Structuring a Scalable Django Project for Serving Deep Learning Models: An Organized Approach

Organizing a Django project that serves multiple deep learning models can be a challenging task, but with a well-structured file hierarchy, it can be made much easier to understand and maintain. In this article, we will discuss one approach that involves creating separate apps for each model and illustrate the file structure that follows this approach.

The first step in organizing the project is to create a new Django project using the command django-admin startproject projectname. This will create the following file structure:

projectname/ manage.py projectname/ __init__.py settings.py urls.py asgi.py wsgi.py

Next, create a new app for each model using the command python manage.py startapp appname. It's important to give the apps meaningful names that reflect the purpose of the model. For example, if you have a model that predicts the price of a house, you can name the app house_price_prediction. And for a model that classifies images of animals, you can name the app animal_classification. This will make it easy to understand the purpose of the app just by looking at its name.

Once the apps are created, you should have the following file structure:

projectname/
    manage.py
    projectname/
        __init__.py
        settings.py
        urls.py
        asgi.py
        wsgi.py
    house_price_prediction/
        migrations/
            __init__.py
        __init__.py
        admin.py
        apps.py
        models.py
        tests.py
        views.py
    animal_classification/
        migrations/
            __init__.py
        __init__.py
        admin.py
        apps.py
        models.py
        tests.py
        views.py

You can move the model files (.h5 files) to the appropriate app's directory. It's recommended to create a new directory within the app directory to store the model files, for example house_price_prediction/models/ or animal_classification/models/. This will keep the model files organized and easy to find. The file structure should look like this:

projectname/
    manage.py
    projectname/
        __init__.py
        settings.py
        urls.py
        asgi.py
        wsgi.py
    house_price_prediction/
        migrations/
            __init__.py
        __init__.py
        admin.py
        apps.py
        models.py
        tests.py
        views.py
        models/
            house_price_model.h5
    animal_classification/
        migrations/
            __init__.py
        __init__.py
        admin.py
        apps.py
        models.py
        tests.py
        views.py
        models/
            animal_classification_model.h5

In the views.py file of each app, you can import the necessary libraries and load the model using the Keras library, you can also create a prediction function that takes the input data, makes predictions using the loaded model and returns the result.

For example, in the views.py

from keras.models import load_model

class HousePricePredictionView(View):
    def __init__(self):
        self.model = load_model('house_price_prediction/models/house_price_model.h5')

    def predict(self, input_data):
        prediction = self.model.predict(input_data)
        return prediction

And in the views.py file of the animal_classification app, you can import the necessary libraries and load the model like this:

from keras.models import load_model

class AnimalClassificationView(View):
    def __init__(self):
        self.model = load_model('animal_classification/models/animal_classification_model.h5')

    def predict(self, input_data):
        prediction = self.model.predict(input_data)
        return prediction

In the above example, each app has its own views class and predict method to interact with the loaded models.

In this way, the project can be well-structured, easy to understand and maintain. Each model is isolated in its own app, making it easy to add, update or remove models without affecting the rest of the project. And by keeping the model files in a separate directory within the app, it is easy to locate and manage the model files. This approach also makes it easy to add more functionality to the project without cluttering the codebase.

Note that this is just one approach, there are several other ways to organize the project, depending on the complexity of the project and the number of models that need to be served. But, this approach is a good starting point for organizing a Django project that serves deep learning models.

The typical file structure overall should look like this:

my_project/
    manage.py
    my_project/
        settings.py
        urls.py
        wsgi.py
        ...
    house_price_prediction/
        models/
            house_price_model.h5
        views.py
        ...
    animal_classification/
        models/
            animal_classification_model.h5
        views.py
        ...

In this example, the project has two apps, house_price_prediction and animal_classification. Each app has its own directory, and within each app directory, there is a models directory that contains the corresponding h5 model files. The views.py file for each app is where the logic for loading and making predictions with the models is located.

It's also worth noting that, In the above example, I have illustrated the use of load_model method from keras library to load the model, this is just one way of loading models, you can use other libraries like Tensorflow, Pytorch, etc to load models.

In conclusion, organizing a Django project that serves deep learning models can be a challenging task. However, by following a structured approach and keeping the models isolated in their own apps, it is possible to create a well-organized project that is easy to understand and maintain. And by keeping the model files in a separate directory within the app, it is easy to locate and manage the model files. This approach also makes it easy to add more functionality to the project without cluttering the codebase.

WRITTEN by Kibekityo Juma Shafara(Applied Data Scientist)