Help Center>
ModelArts>
FAQs>
Service Deployment>
Service Deployment>
Real-Time Services>
Why Did My Service Deployment Fail with Proper Deployment Timeout Configured?
Updated on 2023-01-11 GMT+08:00
Why Did My Service Deployment Fail with Proper Deployment Timeout Configured?
A model can properly start after a service is deployed. The startup status of a model can be detected through a health check.
Check whether a service is deployed using a health check API for custom images. When creating an AI application, configure a health check delay to ensure the initialization of containers.
It is a good practice to configure a proper health check delay for service deployment.
Parent topic: Real-Time Services
Real-Time Services FAQs
- What Do I Do If a Conflict Occurs in the Python Dependency Package of a Custom Prediction Script When I Deploy a Real-Time Service?
- How Do I Speed Up Real-Time Prediction?
- Can a New-Version AI Application Still Use the Original API?
- What Is the Format of a Real-Time Service API?
- How Do I Check Whether an Error Is Caused by a Model When a Real-Time Service Is Running But Prediction Failed?
- How Do I Fill in the Request Header and Request Body of an Inference Request When a Real-Time Service Is Running?
- Why Cannot I Access the Obtained Inference Request Address from the Initiator Client?
- What Do I Do If Deploying a Service Failed Due to Insufficient Quota?
- Why Did My Service Deployment Fail with Proper Deployment Timeout Configured?
Feedback
Was this page helpful?
Provide feedbackThank you very much for your feedback. We will continue working to improve the documentation.See the reply and handling status in My Cloud VOC.
The system is busy. Please try again later.
For any further questions, feel free to contact us through the chatbot.
Chatbotmore
