Django vs. Flask: What Works Better for Machine Learning?

In data science—especially for machine learning—Python is one of the most popular programming languages. Therefore, the question of which framework is best for deploying a machine learning model is valid. Django and Flask are both Python frameworks, but which works best?

Flask is best for beginners while Django is for more advanced machine learning deployments. Flask is a microframework making it more reliant on extensions for functionality. Django is a full-stack web framework. It comes with more ready to access features.

If you are at the deployment stage of a machine learning project, this article will provide you with information on both the Flask and Django frameworks. It will allow you to judge for yourself which framework is best suited for your level of Python experience and the scope of your machine learning project.

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Why Is Choosing a Python Framework for Machine Learning Deployment Important?

Choosing an algorithm and training that algorithm to become a trained model for your machine learning project is an essential first step. However, to get your trained models into the production phase, they need to be deployed.

You will require some form of deployment to collect data from users online or train the model further. Deployment is also necessary if you want your model to return results based on user input using your trained model.

For this type of deployment, Django and Flask offer developers and data science engineers the best option for reducing the complexity involved with such implementations.

Choosing the Python framework that is best for deploying your machine learning model will depend on multiple factors.

Your Level of Familiarity With Python

Python is a programming language that incorporates an easy-to-learn structure and syntax. By extension, this makes working with the many python frameworks equally as easy—at least compared to other programming languages.

Despite this “user-friendliness,” for those new to Python, there is still a learning curve. If you are at the early to mid-stages of mastering Python, the Flask Framework will be best suited for transferring your current knowledge of Python to using Flask.

If you are more advanced in your knowledge and use of Python, you might find the Flask framework more rudimentary than Django. Not that you will discover Flask as being deficient, just that you may find the lack of native development features a drag on your development timeline

Verdict

The key takeaway is that Flask is much more beginner-friendly than Django when it comes to the deployment of machine learning models.

Size of the Machine Learning Project

For those involved in a simple trained model deployment, Django’s full-featured nature might be an overkill. It becomes more evident when you consider the longer learning curve and the amount of code bloat involved in writing a machine learning deployment application in Django compared to Flask.

There will usually be more lines of code in an application written in Django compared to Flask. The longer code is required because Django relies on dependencies and specific folder structures when developing an application.

Fortunately, most machine learning models can be deployed using Flask without the need for Django’s complex options and its libraries.

For example, if your machine learning model consisted of a dataset consisting of three to 12 columns and 100,000 rows and you used the “train_test_split” function from the Scikit-Learn Python library to perform linear regression, the amount of code to deploy such a model would be small.

Looking at the individual components of such a deployment, you’d be looking at:

• Developing the front-end interface so users can input values coded in HTML—under 50 lines of code.
• Styling to the front-end interface using CSS—under 50 lines of code.
• Converting your model from a python object to a character stream using pickling—under 40 lines of code.
• Building the API that will receive the input values and compute them through your model—under 40 lines of code.

That’s under 200 total lines for deploying your machine learning model. Of course, if your model is more complex or requires vastly more permutations, the code will be longer. However, whatever the code length is when using Flask, it will nearly always be longer if using Django.

Verdict

For all but the most complex deployments of machine learning models, Flask is sufficient. In most cases, it will result in a cleaner codebase and require less of a learning curve for the developer to dominate. Django, however, can provide an advantage to those developers who are advanced in Python.

Do You Rely on Community Support?

If you are new to developing machine learning models and deploying them, having a reliable community of users for the framework you use can be important. Such communities can provide you with immense support by providing answers to questions that you may have. These communities can also stimulate the creation of further use applications for the framework.

In terms of community support, both Flask and Django have extensive, knowledgeable, and inspired communities. Django, having been around for five years more than Flask, may boast a broader community, but the Flask community, although smaller, is very participative.

The fact that both frameworks are open-source allows you to use the number of committers to each of their codebases to get a more detailed look at each framework’s support community’s size. Django has nearly 1,900 committers while Flask has only 600.

Given the five year head start enjoyed by Django and the fact that its code is much larger than Flask—close to 300,000 lines of code for Django versus less than 30,000 for Flask—a 3-to-1 ratio in committers between both frameworks is not unusual. It also should not be taken that the Flask community is less participative.

If anything, it can be taken as validation for those who claim the Flask support community is more supportive to those new to the framework and machine learning deployment.

You can also measure the level of community support for each framework by looking at the number of questions posted on Stackoverflow tagged with either “Django” or “Flask.” As of November 2020, the former has over 250,000 tagged questions, and the latter has over 42,000 tagged questions.

Again, this metric might make it seem as if Django is the clear leader in community support. While that assertion might hold when applied to the development of applications, in general, using Django, it doesn’t hold up when you focus specifically on applications for the deployment of machine learning models.

When you do so, you will find that the number of questions on Stack Overflow for each framework tagged with the additional modifier of “machine learning” has nearly the same number of items. What can you extrapolate from this?

Verdict

Even though the support community for Django is larger overall than that of Flask, the amount of community support for both is quite balanced when it comes to machine learning deployment. This balance means that you will not have to sacrifice community support if you use Django or Flask. 

What Database Management System Will You Use in Your Project?

The database management system you use in your machine learning project can also affect your choice of framework for your project’s deployment phase.

To explain this further, consider the database management systems that are used more frequently for machine learning.

Relational Database Management Systems

• Elasticsearch
• MLDB
• Microsoft SQL Server
• MySQL
• PostgreSQL

Non-Relational Database Management Systems

• Apache Cassandra
• Couchbase
• DynamoDB
• Elasticsearch
• MongoDB
• Redis

You can use all of these database management systems with Django and Flask. However, if you use one of the non-relational database management systems, relying on Django can be more complicated.

Django and Non-Relational Database Management Systems

As has been explained above, Django is a robust “batteries-included” framework. Being all-inclusive means that all of the features that you need are contained within the framework. However, when it comes to working with database management systems, non-relational database management systems can be problematic.

Django relies on the ORM Django layer to create “read, write, query, and delete” operations on a relational database using Python code. This ORM cannot do the same for non-relational databases. This discrepancy means that if you are using the Django framework and your machine learning deployment requires conducting operations with a non-relational database, you will have to find or build a backend that can support this.

While you can find such backends on code repositories such as GitHub, how well they perform with your specific machine learning deployment model will depend on many variables. These variables can make relying on these backends sketchy. Additionally, if your Python skills are not advanced, this can make for a nightmarish scenario.

Flask and Non-Relational Database Management Systems

Flask is a microframework and only includes templating, error handling, URL routing, and a debugger, which means that all other functions require extensions. Flask does not come with a native ORM like Django. This lack of a native ORM means that when it comes to how Flask interacts with databases is dependent on the ORM extension chosen by the developer.

For relational databases, the most common ORM extension for Flask is SQLAlchemy.

MongoAlchemy and MongoEngine are two popular ORM extensions for Flask that you can use to integrate MongoDB—one of the most used non-relational database management systems. You can use other Python libraries to connect to other non-relational databases with Flask.

Verdict

In short, both Flask and Django can be used if your deployment involves a relational database. If, however, you are relying on a non-relational database, the ORM that is native to Django will not suffice. It is easier to interact with NoSQL databases using Flask.

Requirements for Authorization and Authentication

In deploying a trained machine learning model, the accompanying web application—and corresponding APIs—involved in such a deployment will require the authorization and authentication of the users accessing them. Django has an integrated package for handling authorization and authentication.

Authorization and Authentication With Django

The authentication system in Django allows you to configure:

• Users
• Groups
• A password hashing system.
• A binary-based permission system.
• A pluggable backend system.

This functionality comes as part of the “django.contrib.auth” module in Django.

Authorization and Authentication With Flask

Flask does not have authorization and authentication functionality built-in. Flask requires the use of extensions to accomplish this. These extensions allow you to have a session- or token-based authentication. You can also have JSON and Ajax support.

The extensions used in Flask for this functionality are:

• Flask-login
• Flask-principal
• Flask-WTF
• Flask-mail

Installation of the PassLib and Itsdangerous Python libraries may also be necessary.

You can elect to use the opinionated extension known as Flask-security that integrates the extensions and libraries cited above.

Verdict

Both Django and Flask allow for authorization and authentication, be it natively or as an extension.

Requirements for Managing Data Based on Your Model

When choosing between Django and Flask, something else to consider is your requirements for administering the data based on your deployed models.

The built-in Django admin tool can handle a wide variety of administrative tasks. The tool allows you to perform CRUD operations as you build out the deployment application. You can accomplish all of this without having to write additional code. It relies on metadata from your model to provide an interface for managing content.

If you use Flask, it does not have an administration feature. Again, as with most other types of functionality on Flask, you need to rely on extensions. The extension used for this is Flask-Admin.

When using Flask-Admin, unlike the built-in Django admin tool, you can choose to completely change the look of the administrative UI. You are also not limited to CRUD operations. You can use WebSockets for the implementation of real-time monitoring.

Verdict

For administering data on your deployed models, both Django and Flask offer different but equally effective methods. Django’s admin panel is built-in; Flask requires an extension.

Handling of Forms

The creation of forms from models is a task that is an integral part of Django. It is handled by the Django helper class “ModelForm.” This helper class includes validation on both the client- and server-side. It also offers protection against cross-site scripting, SQL injection, and cross-site request forgery.

Flask does not have any native form handling feature. It relies on the Flask-WTF extension to create an integration with WTForms. In turn, WTForms-Alchemy can be used to create forms automatically from SQLAlchemy driven models.

Verdict

Form creation and handling with the Django and Flask frameworks are comparable. They both provide you with options for validation on both the client and server-side. Also, common security threats are addressed.

Speed Benchmarks for Django and Flask

The three Github benchmark tests cited for comparing Django and Flask’s speed consisted of the JSON, remote, and complete tests.

JSON Test

The JSON test consists of measuring the time it takes in milliseconds to serialize an object to JSON and waiting for an application/JSON response to be returned.

Django returned a response in an average of 42.52 milliseconds, while Flask averaged 43.33 milliseconds. These results mean that Django could handle 4,762 requests per second and Flask 4,630 requests per second.

Remote Test

The remote test measures the time it takes in milliseconds for an HTTP response to be loaded and returned from a remote server.

Flask had an average time of 3344.27 milliseconds to return the response from the remote server. Django had an average response time of 3477.36. These results mean that the requests per second are practically identical, at 18.15 and 18.1, respectively.

Complete Test

The complete test uses data from a relational database utilizing object-relational mapping to insert a new object into a template while also sorting and rendering it.

In this test, the difference between Flask and Django was more pronounced. Flask averaged a data load to render time of 1440.24 milliseconds. Django lagged way behind at 2904.04 milliseconds—over twice the time of Flask.

This difference is reflected in the average requests per second, with Flask handling 123 and Django only 42.9.

Django came in behind other Python web frameworks, not just Flask. Django lagged behind other popper frameworks, such as Bottle, Falcon, muffin, Pyramid, Weppy, Wheezy Web, and Tornado.

Verdict

Even though Django lags way behind in time to render compared to Flask and other Python web frameworks, its performance on the other speed benchmark tests makes it comparable to Flask. Speed benchmark results alone between the two frameworks do not provide enough reason to favor one over the other.

Conclusion

The question of what Python framework is best for your machine learning project depends on several factors. Among these are:

• The complexity of your project
• The presence of non-relational databases
• Your level of experience with Python
• Your experience with deploying machine learning models

Django is robust and full-featured, making it well suited for complex machine learning deployments. Its complexity has the price of a longer learning curve. Flask is leaner and relies on extensions for added functionality. This comparative simplicity, however, makes it ideal for smaller projects or those requiring flexibility.

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