0d74efbe9e630daae5840edfde765c309a65d4d7,tests/integ/test_inference_pipeline.py,,test_inference_pipeline_model_deploy_with_update_endpoint,#Any#Any#Any#,154

Before Change

        // Wait for endpoint to finish updating
        max_retry_count = 40  // Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
        current_retry_count = 0
        while current_retry_count <= max_retry_count:
            if current_retry_count >= max_retry_count:
                raise Exception("Endpoint status not "InService" within expected timeout.")
            time.sleep(30)
            new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
                EndpointName=endpoint_name
            )
            current_retry_count += 1
            if new_endpoint["EndpointStatus"] == "InService":
                break

        new_config_name = new_endpoint["EndpointConfigName"]

After Change

        // Wait for endpoint to finish updating
        // Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
        for _ in retries(40, "Waiting for "InService" endpoint status", seconds_to_sleep=30):
            new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
                EndpointName=endpoint_name
            )
            if new_endpoint["EndpointStatus"] == "InService":
                break

        new_config_name = new_endpoint["EndpointConfigName"]
        new_config = sagemaker_session.sagemaker_client.describe_endpoint_config(
            EndpointConfigName=new_config_name
        )

Instances