Understanding the ModelNotReadyException in AWS SageMaker Runtime

Introduction

Welcome to our technical blog! In this article, we will dive deep into the ModelNotReadyException of the com.amazonaws.services.sagemakerruntime.model package in AWS SageMaker Runtime. If you are working with machine learning models in SageMaker, understanding this exception is crucial for handling model readiness appropriately.

So, let’s explore what the ModelNotReadyException is, its causes, and how we can handle it effectively.

What is the ModelNotReadyException?

The ModelNotReadyException is an exception class provided by Amazon SageMaker Runtime to indicate that a requested SageMaker model is not yet ready for inference. This exception is thrown by many API operations of the com.amazonaws.services.sagemakerruntime.model package and can occur when attempting to invoke an endpoint with a model that is still being deployed, or when performing an action on a model that is currently being deleted.

When encountering this exception, it’s important to handle it properly to ensure that your application can respond appropriately to the readiness state of the model.

Causes of ModelNotReadyException

Let’s take a look at some common causes for the ModelNotReadyException:

1. Model Deployment: One of the most common causes is when you attempt to invoke an endpoint before the model is fully deployed. This can happen if you try to make predictions immediately after calling the CreateModel operation, without waiting for the deployment to complete.

2. Model Deletion: Another cause can be the deletion of a model in progress. If you try to perform any operations on a model that is being deleted, such as invoking an endpoint or updating its configuration, the ModelNotReadyException may be thrown.

Handling the ModelNotReadyException

Fortunately, SageMaker provides mechanisms to handle the ModelNotReadyException effectively. Here are some best practices to consider:

1. Implement Retry Logic

In scenarios where a model deployment is in progress, it’s a good practice to implement retry logic. By periodically checking the status of the model using the DescribeModel operation, you can determine when the model is ready for inference. Once the model is deployed, you can proceed with making predictions without encountering the ModelNotReadyException.

boolean isModelReady = false;

while (!isModelReady) {
    try {
        // Check the status of the model
        DescribeModelResult describeModelResult = sagemakerClient.describeModel(new DescribeModelRequest().withModelName("my-model"));

        // If the model is ready, set the flag to true and proceed for inference
        if (ModelStatus.DEPLOYED.equals(describeModelResult.getModelStatus())) {
            isModelReady = true;
        } else {
            // Sleep for some time before checking again
            Thread.sleep(5000);
        }
    } catch (ModelNotReadyException ex) {
        // The model is not yet ready, continue the loop
        Thread.sleep(5000);
    }
}

2. Gracefully Handle Exceptions

When invoking SageMaker endpoints, make sure to handle the ModelNotReadyException gracefully. You can catch the exception, log an appropriate message, and decide how to handle the situation based on your application’s requirements. For example, you can return a special response indicating that the model is not yet ready and retry after some time.

try {
    // Invoke the SageMaker endpoint
    InvokeEndpointResult invokeEndpointResult = sagemakerRuntimeClient.invokeEndpoint(new InvokeEndpointRequest()
            .withEndpointName("my-endpoint")
            .withBody(requestPayload));

    // Process the inference response
    // ...
} catch (ModelNotReadyException ex) {
    // Log the exception or return a custom error response
    // ...
}

Conclusion

In this article, we explored the ModelNotReadyException of the com.amazonaws.services.sagemakerruntime.model package in AWS SageMaker Runtime. We discussed the causes of this exception and provided best practices for handling it.

Remember, when working with SageMaker models, it’s crucial to consider the readiness state of the model before making predictions or performing any actions on it. By implementing retry logic and gracefully handling the ModelNotReadyException, you can ensure smooth integration of your machine learning workflows.

To learn more about the ModelNotReadyException and SageMaker Runtime, please refer to the following official documentation:

• SageMaker API Reference
• AWS SDK for Java Documentation

Happy coding, and stay tuned for more technical insights from our blog!

Total read time: 15 minutes