As Python has largely dominated the realm of machine learning technology, it begs the question of which framework is most ideal for deployment.
Upon the completion of development for a given machine learning model, the next step is to ensure its efficient use through deployment. Python, in particular, offers many solutions. To understand two of the most popular, which include Django and Flask, one must first do a background comparison.
Flask
Flask represents a micro web framework that is notoriously easy to learn with very straightforward implementation using just several lines of code. Consequently, it is quite popular amongst many reputable tech firms including but not limited to Mozilla, Netflix, and Reddit.
Inside of your preferred IDE, open a new Terminal and enter the following:
pip install flask
Enter the following command to begin working with the framework immediately:
from flask import Flask, render_template
app = Flask(__name__)@app.route('/')
def home():
return: "Hello World!"
if _name_=='_main_':
app.run(debug=True)
Key Features:
- Rapid debugging with an in-house development server
- Clean API
- Configurations of high flexibility
- Integrated support for unit testing
Refer to the official documentation here for additional information.
Django
Similar to Flask, Django is a web framework built with Python. It follows the Model View Controller (MVC) pattern and is very accessible through its open-source infrastructure. Unfortunately, MVC has become infamous for its complexity to a beginner’s eye. Rather, it represents an ideal option for the deployment of models and web development in general. A number of popular websites such as Instagram and Pinterest run on Django.
Key Features:
- Security
- Versatility
- Scalability
- Long-term maintenance
- Portability
Refer to the official documentation here for more information.
Compare/Contrast
Here is a side-by-side table comparison designed to directly observe the pros and cons of Django and Flask:
What is the scope of your project?
For smaller projects and simply trained machine learning models, it is best to use Flask instead due to Django’s heavier features. Furthermore, with its additional lines of code and strict folder structure with dozens of libraries, Django is completely unsuitable for smaller projects.
For model deployment in which you only possess around 100 lines of HTML with less than or equal to the amount of CSS, Flask demands very little regulation. While Django can also be implemented, it is far more sophisticated and requires an advanced understanding of Python. Due to the fact that this would require a developer to pour through even more documentation, it helps to save time and energy by sticking with Flask.
How experienced are you with Python?
Though Python is a beloved language for its straightforward and seamless design bundled with a rich community offering comprehensive support, Django tends to appeal more to developers who want above-average control of the implementation to include more specific features for user stories.
Either way, as a beginner, the syntax is of Python is extremely concise, so no matter your choice, the code is still very approachable.
Does your solution require Authentication and Authorization?
An integrated package is included with Django for the purposes of handling authentication and authorization. Developers are able to configure, Groups, Users, hashing systems for Passwords, and many other elements. These functionalities are available in the following module:
Django.contrib.auth
However, with Flask, there is no in-house support for said features; requiring extensions instead.
Do you need a handler for Forms?
The creation and handling of forms are comparable in both frameworks. In addition to client-side validation, both offer server-side validation as well. Furthermore, they both have defenses in place for common security threats. Yet, Django has added protection against SQL injections, cross-site scripting, and forgery.
Yet, the forms are designed differently. Django uses ModelForm, a native feature, whereas Flask requires an extension.
Community Support
No matter your experience, the probability is high that you will eventually need a platform to reach out to when running into bugs. With broad, solid support from the community, working with Python and either Flask or Django becomes much easier.
Despite the fact that Django predates Flask by approximately 5 years, the community support for both frameworks is stellar when attempting to deploy machine learning models.
The Verdict
In the case that you possess a light codebase, a small model, and use only HTTP, Flask is superior for your application. If your project is more sophisticated and demands additional features with better security, Django is the preferred choice.
If you have any doubts about which is better for your application, opt for Flask instead. Its simplicity and straightforward design will get the job done.