Python is insanely popular among machine learning enthusiasts these days. Hence, anyone developing a machine learning model normally turns to Python. The real challenge arises at the deployment stage because you can use many frameworks. Figuring out which Python framework to use may add to the confusion.
This post discusses two popular machine learning frameworks, Flask and Django. We’ll also compare them side by side, so that you can make the right choice. If you are also stuck in the deployment stage, hop in because this post is for you.
A Basic Overview of Flask & Django
|A micro web framework written in Python||A web framework built on Python|
|Easy to learn with straightforward implementation||Open-source and accessible but follows the MVC pattern (Model View Controller), which often confuses beginners|
|Fewer lines of code||Lines of code are larger than Flask|
|Primarily used by top companies like Netflix, Reddit and Mozilla||Companies like Pinterest & Instagram use this framework|
Pros & Cons – Flask vs. Django
Like other development frameworks, Flask and Django have their pros and cons – which you must understand to make the right decision. Let’s look at the upsides and downsides of both frameworks:
Pros of Flask
- You can easily scale the server to handle increased load/traffic with Flask. Pinterest decided to migrate from Django to Flask for this reason
- Getting started with Flask is easier. You can easily get the hang of Python with a basic understanding. Moreover, ample documentation is available if you face any problems
- Flask is simple, minimalistic and flexible. Making alterations to any part of it should be easy
- With the modular code of Flask, you can easily create multiple Flask applications with specific purposes – thus enhancing efficiency, testability and performance
Cons of Flask
- Flask has limited tools because of its lightweight nature, meaning developers have to manually add extensions, like libraries. Too many extensions can slow down the app by generating too many multiple requests
- Because of the modular nature of Flask, developers joining in the middle of the project may struggle to understand the code and how the app is designed
- Flask often leads to higher maintenance costs as the project progresses further
Pros of Django
- By letting developers import packages for adding functionalities, Django saves a lot of time compared to writing code from scratch. This enables developers to devote more time to innovation
- Django is ideal for developing applications that leverage the IoT, AI and machine learning, because Django features intelligent development capabilities
- Developers can develop applications with clean, readable and maintainable code, benefitting from the syntax rules of Python
- Django can help you create scalable websites that withstand heavy traffic
- Django has minimal chances of security loopholes and offers active prevention against SQL injection attacks and cross-site request forgery
Cons of Django
- Since Django doesn’t have conventions, developers need to define everything independently, which slows down the development process
- Developers can’t use their own file structures, must play by the rules and use predefined variables, because of the monolithic nature of Django
- Django is unsuitable for small projects, as it comes with lots of code that consumes server processing time
- Django doesn’t allow individual processes to handle multiple requests simultaneously. Developers have to come up with ways to make individual processes handle multiple requests
- The learning curve to master Django is steep for developers, as it comes with many functionalities beginners can’t understand
Deciding Between the Flask or Django Python Framework
Deciding on which python framework to choose between Flask and Django depends on many factors. Let’s compare them one by one:
1. Your Familiarity with Python
Flask is suited if you are a complete beginner or intermediate in Python. The easy structure of the framework will ensure you can deploy your machine learning model without any hassle. However, if you are at the advanced stage of Python, Flask will seem rudimentary, and Django will suit you more. Its native development features can save you a lot of time.
2. The Size of Your Machine Learning Project
Flask is more suitable for simply trained machine learning models than Django. Here are a few reasons why:
- The fully-featured Django framework can be a bit too much
- Flask results in a shorter learning curve. Therefore, developers spend less time getting acquainted with it
- Fewer lines of code are written in Flask, as Django relies on dependencies and specific folder structures. Flask also results in cleaner code
In simple words, Flask is sufficient for most machine learning projects, except complex ones. If you are an advanced Python user, however, Django offers greater advantages.
3. The Database Management System You Plan to Use
The choice of a database management system also plays a crucial role in deciding which Python framework you should use. Not that you can’t use all the database management systems with Django and Flask. However, using non-relational data management systems with Django can be complicated in your machine learning models.
Django relies on the ORM Django layer for creating read, write, query,and delete operations using the Python code in a relational database. However, Django can’t do the same for non-relational databases. Using Django with a non-relational database requires you to build a backend to support the same.
On the other hand, Flask doesn’t have a native ORM. Therefore, how the framework interacts with databases depends on the ORM extension you choose. Hence, Flask is a better choice when using non-relational database management systems.
4. Your Need for Authorization & Authentication
Both Django and Flask support authentication and authorization. The only difference is that Django comes with a pre-integrated package for this functionality. Using Django, then, simplifies how you configure users, groups, passwords, systems, etc.
On the other hand, Flask doesn’t have built-in authentication and authorization functionalities. You will need extensions like Flask-login, Flask Mail, or Flask-WTF.
So, if you want to save yourself from the headache of having to install different extensions, you can choose Django. Otherwise, Flask is a fine choice.
5. Handling of Forms
Django comes with a native form handling feature called ModelForm that allows both client-side and server-side validations. ModelForm secures your machine learning model against threats like SQL injection, cross-site scripting and cross-site request forgery.
Flask, however, doesn’t have any such feature. Instead, it relies on the Flask-WTF extension for creating an integration with WTForms.
6. Speed Benchmarks
The following benchmarks provide a base-level comparison of Django and Flask speed.
|JSON – Average response time of 43.33 milliseconds (4,630 requests per second)||JSON – Average response time of 42.52 milliseconds (4,762 requests per second)|
|Remote Test – Average response time of 3344.27 milliseconds from a remote server (18.15 requests per second)||Remote Test – Average time of 3477.36 milliseconds from a remote server (18.1 requests per second)|
|Complete Test – Average data load to render time of 1440.24 milliseconds||Complete Test – Average data load to render time of 2904.04 milliseconds (twice as long as Flask)|
So, Django may lag behind Flask in the complete test. Still, there are other speed benchmarks where it is comparable.
7. Community Support
Simply put, you won’t have to sacrifice community support by choosing either Django or Flask. Django has a larger community, as it’s been around for six more years than Flask. However, both frameworks offer active community support.
In Simplest Terms
Go with Flask if you deploy a small machine learning model with a light codebase.If you’re planning to build something spectacular like Facebook, Django is a more suitable choice. If you are a beginner, go with Flask. While Django has advantages in certain projects, the learning curve is far too steep for a beginner. Hopefully, this clears up which framework you should choose for your machine learning model.
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