Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.
The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
Sourcepub fn get_stopping_condition(&self) -> &Option<StoppingCondition>
pub fn get_stopping_condition(&self) -> &Option<StoppingCondition>
Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.
The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
Appends an item to Tags.
To override the contents of this collection use set_tags.
A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Sourcepub fn vpc_config(self, input: OptimizationVpcConfig) -> Self
pub fn vpc_config(self, input: OptimizationVpcConfig) -> Self
A VPC in Amazon VPC that your optimized model has access to.
Sourcepub fn set_vpc_config(self, input: Option<OptimizationVpcConfig>) -> Self
pub fn set_vpc_config(self, input: Option<OptimizationVpcConfig>) -> Self
A VPC in Amazon VPC that your optimized model has access to.
Sourcepub fn get_vpc_config(&self) -> &Option<OptimizationVpcConfig>
pub fn get_vpc_config(&self) -> &Option<OptimizationVpcConfig>
A VPC in Amazon VPC that your optimized model has access to.
Trait Implementations§
Source§impl Clone for CreateOptimizationJobFluentBuilder
impl Clone for CreateOptimizationJobFluentBuilder
Source§fn clone(&self) -> CreateOptimizationJobFluentBuilder
fn clone(&self) -> CreateOptimizationJobFluentBuilder
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreAuto Trait Implementations§
impl Freeze for CreateOptimizationJobFluentBuilder
impl !RefUnwindSafe for CreateOptimizationJobFluentBuilder
impl Send for CreateOptimizationJobFluentBuilder
impl Sync for CreateOptimizationJobFluentBuilder
impl Unpin for CreateOptimizationJobFluentBuilder
impl !UnwindSafe for CreateOptimizationJobFluentBuilder
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