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

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::start_model::_start_model_output::StartModelOutputBuilder;

pub use crate::operation::start_model::_start_model_input::StartModelInputBuilder;

impl StartModelInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::start_model::StartModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::start_model::StartModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.start_model();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `StartModel`.
///
/// <p>Starts the running of the version of an Amazon Lookout for Vision model. Starting a model takes a while to complete. To check the current state of the model, use <code>DescribeModel</code>.</p>
/// <p>A model is ready to use when its status is <code>HOSTED</code>.</p>
/// <p>Once the model is running, you can detect custom labels in new images by calling <code>DetectAnomalies</code>.</p> <note>
/// <p>You are charged for the amount of time that the model is running. To stop a running model, call <code>StopModel</code>.</p>
/// </note>
/// <p>This operation requires permissions to perform the <code>lookoutvision:StartModel</code> operation.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct StartModelFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::start_model::builders::StartModelInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::start_model::StartModelOutput,
        crate::operation::start_model::StartModelError,
    > for StartModelFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::start_model::StartModelOutput,
            crate::operation::start_model::StartModelError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl StartModelFluentBuilder {
    /// Creates a new `StartModel`.
    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 StartModel as a reference.
    pub fn as_input(&self) -> &crate::operation::start_model::builders::StartModelInputBuilder {
        &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::start_model::StartModelOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::start_model::StartModelError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::start_model::StartModel::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::start_model::StartModel::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::start_model::StartModelOutput,
        crate::operation::start_model::StartModelError,
        Self,
    > {
        crate::client::customize::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 project that contains the model that you want to start.</p>
    pub fn project_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.project_name(input.into());
        self
    }
    /// <p>The name of the project that contains the model that you want to start.</p>
    pub fn set_project_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_project_name(input);
        self
    }
    /// <p>The name of the project that contains the model that you want to start.</p>
    pub fn get_project_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_project_name()
    }
    /// <p>The version of the model that you want to start.</p>
    pub fn model_version(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.model_version(input.into());
        self
    }
    /// <p>The version of the model that you want to start.</p>
    pub fn set_model_version(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_model_version(input);
        self
    }
    /// <p>The version of the model that you want to start.</p>
    pub fn get_model_version(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_model_version()
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub fn min_inference_units(mut self, input: i32) -> Self {
        self.inner = self.inner.min_inference_units(input);
        self
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub fn set_min_inference_units(mut self, input: ::std::option::Option<i32>) -> Self {
        self.inner = self.inner.set_min_inference_units(input);
        self
    }
    /// <p>The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use. </p>
    pub fn get_min_inference_units(&self) -> &::std::option::Option<i32> {
        self.inner.get_min_inference_units()
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn client_token(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.client_token(input.into());
        self
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn set_client_token(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_client_token(input);
        self
    }
    /// <p>ClientToken is an idempotency token that ensures a call to <code>StartModel</code> completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from <code>StartModel</code>. In this case, safely retry your call to <code>StartModel</code> by using the same <code>ClientToken</code> parameter value. </p>
    /// <p>If you don't supply a value for <code>ClientToken</code>, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases. </p>
    /// <p>An error occurs if the other input parameters are not the same as in the first request. Using a different value for <code>ClientToken</code> is considered a new call to <code>StartModel</code>. An idempotency token is active for 8 hours. </p>
    pub fn get_client_token(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_client_token()
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn max_inference_units(mut self, input: i32) -> Self {
        self.inner = self.inner.max_inference_units(input);
        self
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn set_max_inference_units(mut self, input: ::std::option::Option<i32>) -> Self {
        self.inner = self.inner.set_max_inference_units(input);
        self
    }
    /// <p>The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.</p>
    pub fn get_max_inference_units(&self) -> &::std::option::Option<i32> {
        self.inner.get_max_inference_units()
    }
}