ajay2307 / auto-dl

Auto-DL helps you make Deep Learning models without writing a single line of code and giving as little input as possible.

Generator

The interface section of Auto-DL contains front-end and back-end servers based on React and Django Rest Framework respectively. The backend calls the DLMML API according to the requests it recieves.

Generator has DLMML as a submodule.

Demo

How to run

1. # clone the repo
   git clone https://github.com/Auto-DL/Generator.git
   git submodule init
   git submodule update

2. Activate your environment (not necessary but highly recommended).

3. # install the requirements, this might take some time, be patient
   pip install -r requirements.txt

4. # If you think your machine can handle a simulatenous installation of node modules, open another terminal
   
   cd FrontEndApp
   npm install
   
   # go grab a cup of coffee, it takes an eternity XD

5. Place data in the ./data directory.

   Your data should be divided into classes for classification, for example, if you're classifying "Cats V/s Dogs", then your ./data directory would look like:

   data
   └───dogs_and_cats
       ├───test
       │   ├───cats
       │   └───dogs
       └───train
           ├───cats
           └───dogs

6. # run the backend
   # only after all requriements from requirements.txt are installed
   cd BackEndApp
   python manage.py runserver
   # you can ignore any migration warnings

7. # finally, run the react frontend
   # on a new terminal tab
   cd FrontEndApp/v1-react
   npm start

Note: For detailed instruction on data directory (point 5) please read DLMML's User Guide.

Where to go next?

To know more about the project and initiative, please visit our website

Curious to know about the DLMML API? Here, Have a look :)

Note

• To know more about the technicalities of the project, read our developer guidelines.
• For more detailed instructions to run the Generator module, Read our User guidelines

Contributing

Please take a look at our contributing guidelines if you're interested in helping!

Features/Enhancements Planned

• Improve the UI and UX.

• Show model training realted stats on the frontend.

• Visualization and data preprocessing steps.

• Model Explainability.