为什么使用 Boto3 通过 AWS Sagemaker 创建终端节点需要这么长时间？
Why did it take so long to create endpoint with AWS Sagemaker using Boto3?
根据存储的端点配置创建我的端点需要 45 分钟。 （我测试过它也有效）。这是我第一次使用 boto3 来执行此操作，而之前我只是使用 Sagemaker Web GUI 从端点配置创建端点。感谢对我的代码的建议：
import boto3
sagemaker_client = boto3.client('sagemaker')
response = sagemaker_client.create_endpoint(
EndpointName='sagemaker-tensorflow-x',
EndpointConfigName='sagemaker-tensorflow-x'
)
注意：我已将端点名称的最后一部分替换为 x。
AWS 目前 issues 与 Sagemaker：
Increased Error Rates and Latencies for Multiple API operations
5:33 PM PDT We are investigating increased error rates and latencies for CreateTrainingJob, CreateHyperParameterTuningJob, and CreateEndpoint API operations in the US-EAST-1 Region. Previously created jobs and endpoints are unaffected.
6:04 PM PDT We are continuing to investigate increased error rates and latencies for CreateTrainingJob, CreateHyperParameterTuningJob, and CreateEndpoint API operations in the US-EAST-1 Region. Previously created jobs and endpoints are unaffected.
根据存储的端点配置创建我的端点需要 45 分钟。 （我测试过它也有效）。这是我第一次使用 boto3 来执行此操作，而之前我只是使用 Sagemaker Web GUI 从端点配置创建端点。感谢对我的代码的建议：
import boto3
sagemaker_client = boto3.client('sagemaker')
response = sagemaker_client.create_endpoint(
EndpointName='sagemaker-tensorflow-x',
EndpointConfigName='sagemaker-tensorflow-x'
)
注意：我已将端点名称的最后一部分替换为 x。
AWS 目前 issues 与 Sagemaker：
Increased Error Rates and Latencies for Multiple API operations
5:33 PM PDT We are investigating increased error rates and latencies for CreateTrainingJob, CreateHyperParameterTuningJob, and CreateEndpoint API operations in the US-EAST-1 Region. Previously created jobs and endpoints are unaffected.
6:04 PM PDT We are continuing to investigate increased error rates and latencies for CreateTrainingJob, CreateHyperParameterTuningJob, and CreateEndpoint API operations in the US-EAST-1 Region. Previously created jobs and endpoints are unaffected.