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# 启动 Amazon Rekognition Custom Labels 模型
您可以使用控制台或操作开始运行 Amazon Rekognition 自定义标签模型。[StartProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion)
**重要**
您需要为模型运行的小时数和模型在运行时使用的推理单元数付费。有关更多信息，请参阅 [运行经过训练的 Amazon Rekognition Custom Labels 模型](running-model.md)。
启动模型可能需要几分钟才能完成。要检查模型的当前就绪状态，请查看项目或用途的详细信息页面[DescribeProjectVersions](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions)。
模型启动后 [DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels)，您可以使用模型来分析图像。有关更多信息，请参阅 [使用经过训练的模型分析图像](detecting-custom-labels.md)。控制台还提供了调用 `DetectCustomLabels` 的示例代码。
**Topics**
+ [启动 Amazon Rekognition Custom Labels 模型（控制台）](#rm-start-console)
+ [启动 Amazon Rekognition Custom Labels 模型 (SDK)](#rm-start-sdk)
## 启动 Amazon Rekognition Custom Labels 模型（控制台）
按照以下过程通过控制台启动运行 Amazon Rekognition Custom Labels 模型。您可以直接从控制台启动模型，也可以使用控制台提供的 AWS SDK 代码。
**启动模型（控制台）**
1. 打开亚马逊 Rekognition 控制台，网址为[https://console.aws.amazon.com/rekognition/](https://console.aws.amazon.com/rekognition/)。
1. 选择**使用自定义标签**。
1. 选择**开始**。
1. 在左侧导航窗格中，选择**项目**。
1. 在**项目**资源页面上，选择包含要启动的已训练模型的项目。
1. 在**模型**部分中，选择要启动的模型。
1. 选择**使用模型**选项卡。
1. 执行下列操作之一：
------
#### [ Start model using the console ]
在**启动或停止模型**部分中，执行以下操作：
1. 选择要使用的推理单元的数量。有关更多信息，请参阅 [运行经过训练的 Amazon Rekognition Custom Labels 模型](running-model.md)。
1. 选择**启动**。
1. 在**启动模型**对话框中，选择**启动**。
------
#### [ Start model using the AWS SDK ]
在**使用模型**部分中，执行以下操作：
1. 选择 **API 代码**。
1. 选择 **AWS CLI** 或 **Python**。
1. 在**启动模型**中，复制示例代码。
1. 使用示例代码启动模型。有关更多信息，请参阅 [启动 Amazon Rekognition Custom Labels 模型 (SDK)](#rm-start-sdk)。
------
1. 要返回到项目概述页面，请在页面顶部选择项目名称。
1. 在**模型**部分中，检查模型的状态。当模型状态为 **RUNNING** 时，就可以使用模型来分析图像。有关更多信息，请参阅 [使用经过训练的模型分析图像](detecting-custom-labels.md)。
## 启动 Amazon Rekognition Custom Labels 模型 (SDK)
您可以通过调用 [StartProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion)API 并在`ProjectVersionArn`输入参数中传递模型的 Amazon 资源名称 (ARN) 来启动模型。此外，还需要指定要使用的推理单元数量。有关更多信息，请参阅 [运行经过训练的 Amazon Rekognition Custom Labels 模型](running-model.md)。
模型可能需要一段时间才能启动。本主题中的 Python 和 Java 示例使用 waiter 来等待模型启动。waiter 是一种实用程序方法，用于轮询是否发生了特定状态。或者，您可以通过致电来查看当前状态[DescribeProjectVersions](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions)。
**启动模型 (SDK)**
1. 如果您尚未这样做，请安装并配置 AWS CLI 和 AWS SDKs。有关更多信息，请参阅 [步骤 4：设置 AWS CLI 和 AWS SDKs](su-awscli-sdk.md)。
1. 使用以下示例代码启动模型。
------
#### [ CLI ]
将 `project-version-arn` 的值更改为要启动的模型的 ARN。将 `--min-inference-units` 的值更改为要使用的推理单元数。（可选）将 `--max-inference-units` 更改为 Amazon Rekognition Custom Labels 可用于自动扩缩模型的最大推理单元数。
```
aws rekognition start-project-version --project-version-arn model_arn \
--min-inference-units minimum number of units \
--max-inference-units maximum number of units \
--profile custom-labels-access
```
------
#### [ Python ]
提供以下命令行参数：
+ `project_arn`：包含要启动的模型的项目的 ARN。
+ `model_arn`：要启动的模型的 ARN。
+ `min_inference_units`：要使用的推理单元数。
+ （可选）`--max_inference_units`：Amazon Rekognition Custom Labels 可用于自动扩缩模型的最大推理单元数。
```
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Purpose
Shows how to start running an Amazon Lookout for Vision model.
"""
import argparse
import logging
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
def get_model_status(rek_client, project_arn, model_arn):
"""
Gets the current status of an Amazon Rekognition Custom Labels model
:param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
:param project_name: The name of the project that you want to use.
:param model_arn: The name of the model that you want the status for.
:return: The model status
"""
logger.info("Getting status for %s.", model_arn)
# Extract the model version from the model arn.
version_name = (model_arn.split("version/", 1)[1]).rpartition('/')[0]
models = rek_client.describe_project_versions(ProjectArn=project_arn,
VersionNames=[version_name])
for model in models['ProjectVersionDescriptions']:
logger.info("Status: %s", model['StatusMessage'])
return model["Status"]
error_message = f"Model {model_arn} not found."
logger.exception(error_message)
raise Exception(error_message)
def start_model(rek_client, project_arn, model_arn, min_inference_units, max_inference_units=None):
"""
Starts the hosting of an Amazon Rekognition Custom Labels model.
:param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
:param project_name: The name of the project that contains the
model that you want to start hosting.
:param min_inference_units: The number of inference units to use for hosting.
:param max_inference_units: The number of inference units to use for auto-scaling
the model. If not supplied, auto-scaling does not happen.
"""
try:
# Start the model
logger.info(f"Starting model: {model_arn}. Please wait....")
if max_inference_units is None:
rek_client.start_project_version(ProjectVersionArn=model_arn,
MinInferenceUnits=int(min_inference_units))
else:
rek_client.start_project_version(ProjectVersionArn=model_arn,
MinInferenceUnits=int(
min_inference_units),
MaxInferenceUnits=int(max_inference_units))
# Wait for the model to be in the running state
version_name = (model_arn.split("version/", 1)[1]).rpartition('/')[0]
project_version_running_waiter = rek_client.get_waiter(
'project_version_running')
project_version_running_waiter.wait(
ProjectArn=project_arn, VersionNames=[version_name])
# Get the running status
return get_model_status(rek_client, project_arn, model_arn)
except ClientError as err:
logger.exception("Client error: Problem starting model: %s", err)
raise
def add_arguments(parser):
"""
Adds command line arguments to the parser.
:param parser: The command line parser.
"""
parser.add_argument(
"project_arn", help="The ARN of the project that contains that the model you want to start."
)
parser.add_argument(
"model_arn", help="The ARN of the model that you want to start."
)
parser.add_argument(
"min_inference_units", help="The minimum number of inference units to use."
)
parser.add_argument(
"--max_inference_units", help="The maximum number of inference units to use for auto-scaling the model.", required=False
)
def main():
logging.basicConfig(level=logging.INFO,
format="%(levelname)s: %(message)s")
try:
# Get command line arguments.
parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
add_arguments(parser)
args = parser.parse_args()
# Start the model.
session = boto3.Session(profile_name='custom-labels-access')
rekognition_client = session.client("rekognition")
status = start_model(rekognition_client,
args.project_arn, args.model_arn,
args.min_inference_units,
args.max_inference_units)
print(f"Finished starting model: {args.model_arn}")
print(f"Status: {status}")
except ClientError as err:
error_message = f"Client error: Problem starting model: {err}"
logger.exception(error_message)
print(error_message)
except Exception as err:
error_message = f"Problem starting model:{err}"
logger.exception(error_message)
print(error_message)
if __name__ == "__main__":
main()
```
------
#### [ Java V2 ]
提供以下命令行参数：
+ `project_arn`：包含要启动的模型的项目的 ARN。
+ `model_arn`：要启动的模型的 ARN。
+ `min_inference_units`：要使用的推理单元数。
+ （可选）`max_inference_units`：Amazon Rekognition Custom Labels 可用于自动扩缩模型的最大推理单元数。如果不指定值，则不会进行自动扩缩。
```
/*
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
*/
package com.example.rekognition;
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.waiters.WaiterResponse;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DescribeProjectVersionsRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeProjectVersionsResponse;
import software.amazon.awssdk.services.rekognition.model.ProjectVersionDescription;
import software.amazon.awssdk.services.rekognition.model.ProjectVersionStatus;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.StartProjectVersionRequest;
import software.amazon.awssdk.services.rekognition.model.StartProjectVersionResponse;
import software.amazon.awssdk.services.rekognition.waiters.RekognitionWaiter;
import java.util.Optional;
import java.util.logging.Level;
import java.util.logging.Logger;
public class StartModel {
public static final Logger logger = Logger.getLogger(StartModel.class.getName());
public static int findForwardSlash(String modelArn, int n) {
int start = modelArn.indexOf('/');
while (start >= 0 && n > 1) {
start = modelArn.indexOf('/', start + 1);
n -= 1;
}
return start;
}
public static void startMyModel(RekognitionClient rekClient, String projectArn, String modelArn,
Integer minInferenceUnits, Integer maxInferenceUnits
) throws Exception, RekognitionException {
try {
logger.log(Level.INFO, "Starting model: {0}", modelArn);
StartProjectVersionRequest startProjectVersionRequest = null;
if (maxInferenceUnits == null) {
startProjectVersionRequest = StartProjectVersionRequest.builder()
.projectVersionArn(modelArn)
.minInferenceUnits(minInferenceUnits)
.build();
}
else {
startProjectVersionRequest = StartProjectVersionRequest.builder()
.projectVersionArn(modelArn)
.minInferenceUnits(minInferenceUnits)
.maxInferenceUnits(maxInferenceUnits)
.build();
}
StartProjectVersionResponse response = rekClient.startProjectVersion(startProjectVersionRequest);
logger.log(Level.INFO, "Status: {0}", response.statusAsString() );
// Get the model version
int start = findForwardSlash(modelArn, 3) + 1;
int end = findForwardSlash(modelArn, 4);
String versionName = modelArn.substring(start, end);
// wait until model starts
DescribeProjectVersionsRequest describeProjectVersionsRequest = DescribeProjectVersionsRequest.builder()
.versionNames(versionName)
.projectArn(projectArn)
.build();
RekognitionWaiter waiter = rekClient.waiter();
WaiterResponse waiterResponse = waiter
.waitUntilProjectVersionRunning(describeProjectVersionsRequest);
Optional optionalResponse = waiterResponse.matched().response();
DescribeProjectVersionsResponse describeProjectVersionsResponse = optionalResponse.get();
for (ProjectVersionDescription projectVersionDescription : describeProjectVersionsResponse
.projectVersionDescriptions()) {
if(projectVersionDescription.status() == ProjectVersionStatus.RUNNING) {
logger.log(Level.INFO, "Model is running" );
}
else {
String error = "Model training failed: " + projectVersionDescription.statusAsString() + " "
+ projectVersionDescription.statusMessage() + " " + modelArn;
logger.log(Level.SEVERE, error);
throw new Exception(error);
}
}
} catch (RekognitionException e) {
logger.log(Level.SEVERE, "Could not start model: {0}", e.getMessage());
throw e;
}
}
public static void main(String[] args) {
String modelArn = null;
String projectArn = null;
Integer minInferenceUnits = null;
Integer maxInferenceUnits = null;
final String USAGE = "\n" + "Usage: " + " \n\n" + "Where:\n"
+ " project_arn - The ARN of the project that contains the model that you want to start. \n\n"
+ " model_arn - The ARN of the model version that you want to start.\n\n"
+ " min_inference_units - The number of inference units to start the model with.\n\n"
+ " max_inference_units - The maximum number of inference units that Custom Labels can use to "
+ " automatically scale the model. If the value is null, automatic scaling doesn't happen.\n\n";
if (args.length < 3 || args.length >4) {
System.out.println(USAGE);
System.exit(1);
}
projectArn = args[0];
modelArn = args[1];
minInferenceUnits=Integer.parseInt(args[2]);
if (args.length == 4) {
maxInferenceUnits = Integer.parseInt(args[3]);
}
try {
// Get the Rekognition client.
RekognitionClient rekClient = RekognitionClient.builder()
.credentialsProvider(ProfileCredentialsProvider.create("custom-labels-access"))
.region(Region.US_WEST_2)
.build();
// Start the model.
startMyModel(rekClient, projectArn, modelArn, minInferenceUnits, maxInferenceUnits);
System.out.println(String.format("Model started: %s", modelArn));
rekClient.close();
} catch (RekognitionException rekError) {
logger.log(Level.SEVERE, "Rekognition client error: {0}", rekError.getMessage());
System.exit(1);
} catch (Exception rekError) {
logger.log(Level.SEVERE, "Error: {0}", rekError.getMessage());
System.exit(1);
}
}
}
```
------