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Showing posts from June, 2020

GSOC Coding: Week 4

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" Previously on LibreHealth GSOC 2020 "   [ Recap ] The demo of the app was ready but it was not dockerized. Running it would have been a nightmare. "Docker"   [ Work ] This week was all about learning about docker and make it easy to deploy my project. Which I successfully did thanks to docker compose. Docker compose is awesome if you want to build and run multiple container which communicate with each other. In our case as of now we have 3 docker containers communicating with each other. " Next week "   [ On LibreHealth ] 1. Add support for the react app.

GSOC Coding: Week 3

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The Django Backend and TF-Serving has been successfully implemented. The communication between the two is facilitated by Python based middleware. " Synchronous to Asynchronous "   [ Work ] This week was all about converting the python middleware into asynchronous service and generating Doc strings and documentation as the end of first month is nearing. Fortunately, I have completed my commitments for the first month and right no, I'm polishing the code. Python's Asyncio module was used to get the async await features. For facilitating dataset storage, I have looked into NextCloud. " Next week "   [ On LibreHealth ] 1) Train the NIH Xray dataset on a different object detection algo and add a support for selecting different ML algos. 2) Complete all documentations.

GSOC Coding: Week 2

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" Previously on LibreHealth GSOC 2020 "   [ Recap ] The Django server is implemented successfully. The Django backend is a Restful server that is responsible for communication between user and TF-Serving. Also, it is responsible for storing user data. "T ensorflow Serving "   [ Work ] The diagram above explains how we plan to deploy our ML models. Basically, the python middleware sends images to the TF Serving and receives BBox information and then send the received data to client. The client in this case is the Django Rest Server. The Django server stores Images and BBox data in a SQL DB. The User can now request for image and Bbox data from Django Server. As of now everything is implemented, but improvements will be made to the python based Middleware. " Next week "   [ On LibreHealth ] 1) Make the Python middleware Asynchronous and document everything. 2) Decide what type of file storage server we want to implement.

GSOC Coding: Week 1

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" Previously on LibreHealth GSOC 2020 "   [ Recap ] The community bonding period has ended and we are clear with our requirements of what we are going to be doing for the summer. " Django backend "   [ Work ] The server in the above figure is a combination of 2 servers the django-server and Tensorflow servering. Basically, the Django server acts as the middleman between the Client and tensorflow serving. I have implemented the API for Django-server. I have used generic views and model serializers. The DB is sqlLite. " Next week "   [ On LibreHealth ] 1) Training the tf model. 2) Setup tf-serving container.