This repo contains the boiler-plate code for serving any tensoflow model(frozen graph). The code base is written on C++.

There may be many repos for python-serving, but there are only a modicum of repos that are based on C++ serving.

The features of the model are summarized below:

• Tensorflow inference Code.
• Logging for each request
• Opencv backed image processing
• Fast Webserver

The code is based on pistache REST AP server, OpenCV, spdlog Logging, json, Boost libraries

The model provided is a resnet50 model, trained on imagenet1000.

• CPU: Intel i7-8550U CPU @ 1.80GHz
• RAM: 16GB

Python Implementation

• Inference Time: 300 secs/ 500 requests
• Workers: 6
• Ram consumption: 1.5GB

C++ Implementation

• Inference Time: 275 secs/ 500 requests
• Workers: 6
• Ram consumption: 0.5GB

1. Installation
2. Download Pre-Trained Model
3. Demo

Run,

./dependencies/install_dependencies.sh

to install boost, pistache, opencv, and all other dependancies.

Then run,

./startup.sh

to build and run the webserver.

To set the ip address and the port number modify the ./startup.sh. and to change the route modify it in src/app.cpp

A Pre-trained test model is available at this Link GoogleDrive, dowload it and place it in models/ path.

If you've cloned the repo and then downloaded the pre-trained model, run

./dependencies/install_dependencies.sh
./startup.sh

Now you could acess the websever using POST Reqeust using the endpoint,

http://0.0.0.0:52118/test-tensorflow-serving-api

with content type as "application/json"

{
  "url": "some-image-url"
}

or if you want to give a base64Image input then,

{
  "base64Image": "base64_image_data"
}

to facilitate a response.

If you encounter any issues, please create an issue tracker.