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启动您的 Amazon Lookout for Vision 模型
在使用 Amazon Lookout for Vision 模型进行异常检测之前，必须先启动该模型。您可以通过调用 StartModel API 并传递以下项来启动模型：
ProjectName：包含要启动的模型的项目的名称。
ModelVersion：要启动的模型版本。
MinInferenceUnits：推理单位的最小数量。有关更多信息，请参阅 推理单位。
（可选）MaxInferenceUnits：Amazon Lookout for Vision 可用来自动扩缩模型的最大推理单位数量。有关更多信息，请参阅 自动扩缩推理单位数量。
Amazon Lookout for Vision 控制台提供了示例代码，可用于启动和停止模型。
启动您的模型（控制台）
Amazon Lookout for Vision 控制台提供了 AWS CLI 命令，可用于启动模型。模型启动后，您便可以开始检测图像中的异常。有关更多信息，请参阅 检测图像中的异常。
启动模型（控制台）
-
安装并配置 AWS CLI 和 AWS SDK（如果尚未如此）。有关更多信息，请参阅 步骤 4：设置 AWS CLI 和 AWS 软件开发工具包。
打开 Amazon Lookout for Vision 控制台，网址为 https://console.aws.amazon.com/lookoutvision/。
选择开始使用。
在左侧导航窗格中，选择项目。
在项目资源页面上，选择包含要启动的已训练模型的项目。
在模型部分，选择要启动的模型。
在模型的详细信息页面上，选择使用模型，然后选择将 API 集成到云。
在 Amazon CLI 命令下，复制用于调用 start-model 的 AWS CLI 命令。
在命令提示符处，输入您在上一步中复制的 start-model 命令。如果您使用 lookoutvision 配置文件来获取凭证，请添加 --profile lookoutvision-access 参数。
在控制台中，选择左侧导航页面中的模型。
查看状态列以了解模型的当前状态，当状态为已托管时，您便可以使用模型来检测图像中的异常。有关更多信息，请参阅 检测图像中的异常。
启动您的 Amazon Lookout for Vision 模型 (SDK)
您可以通过调用 StartModel 操作来启动模型。
模型可能需要一段时间才能启动。您可以通过调用 DescribeModel 来检查当前状态。有关更多信息，请参阅 查看您的模型。
启动您的模型 (SDK)
-
安装并配置 AWS CLI 和 AWS SDK（如果尚未如此）。有关更多信息，请参阅 步骤 4：设置 AWS CLI 和 AWS 软件开发工具包。
使用以下示例代码启动模型。
- CLI
-
更改以下值：
project-name 更改为包含要启动的模型的项目的名称。
model-version 更改为要启动的模型版本。
--min-inference-units 更改为要使用的推理单位数。
（可选）--max-inference-units 更改为 Amazon Lookout for Vision 可用来自动扩缩模型的最大推理单位数量。
aws lookoutvision start-model --project-name "project name"\
--model-version model version\
--min-inference-units minimum number of units\
--max-inference-units max number of units \
--profile lookoutvision-access
- Python
-
此代码取自 AWS 文档 SDK 示例 GitHub 存储库。请在此处查看完整示例。
@staticmethod
def start_model(
lookoutvision_client, project_name, model_version, min_inference_units, max_inference_units = None):
"""
Starts the hosting of a Lookout for Vision model.
:param lookoutvision_client: A Boto3 Lookout for Vision client.
:param project_name: The name of the project that contains the version of the
model that you want to start hosting.
:param model_version: The version of 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: (Optional) The maximum number of inference units that
Lookout for Vision can use to automatically scale the model.
"""
try:
logger.info(
"Starting model version %s for project %s", model_version, project_name)
if max_inference_units is None:
lookoutvision_client.start_model(
ProjectName = project_name,
ModelVersion = model_version,
MinInferenceUnits = min_inference_units)
else:
lookoutvision_client.start_model(
ProjectName = project_name,
ModelVersion = model_version,
MinInferenceUnits = min_inference_units,
MaxInferenceUnits = max_inference_units)
print("Starting hosting...")
status = ""
finished = False
# Wait until hosted or failed.
while finished is False:
model_description = lookoutvision_client.describe_model(
ProjectName=project_name, ModelVersion=model_version)
status = model_description["ModelDescription"]["Status"]
if status == "STARTING_HOSTING":
logger.info("Host starting in progress...")
time.sleep(10)
continue
if status == "HOSTED":
logger.info("Model is hosted and ready for use.")
finished = True
continue
logger.info("Model hosting failed and the model can't be used.")
finished = True
if status != "HOSTED":
logger.error("Error hosting model: %s", status)
raise Exception(f"Error hosting model: {status}")
except ClientError:
logger.exception("Couldn't host model.")
raise
- Java V2
-
此代码取自 AWS 文档 SDK 示例 GitHub 存储库。请在此处查看完整示例。
/**
* Starts hosting an Amazon Lookout for Vision model. Returns when the model has
* started or if hosting fails. You are charged for the amount of time that a
* model is hosted. To stop hosting a model, use the StopModel operation.
*
* @param lfvClient An Amazon Lookout for Vision client.
* @param projectName The name of the project that contains the model that you
* want to host.
* @modelVersion The version of the model that you want to host.
* @minInferenceUnits The number of inference units to use for hosting.
* @maxInferenceUnits The maximum number of inference units that Lookout for
* Vision can use for automatically scaling the model. If the
* value is null, automatic scaling doesn't happen.
* @return ModelDescription The description of the model, which includes the
* model hosting status.
*/
public static ModelDescription startModel(LookoutVisionClient lfvClient, String projectName, String modelVersion,
Integer minInferenceUnits, Integer maxInferenceUnits) throws LookoutVisionException, InterruptedException {
logger.log(Level.INFO, "Starting Model version {0} for project {1}.",
new Object[] { modelVersion, projectName });
StartModelRequest startModelRequest = null;
if (maxInferenceUnits == null) {
startModelRequest = StartModelRequest.builder().projectName(projectName).modelVersion(modelVersion)
.minInferenceUnits(minInferenceUnits).build();
} else {
startModelRequest = StartModelRequest.builder().projectName(projectName).modelVersion(modelVersion)
.minInferenceUnits(minInferenceUnits).maxInferenceUnits(maxInferenceUnits).build();
}
// Start hosting the model.
lfvClient.startModel(startModelRequest);
DescribeModelRequest describeModelRequest = DescribeModelRequest.builder().projectName(projectName)
.modelVersion(modelVersion).build();
ModelDescription modelDescription = null;
boolean finished = false;
// Wait until model is hosted or failure occurs.
do {
modelDescription = lfvClient.describeModel(describeModelRequest).modelDescription();
switch (modelDescription.status()) {
case HOSTED:
logger.log(Level.INFO, "Model version {0} for project {1} is running.",
new Object[] { modelVersion, projectName });
finished = true;
break;
case STARTING_HOSTING:
logger.log(Level.INFO, "Model version {0} for project {1} is starting.",
new Object[] { modelVersion, projectName });
TimeUnit.SECONDS.sleep(60);
break;
case HOSTING_FAILED:
logger.log(Level.SEVERE, "Hosting failed for model version {0} for project {1}.",
new Object[] { modelVersion, projectName });
finished = true;
break;
default:
logger.log(Level.SEVERE, "Unexpected error when hosting model version {0} for project {1}: {2}.",
new Object[] { projectName, modelVersion, modelDescription.status() });
finished = true;
break;
}
} while (!finished);
logger.log(Level.INFO, "Finished starting model version {0} for project {1} status: {2}",
new Object[] { modelVersion, projectName, modelDescription.statusMessage() });
return modelDescription;
}
如果代码的输出为 Model is hosted and ready for use，则可以使用模型来检测图像中的异常。有关更多信息，请参阅 检测图像中的异常。
