User Guide

Serving deep learning model thorugh API.

Installation

Used fastapi, pytorch, pytorch-lightning mainly to create this project. You can use requirements.txt to install appropriate package version. First, create a virtual environment before installing any packages.

pip install -r requirements.txt

Note: The model was trained on Google Colab with GPU support.

About Project

This is a pet project where I wanted to learn FastApi. It's easy to train a new model these days. I also wanted to serve the model through an API. FastApi is very light and Flask like framework.

After googling I found some similar projects but they used keras. As a pytorch fan I used pytorch lightning for the training purpose.

CIFAR10 dataset was used in this project. The dataset was downloaded via torchvision dataset.

The project was designed in such a way that anyone can clone/download and run the projcet without any tweaking.

How to run the app

After installing necessary packages, use the following command to run the app from project root directory-

uvicorn app.main:app

And visit http://127.0.0.1:8000/docs from your browser. You will be able to see swagger. From there you can upload an image through predict endpoint and then you will get a json response.

Use --reload argument if you want to change code and see the effect immediately.

To Run the Test Cases Use pytest command from project root directory. Then you will see something similar to this -

How to run the app with docker

Make sure you are in the project root directory and you have started docker. Then create docker image using the following command.

docker build -t image-classifier-api .

After the image is successfully built, run the following commands to run the container.

docker run -p 5000:80 image-classifier-api

And visit http://127.0.0.1:5000/docs from your browser. You will be able to see swagger. From there you can upload an image through predict endpoint and then you will get a json response.

Model Training and Performance

Model performance wasn't the main focus of this project. So, I didn't try much to improve the model performance. All the hyperparameters can be found in config.py file.

All the files related to training can be found in the deep_learning_model/training folder.

Personal Feedback About The Project

• Naming is an issue that is there. (variable naming, Class naing, module naming, etc). This could have been better.
• There isn't clear seperation between the service and controller layer.
• There is "TorchScript" to take pytorch code to production which is much optimized version.
• The description of this README file isn't enough user friendly.

NOTE: Please, raise issue if you find an area where it needs some improvement.

Future Plan

This projects isn't upto the production standard at all. I will be updating this periodically. I have asked few of my seniors to review my codebase and provide feedback.

Though I have plan to work on the following improvements:

• Introducing Docker
• CI pipeline
• SonarCloud or Other code quality checking tool/service integration
• Dependency Injection in FastApi
• Saving requested images in s3 bucket.
• deploying the project on AWS or Heroku App

References:

• Developing and Testing an Asynchronous API with FastAPI and Pytest
• Tutorial: How to deploy your ConvNet classifier with Keras and FastAPI
• Building a Machine Learning API with FastAPI
• TRAINING A CLASSIFIER
• Tutorial - User Guide
• How to test a FastAPI api endpoint that consumes images?
• Find current directory and file's directory [duplicate]