- The aim of the project was to develop a cloud app which will provide Image Recognition as a Service to users by using the AWS cloud resources to perform deep learning on images provided by the users.
- The deep learning model was provided in an AWS image (ID: ami-07303b67, Name: imageRecognition, Region: us-west-1a). This application invokes this model to perform image recognition on the received images.
- The application will handle multiple requests concurrently. It will automatically scale out when the request demand increases, and automatically scale in when the demand drops.
- AWS services used in the project are
- Elastic Compute Cloud (EC2)
- Simple Queue Service (SQS)
- Simple Storage Service (S3)
- Further details are provided in the report.
- This is a RESTful Web Service which accepts requests from the user (Image URL) and puts the request body onto an Input Queue.
- After which, it starts listening to the Output Queue for the response.
- This application also has a load balancer service which creates app instances when the request demand increases (Scale out).
- This application runs inside the app instances and listens for messages (requests) in the Input Queue.
- When the message arrives, it takes the message and runs the deep learning model for classification and puts the classification result into a S3 bucket. The classification result is also inserted into the Output Queue.
- When there is no message in the Input queue, the application shuts down the instance in which its running, facilitating scale in.
- This is the same as Listener application but the instance which is running this application won't terminate at all, facilitating quick response to the user.
- HTTP Requests should be sent in the below format
- http://[IP Address]/cloudimagerecognition?input=[URL of the image]
- example: http://13.57.186.192:8080/cloudimagerecognition?input=http://visa.lab.asu.edu/cifar-10/104_automobile.png
