Create a deep learning REST API that wraps a Keras model in an efficient, scalable manner using Redis

In this project I am going to utilize Redis as a message broker/message queue. This involves:

• Running Redis on our machine
• Queuing up data (images) to our Redis store to be processed by our REST API
• Polling Redis for new batches of input images
• Classifying the images and returning the results to the client
• Stress testing with multiple requests

Functionality

• Our model will classify image using pretrained ResNet50 keras API

Environment

• Windows 10, Python 3.7.3, keras, Redis, Flask

Blog

• I have detailed the steps in my blog also

http://dsbyprateekg.blogspot.com/2019/03/how-to-use-redis-in-windows-along-with.html

Installing

• Create and Activate virtual env in Anaconda prompt using below commands-

conda create --name <env_name> python=3.7.3
activate <env_name>

• Follow below steps for installing Redis if you are using Windows

- Download latest of Redis from Redis for Windows link
- Download file be in zip format like Redis-x64-3.2.100.zip
- Create a new folder and extract downloaded zip file there
- Navigate to extracted folder and open it in a cmd as an Admin
- Run the following command:redis-server.exe
- A pop up will come and ask you to allow access, accept the default. It will start the Redis server
- Leave this terminal open to keep the Redis data store running
- In another terminal, you can validate Redis is up and running by following command: redis-cli.exe ping
- In response you will get a PONG back from Redis, it means you’re ready to go

Running examples

• Make sure Redis server is up and running
• Open anaconda prompt with your activated virtual env and Run python run_model_server.py wait till server is running
• Open another anaconda prompt with your activated virtual env and Run python run_web_server.py wait till server is running
• Open third anaconda prompt with your activated virtual env and Run python stress_test.py