How do you deploy a trained PyTorch model on AWS Lambda for real-time inference

Question

With the help of code, can you explain how to deploy a trained PyTorch model on AWS Lambda for real-time inference?

Answer 1

In order to deploy a trained PyTorch model on AWS Lambda for real-time inference, You can refer to the following steps:

• Prepare your PyTorch model
• Save the trained model as a .pth file.
• Create a Lambda Function
• Create a Python function that loads the model and performs inference.

Here is the code reference for the above steps:

•  Package the Lambda Deployment:
  • Package the Lambda function and dependencies into a deployment package (ZIP file).
  • Include PyTorch and any necessary dependencies, which can be added via a Lambda Layer or bundled into the ZIP.
• Deploy on AWS Lambda:
  • Create a Lambda function in the AWS Console.
  • Upload the deployment package.
  • Set the handler to lambda_function.lambda_handler.
• Test the Lambda Function:
  • Invoke the Lambda function through an API Gateway or AWS SDK.

In the above code methods, we are using Model Loading, Which Loads the saved model inside the Lambda function; Inference, which performs inference inside the Lambda function using the model; and API Gateway, which exposes the Lambda function via API Gateway for real-time access.

Hence, this setup allows for efficient real-time inference in AWS Lambda using a trained PyTorch model.