aws_sdk_sagemaker/operation/create_transform_job/builders.rs
1// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
2pub use crate::operation::create_transform_job::_create_transform_job_output::CreateTransformJobOutputBuilder;
3
4pub use crate::operation::create_transform_job::_create_transform_job_input::CreateTransformJobInputBuilder;
5
6impl crate::operation::create_transform_job::builders::CreateTransformJobInputBuilder {
7 /// Sends a request with this input using the given client.
8 pub async fn send_with(
9 self,
10 client: &crate::Client,
11 ) -> ::std::result::Result<
12 crate::operation::create_transform_job::CreateTransformJobOutput,
13 ::aws_smithy_runtime_api::client::result::SdkError<
14 crate::operation::create_transform_job::CreateTransformJobError,
15 ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
16 >,
17 > {
18 let mut fluent_builder = client.create_transform_job();
19 fluent_builder.inner = self;
20 fluent_builder.send().await
21 }
22}
23/// Fluent builder constructing a request to `CreateTransformJob`.
24///
25/// <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>
26/// <p>To perform batch transformations, you create a transform job and use the data that you have readily available.</p>
27/// <p>In the request body, you provide the following:</p>
28/// <ul>
29/// <li>
30/// <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>
31/// <li>
32/// <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>
33/// <li>
34/// <p><code>TransformInput</code> - Describes the dataset to be transformed and the Amazon S3 location where it is stored.</p></li>
35/// <li>
36/// <p><code>TransformOutput</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.</p></li>
37/// <li>
38/// <p><code>TransformResources</code> - Identifies the ML compute instances and AMI image versions for the transform job.</p></li>
39/// </ul>
40/// <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>
41#[derive(::std::clone::Clone, ::std::fmt::Debug)]
42pub struct CreateTransformJobFluentBuilder {
43 handle: ::std::sync::Arc<crate::client::Handle>,
44 inner: crate::operation::create_transform_job::builders::CreateTransformJobInputBuilder,
45 config_override: ::std::option::Option<crate::config::Builder>,
46}
47impl
48 crate::client::customize::internal::CustomizableSend<
49 crate::operation::create_transform_job::CreateTransformJobOutput,
50 crate::operation::create_transform_job::CreateTransformJobError,
51 > for CreateTransformJobFluentBuilder
52{
53 fn send(
54 self,
55 config_override: crate::config::Builder,
56 ) -> crate::client::customize::internal::BoxFuture<
57 crate::client::customize::internal::SendResult<
58 crate::operation::create_transform_job::CreateTransformJobOutput,
59 crate::operation::create_transform_job::CreateTransformJobError,
60 >,
61 > {
62 ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
63 }
64}
65impl CreateTransformJobFluentBuilder {
66 /// Creates a new `CreateTransformJobFluentBuilder`.
67 pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
68 Self {
69 handle,
70 inner: ::std::default::Default::default(),
71 config_override: ::std::option::Option::None,
72 }
73 }
74 /// Access the CreateTransformJob as a reference.
75 pub fn as_input(&self) -> &crate::operation::create_transform_job::builders::CreateTransformJobInputBuilder {
76 &self.inner
77 }
78 /// Sends the request and returns the response.
79 ///
80 /// If an error occurs, an `SdkError` will be returned with additional details that
81 /// can be matched against.
82 ///
83 /// By default, any retryable failures will be retried twice. Retry behavior
84 /// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
85 /// set when configuring the client.
86 pub async fn send(
87 self,
88 ) -> ::std::result::Result<
89 crate::operation::create_transform_job::CreateTransformJobOutput,
90 ::aws_smithy_runtime_api::client::result::SdkError<
91 crate::operation::create_transform_job::CreateTransformJobError,
92 ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
93 >,
94 > {
95 let input = self
96 .inner
97 .build()
98 .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
99 let runtime_plugins = crate::operation::create_transform_job::CreateTransformJob::operation_runtime_plugins(
100 self.handle.runtime_plugins.clone(),
101 &self.handle.conf,
102 self.config_override,
103 );
104 crate::operation::create_transform_job::CreateTransformJob::orchestrate(&runtime_plugins, input).await
105 }
106
107 /// Consumes this builder, creating a customizable operation that can be modified before being sent.
108 pub fn customize(
109 self,
110 ) -> crate::client::customize::CustomizableOperation<
111 crate::operation::create_transform_job::CreateTransformJobOutput,
112 crate::operation::create_transform_job::CreateTransformJobError,
113 Self,
114 > {
115 crate::client::customize::CustomizableOperation::new(self)
116 }
117 pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
118 self.set_config_override(::std::option::Option::Some(config_override.into()));
119 self
120 }
121
122 pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
123 self.config_override = config_override;
124 self
125 }
126 /// <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>
127 pub fn transform_job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
128 self.inner = self.inner.transform_job_name(input.into());
129 self
130 }
131 /// <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>
132 pub fn set_transform_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
133 self.inner = self.inner.set_transform_job_name(input);
134 self
135 }
136 /// <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>
137 pub fn get_transform_job_name(&self) -> &::std::option::Option<::std::string::String> {
138 self.inner.get_transform_job_name()
139 }
140 /// <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>
141 pub fn model_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
142 self.inner = self.inner.model_name(input.into());
143 self
144 }
145 /// <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>
146 pub fn set_model_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
147 self.inner = self.inner.set_model_name(input);
148 self
149 }
150 /// <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>
151 pub fn get_model_name(&self) -> &::std::option::Option<::std::string::String> {
152 self.inner.get_model_name()
153 }
154 /// <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>
155 pub fn max_concurrent_transforms(mut self, input: i32) -> Self {
156 self.inner = self.inner.max_concurrent_transforms(input);
157 self
158 }
159 /// <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>
160 pub fn set_max_concurrent_transforms(mut self, input: ::std::option::Option<i32>) -> Self {
161 self.inner = self.inner.set_max_concurrent_transforms(input);
162 self
163 }
164 /// <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>
165 pub fn get_max_concurrent_transforms(&self) -> &::std::option::Option<i32> {
166 self.inner.get_max_concurrent_transforms()
167 }
168 /// <p>Configures the timeout and maximum number of retries for processing a transform job invocation.</p>
169 pub fn model_client_config(mut self, input: crate::types::ModelClientConfig) -> Self {
170 self.inner = self.inner.model_client_config(input);
171 self
172 }
173 /// <p>Configures the timeout and maximum number of retries for processing a transform job invocation.</p>
174 pub fn set_model_client_config(mut self, input: ::std::option::Option<crate::types::ModelClientConfig>) -> Self {
175 self.inner = self.inner.set_model_client_config(input);
176 self
177 }
178 /// <p>Configures the timeout and maximum number of retries for processing a transform job invocation.</p>
179 pub fn get_model_client_config(&self) -> &::std::option::Option<crate::types::ModelClientConfig> {
180 self.inner.get_model_client_config()
181 }
182 /// <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>
183 /// <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>
184 /// <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>
185 pub fn max_payload_in_mb(mut self, input: i32) -> Self {
186 self.inner = self.inner.max_payload_in_mb(input);
187 self
188 }
189 /// <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>
190 /// <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>
191 /// <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>
192 pub fn set_max_payload_in_mb(mut self, input: ::std::option::Option<i32>) -> Self {
193 self.inner = self.inner.set_max_payload_in_mb(input);
194 self
195 }
196 /// <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>
197 /// <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>
198 /// <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>
199 pub fn get_max_payload_in_mb(&self) -> &::std::option::Option<i32> {
200 self.inner.get_max_payload_in_mb()
201 }
202 /// <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>
203 /// <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>
204 /// <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>
205 /// <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>
206 pub fn batch_strategy(mut self, input: crate::types::BatchStrategy) -> Self {
207 self.inner = self.inner.batch_strategy(input);
208 self
209 }
210 /// <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>
211 /// <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>
212 /// <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>
213 /// <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>
214 pub fn set_batch_strategy(mut self, input: ::std::option::Option<crate::types::BatchStrategy>) -> Self {
215 self.inner = self.inner.set_batch_strategy(input);
216 self
217 }
218 /// <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>
219 /// <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>
220 /// <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>
221 /// <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>
222 pub fn get_batch_strategy(&self) -> &::std::option::Option<crate::types::BatchStrategy> {
223 self.inner.get_batch_strategy()
224 }
225 ///
226 /// Adds a key-value pair to `Environment`.
227 ///
228 /// To override the contents of this collection use [`set_environment`](Self::set_environment).
229 ///
230 /// <p>The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.</p>
231 pub fn environment(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
232 self.inner = self.inner.environment(k.into(), v.into());
233 self
234 }
235 /// <p>The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.</p>
236 pub fn set_environment(
237 mut self,
238 input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
239 ) -> Self {
240 self.inner = self.inner.set_environment(input);
241 self
242 }
243 /// <p>The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.</p>
244 pub fn get_environment(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
245 self.inner.get_environment()
246 }
247 /// <p>Describes the input source and the way the transform job consumes it.</p>
248 pub fn transform_input(mut self, input: crate::types::TransformInput) -> Self {
249 self.inner = self.inner.transform_input(input);
250 self
251 }
252 /// <p>Describes the input source and the way the transform job consumes it.</p>
253 pub fn set_transform_input(mut self, input: ::std::option::Option<crate::types::TransformInput>) -> Self {
254 self.inner = self.inner.set_transform_input(input);
255 self
256 }
257 /// <p>Describes the input source and the way the transform job consumes it.</p>
258 pub fn get_transform_input(&self) -> &::std::option::Option<crate::types::TransformInput> {
259 self.inner.get_transform_input()
260 }
261 /// <p>Describes the results of the transform job.</p>
262 pub fn transform_output(mut self, input: crate::types::TransformOutput) -> Self {
263 self.inner = self.inner.transform_output(input);
264 self
265 }
266 /// <p>Describes the results of the transform job.</p>
267 pub fn set_transform_output(mut self, input: ::std::option::Option<crate::types::TransformOutput>) -> Self {
268 self.inner = self.inner.set_transform_output(input);
269 self
270 }
271 /// <p>Describes the results of the transform job.</p>
272 pub fn get_transform_output(&self) -> &::std::option::Option<crate::types::TransformOutput> {
273 self.inner.get_transform_output()
274 }
275 /// <p>Configuration to control how SageMaker captures inference data.</p>
276 pub fn data_capture_config(mut self, input: crate::types::BatchDataCaptureConfig) -> Self {
277 self.inner = self.inner.data_capture_config(input);
278 self
279 }
280 /// <p>Configuration to control how SageMaker captures inference data.</p>
281 pub fn set_data_capture_config(mut self, input: ::std::option::Option<crate::types::BatchDataCaptureConfig>) -> Self {
282 self.inner = self.inner.set_data_capture_config(input);
283 self
284 }
285 /// <p>Configuration to control how SageMaker captures inference data.</p>
286 pub fn get_data_capture_config(&self) -> &::std::option::Option<crate::types::BatchDataCaptureConfig> {
287 self.inner.get_data_capture_config()
288 }
289 /// <p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>
290 pub fn transform_resources(mut self, input: crate::types::TransformResources) -> Self {
291 self.inner = self.inner.transform_resources(input);
292 self
293 }
294 /// <p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>
295 pub fn set_transform_resources(mut self, input: ::std::option::Option<crate::types::TransformResources>) -> Self {
296 self.inner = self.inner.set_transform_resources(input);
297 self
298 }
299 /// <p>Describes the resources, including ML instance types and ML instance count, to use for the transform job.</p>
300 pub fn get_transform_resources(&self) -> &::std::option::Option<crate::types::TransformResources> {
301 self.inner.get_transform_resources()
302 }
303 /// <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>
304 pub fn data_processing(mut self, input: crate::types::DataProcessing) -> Self {
305 self.inner = self.inner.data_processing(input);
306 self
307 }
308 /// <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>
309 pub fn set_data_processing(mut self, input: ::std::option::Option<crate::types::DataProcessing>) -> Self {
310 self.inner = self.inner.set_data_processing(input);
311 self
312 }
313 /// <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>
314 pub fn get_data_processing(&self) -> &::std::option::Option<crate::types::DataProcessing> {
315 self.inner.get_data_processing()
316 }
317 ///
318 /// Appends an item to `Tags`.
319 ///
320 /// To override the contents of this collection use [`set_tags`](Self::set_tags).
321 ///
322 /// <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>
323 pub fn tags(mut self, input: crate::types::Tag) -> Self {
324 self.inner = self.inner.tags(input);
325 self
326 }
327 /// <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>
328 pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
329 self.inner = self.inner.set_tags(input);
330 self
331 }
332 /// <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>
333 pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
334 self.inner.get_tags()
335 }
336 /// <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:</p>
337 /// <ul>
338 /// <li>
339 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html">CreateProcessingJob</a></p></li>
340 /// <li>
341 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a></p></li>
342 /// <li>
343 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html">CreateTransformJob</a></p></li>
344 /// </ul>
345 pub fn experiment_config(mut self, input: crate::types::ExperimentConfig) -> Self {
346 self.inner = self.inner.experiment_config(input);
347 self
348 }
349 /// <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:</p>
350 /// <ul>
351 /// <li>
352 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html">CreateProcessingJob</a></p></li>
353 /// <li>
354 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a></p></li>
355 /// <li>
356 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html">CreateTransformJob</a></p></li>
357 /// </ul>
358 pub fn set_experiment_config(mut self, input: ::std::option::Option<crate::types::ExperimentConfig>) -> Self {
359 self.inner = self.inner.set_experiment_config(input);
360 self
361 }
362 /// <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:</p>
363 /// <ul>
364 /// <li>
365 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html">CreateProcessingJob</a></p></li>
366 /// <li>
367 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html">CreateTrainingJob</a></p></li>
368 /// <li>
369 /// <p><a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html">CreateTransformJob</a></p></li>
370 /// </ul>
371 pub fn get_experiment_config(&self) -> &::std::option::Option<crate::types::ExperimentConfig> {
372 self.inner.get_experiment_config()
373 }
374}