1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360
// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_transform_job::_create_transform_job_output::CreateTransformJobOutputBuilder;
pub use crate::operation::create_transform_job::_create_transform_job_input::CreateTransformJobInputBuilder;
impl CreateTransformJobInputBuilder {
/// Sends a request with this input using the given client.
pub async fn send_with(
self,
client: &crate::Client,
) -> ::std::result::Result<
crate::operation::create_transform_job::CreateTransformJobOutput,
::aws_smithy_http::result::SdkError<
crate::operation::create_transform_job::CreateTransformJobError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.create_transform_job();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `CreateTransformJob`.
///
/// <p>Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.</p>
/// <p>To perform batch transformations, you create a transform job and use the data that you have readily available.</p>
/// <p>In the request body, you provide the following:</p>
/// <ul>
/// <li> <p> <code>TransformJobName</code> - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.</p> </li>
/// <li> <p> <code>ModelName</code> - Identifies the model to use. <code>ModelName</code> must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html">CreateModel</a>.</p> </li>
/// <li> <p> <code>TransformInput</code> - Describes the dataset to be transformed and the Amazon S3 location where it is stored.</p> </li>
/// <li> <p> <code>TransformOutput</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.</p> </li>
/// <li> <p> <code>TransformResources</code> - Identifies the ML compute instances for the transform job.</p> </li>
/// </ul>
/// <p>For more information about how batch transformation works, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html">Batch Transform</a>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateTransformJobFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::create_transform_job::builders::CreateTransformJobInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl
crate::client::customize::internal::CustomizableSend<
crate::operation::create_transform_job::CreateTransformJobOutput,
crate::operation::create_transform_job::CreateTransformJobError,
> for CreateTransformJobFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<
crate::operation::create_transform_job::CreateTransformJobOutput,
crate::operation::create_transform_job::CreateTransformJobError,
>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl CreateTransformJobFluentBuilder {
/// Creates a new `CreateTransformJob`.
pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
Self {
handle,
inner: ::std::default::Default::default(),
config_override: ::std::option::Option::None,
}
}
/// Access the CreateTransformJob as a reference.
pub fn as_input(&self) -> &crate::operation::create_transform_job::builders::CreateTransformJobInputBuilder {
&self.inner
}
/// 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](aws_smithy_types::retry::RetryConfig), which can be
/// set when configuring the client.
pub async fn send(
self,
) -> ::std::result::Result<
crate::operation::create_transform_job::CreateTransformJobOutput,
::aws_smithy_http::result::SdkError<
crate::operation::create_transform_job::CreateTransformJobError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let input = self.inner.build().map_err(::aws_smithy_http::result::SdkError::construction_failure)?;
let runtime_plugins = crate::operation::create_transform_job::CreateTransformJob::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::create_transform_job::CreateTransformJob::orchestrate(&runtime_plugins, input).await
}
/// Consumes this builder, creating a customizable operation that can be modified before being
/// sent.
// TODO(enableNewSmithyRuntimeCleanup): Remove `async` and `Result` once we switch to orchestrator
pub async fn customize(
self,
) -> ::std::result::Result<
crate::client::customize::orchestrator::CustomizableOperation<
crate::operation::create_transform_job::CreateTransformJobOutput,
crate::operation::create_transform_job::CreateTransformJobError,
Self,
>,
::aws_smithy_http::result::SdkError<crate::operation::create_transform_job::CreateTransformJobError>,
> {
::std::result::Result::Ok(crate::client::customize::orchestrator::CustomizableOperation::new(self))
}
pub(crate) fn config_override(mut self, config_override: impl Into<crate::config::Builder>) -> Self {
self.set_config_override(Some(config_override.into()));
self
}
pub(crate) fn set_config_override(&mut self, config_override: Option<crate::config::Builder>) -> &mut Self {
self.config_override = config_override;
self
}
/// <p>The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. </p>
pub fn transform_job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.transform_job_name(input.into());
self
}
/// <p>The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. </p>
pub fn set_transform_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_transform_job_name(input);
self
}
/// <p>The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. </p>
pub fn get_transform_job_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_transform_job_name()
}
/// <p>The name of the model that you want to use for the transform job. <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.</p>
pub fn model_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.model_name(input.into());
self
}
/// <p>The name of the model that you want to use for the transform job. <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.</p>
pub fn set_model_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_model_name(input);
self
}
/// <p>The name of the model that you want to use for the transform job. <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.</p>
pub fn get_model_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_model_name()
}
/// <p>The maximum number of parallel requests that can be sent to each instance in a transform job. If <code>MaxConcurrentTransforms</code> is set to <code>0</code> or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is <code>1</code>. For more information on execution-parameters, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests">How Containers Serve Requests</a>. For built-in algorithms, you don't need to set a value for <code>MaxConcurrentTransforms</code>.</p>
pub fn max_concurrent_transforms(mut self, input: i32) -> Self {
self.inner = self.inner.max_concurrent_transforms(input);
self
}
/// <p>The maximum number of parallel requests that can be sent to each instance in a transform job. If <code>MaxConcurrentTransforms</code> is set to <code>0</code> or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is <code>1</code>. For more information on execution-parameters, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests">How Containers Serve Requests</a>. For built-in algorithms, you don't need to set a value for <code>MaxConcurrentTransforms</code>.</p>
pub fn set_max_concurrent_transforms(mut self, input: ::std::option::Option<i32>) -> Self {
self.inner = self.inner.set_max_concurrent_transforms(input);
self
}
/// <p>The maximum number of parallel requests that can be sent to each instance in a transform job. If <code>MaxConcurrentTransforms</code> is set to <code>0</code> or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is <code>1</code>. For more information on execution-parameters, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests">How Containers Serve Requests</a>. For built-in algorithms, you don't need to set a value for <code>MaxConcurrentTransforms</code>.</p>
pub fn get_max_concurrent_transforms(&self) -> &::std::option::Option<i32> {
self.inner.get_max_concurrent_transforms()
}
/// <p>Configures the timeout and maximum number of retries for processing a transform job invocation.</p>
pub fn model_client_config(mut self, input: crate::types::ModelClientConfig) -> Self {
self.inner = self.inner.model_client_config(input);
self
}
/// <p>Configures the timeout and maximum number of retries for processing a transform job invocation.</p>
pub fn set_model_client_config(mut self, input: ::std::option::Option<crate::types::ModelClientConfig>) -> Self {
self.inner = self.inner.set_model_client_config(input);
self
}
/// <p>Configures the timeout and maximum number of retries for processing a transform job invocation.</p>
pub fn get_model_client_config(&self) -> &::std::option::Option<crate::types::ModelClientConfig> {
self.inner.get_model_client_config()
}
/// <p>The maximum allowed size of the payload, in MB. A <i>payload</i> is the data portion of a record (without metadata). The value in <code>MaxPayloadInMB</code> must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is <code>6</code> MB. </p>
/// <p>The value of <code>MaxPayloadInMB</code> cannot be greater than 100 MB. If you specify the <code>MaxConcurrentTransforms</code> parameter, the value of <code>(MaxConcurrentTransforms * MaxPayloadInMB)</code> also cannot exceed 100 MB.</p>
/// <p>For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to <code>0</code>. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.</p>
pub fn max_payload_in_mb(mut self, input: i32) -> Self {
self.inner = self.inner.max_payload_in_mb(input);
self
}
/// <p>The maximum allowed size of the payload, in MB. A <i>payload</i> is the data portion of a record (without metadata). The value in <code>MaxPayloadInMB</code> must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is <code>6</code> MB. </p>
/// <p>The value of <code>MaxPayloadInMB</code> cannot be greater than 100 MB. If you specify the <code>MaxConcurrentTransforms</code> parameter, the value of <code>(MaxConcurrentTransforms * MaxPayloadInMB)</code> also cannot exceed 100 MB.</p>
/// <p>For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to <code>0</code>. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.</p>
pub fn set_max_payload_in_mb(mut self, input: ::std::option::Option<i32>) -> Self {
self.inner = self.inner.set_max_payload_in_mb(input);
self
}
/// <p>The maximum allowed size of the payload, in MB. A <i>payload</i> is the data portion of a record (without metadata). The value in <code>MaxPayloadInMB</code> must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is <code>6</code> MB. </p>
/// <p>The value of <code>MaxPayloadInMB</code> cannot be greater than 100 MB. If you specify the <code>MaxConcurrentTransforms</code> parameter, the value of <code>(MaxConcurrentTransforms * MaxPayloadInMB)</code> also cannot exceed 100 MB.</p>
/// <p>For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to <code>0</code>. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.</p>
pub fn get_max_payload_in_mb(&self) -> &::std::option::Option<i32> {
self.inner.get_max_payload_in_mb()
}
/// <p>Specifies the number of records to include in a mini-batch for an HTTP inference request. A <i>record</i> <i></i> is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. </p>
/// <p>To enable the batch strategy, you must set the <code>SplitType</code> property to <code>Line</code>, <code>RecordIO</code>, or <code>TFRecord</code>.</p>
/// <p>To use only one record when making an HTTP invocation request to a container, set <code>BatchStrategy</code> to <code>SingleRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
/// <p>To fit as many records in a mini-batch as can fit within the <code>MaxPayloadInMB</code> limit, set <code>BatchStrategy</code> to <code>MultiRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
pub fn batch_strategy(mut self, input: crate::types::BatchStrategy) -> Self {
self.inner = self.inner.batch_strategy(input);
self
}
/// <p>Specifies the number of records to include in a mini-batch for an HTTP inference request. A <i>record</i> <i></i> is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. </p>
/// <p>To enable the batch strategy, you must set the <code>SplitType</code> property to <code>Line</code>, <code>RecordIO</code>, or <code>TFRecord</code>.</p>
/// <p>To use only one record when making an HTTP invocation request to a container, set <code>BatchStrategy</code> to <code>SingleRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
/// <p>To fit as many records in a mini-batch as can fit within the <code>MaxPayloadInMB</code> limit, set <code>BatchStrategy</code> to <code>MultiRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
pub fn set_batch_strategy(mut self, input: ::std::option::Option<crate::types::BatchStrategy>) -> Self {
self.inner = self.inner.set_batch_strategy(input);
self
}
/// <p>Specifies the number of records to include in a mini-batch for an HTTP inference request. A <i>record</i> <i></i> is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. </p>
/// <p>To enable the batch strategy, you must set the <code>SplitType</code> property to <code>Line</code>, <code>RecordIO</code>, or <code>TFRecord</code>.</p>
/// <p>To use only one record when making an HTTP invocation request to a container, set <code>BatchStrategy</code> to <code>SingleRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
/// <p>To fit as many records in a mini-batch as can fit within the <code>MaxPayloadInMB</code> limit, set <code>BatchStrategy</code> to <code>MultiRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
pub fn get_batch_strategy(&self) -> &::std::option::Option<crate::types::BatchStrategy> {
self.inner.get_batch_strategy()
}
/// Adds a key-value pair to `Environment`.
///
/// To override the contents of this collection use [`set_environment`](Self::set_environment).
///
/// <p>The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.</p>
pub fn environment(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.environment(k.into(), v.into());
self
}
/// <p>The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.</p>
pub fn set_environment(
mut self,
input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
) -> Self {
self.inner = self.inner.set_environment(input);
self
}
/// <p>The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.</p>
pub fn get_environment(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
self.inner.get_environment()
}
/// <p>Describes the input source and the way the transform job consumes it.</p>
pub fn transform_input(mut self, input: crate::types::TransformInput) -> Self {
self.inner = self.inner.transform_input(input);
self
}
/// <p>Describes the input source and the way the transform job consumes it.</p>
pub fn set_transform_input(mut self, input: ::std::option::Option<crate::types::TransformInput>) -> Self {
self.inner = self.inner.set_transform_input(input);
self
}
/// <p>Describes the input source and the way the transform job consumes it.</p>
pub fn get_transform_input(&self) -> &::std::option::Option<crate::types::TransformInput> {
self.inner.get_transform_input()
}
/// <p>Describes the results of the transform job.</p>
pub fn transform_output(mut self, input: crate::types::TransformOutput) -> Self {
self.inner = self.inner.transform_output(input);
self
}
/// <p>Describes the results of the transform job.</p>
pub fn set_transform_output(mut self, input: ::std::option::Option<crate::types::TransformOutput>) -> Self {
self.inner = self.inner.set_transform_output(input);
self
}
/// <p>Describes the results of the transform job.</p>
pub fn get_transform_output(&self) -> &::std::option::Option<crate::types::TransformOutput> {
self.inner.get_transform_output()
}
/// <p>Configuration to control how SageMaker captures inference data.</p>
pub fn data_capture_config(mut self, input: crate::types::BatchDataCaptureConfig) -> Self {
self.inner = self.inner.data_capture_config(input);
self
}
/// <p>Configuration to control how SageMaker captures inference data.</p>
pub fn set_data_capture_config(mut self, input: ::std::option::Option<crate::types::BatchDataCaptureConfig>) -> Self {
self.inner = self.inner.set_data_capture_config(input);
self
}
/// <p>Configuration to control how SageMaker captures inference data.</p>
pub fn get_data_capture_config(&self) -> &::std::option::Option<crate::types::BatchDataCaptureConfig> {
self.inner.get_data_capture_config()
}
/// <p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>
pub fn transform_resources(mut self, input: crate::types::TransformResources) -> Self {
self.inner = self.inner.transform_resources(input);
self
}
/// <p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>
pub fn set_transform_resources(mut self, input: ::std::option::Option<crate::types::TransformResources>) -> Self {
self.inner = self.inner.set_transform_resources(input);
self
}
/// <p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>
pub fn get_transform_resources(&self) -> &::std::option::Option<crate::types::TransformResources> {
self.inner.get_transform_resources()
}
/// <p>The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html">Associate Prediction Results with their Corresponding Input Records</a>.</p>
pub fn data_processing(mut self, input: crate::types::DataProcessing) -> Self {
self.inner = self.inner.data_processing(input);
self
}
/// <p>The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html">Associate Prediction Results with their Corresponding Input Records</a>.</p>
pub fn set_data_processing(mut self, input: ::std::option::Option<crate::types::DataProcessing>) -> Self {
self.inner = self.inner.set_data_processing(input);
self
}
/// <p>The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html">Associate Prediction Results with their Corresponding Input Records</a>.</p>
pub fn get_data_processing(&self) -> &::std::option::Option<crate::types::DataProcessing> {
self.inner.get_data_processing()
}
/// Appends an item to `Tags`.
///
/// To override the contents of this collection use [`set_tags`](Self::set_tags).
///
/// <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management User Guide</i>.</p>
pub fn tags(mut self, input: crate::types::Tag) -> Self {
self.inner = self.inner.tags(input);
self
}
/// <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management User Guide</i>.</p>
pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
self.inner = self.inner.set_tags(input);
self
}
/// <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management User Guide</i>.</p>
pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
self.inner.get_tags()
}
/// <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:</p>
/// <ul>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html">CreateProcessingJob</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html">CreateTransformJob</a> </p> </li>
/// </ul>
pub fn experiment_config(mut self, input: crate::types::ExperimentConfig) -> Self {
self.inner = self.inner.experiment_config(input);
self
}
/// <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:</p>
/// <ul>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html">CreateProcessingJob</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html">CreateTransformJob</a> </p> </li>
/// </ul>
pub fn set_experiment_config(mut self, input: ::std::option::Option<crate::types::ExperimentConfig>) -> Self {
self.inner = self.inner.set_experiment_config(input);
self
}
/// <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:</p>
/// <ul>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html">CreateProcessingJob</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a> </p> </li>
/// <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html">CreateTransformJob</a> </p> </li>
/// </ul>
pub fn get_experiment_config(&self) -> &::std::option::Option<crate::types::ExperimentConfig> {
self.inner.get_experiment_config()
}
}