Python FastAPI vs. Flask for Machine Learning Projects (2024)

As more businesses create machine learning applications, it is essential to have the right programming language that makes code less complex and easier to implement. Python is popular for building machine learning (ML) and data science applications. Why? Because it contains a wide variety of libraries, is extensible, offers simple-to-use and flexible tools, and has a strong development community. This blog compares FastAPI vs. Flask, two of the most popular Python frameworks for developing machine learning applications.


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5 Key Differences Between FastAPI vs. Flask

Below is a detailed comparison of FastAPI vs. Flask for machine learning projects.

1. FastAPI vs. Flask: Performance

FastAPI performs significantly better in terms of efficiency. This happens as a result of asynchronous request processing. This makes FastAPI superior to Flask for larger-scale machine learning projects, especially enterprise ones, as it can handle requests much more efficiently. FastAPI employs the asyncio module, which enables Python programmers to write concurrent code.

The default interface for Flask, WSGI, handles requests synchronously. This implies that requests are processed in order, and you must wait until the previous task is over. The Flask framework is ideal for users who want to create their own applications. You should use the Flask framework if you have less time and want to create a basic API.

2. FastAPI vs. Flask: Security

The fastapi.security module of FastAPI has several tools for various security mechanisms.

Although Flask is a simple framework, it excels at providing solutions for typical security issues like CSRF, XSS, JSON security, and more. You can implement standard security measures using 3rd party extensions like Flask-Security.

3. FastAPI vs. Flask: Ease of Learning

FastAPI is easy to learn, especially for those without web development experience. However, there aren't many online resources, courses, or tutorials.

Flask is easy to use, and learning its fundamental components is simple. Online materials are also widely available to support learning Flask.

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4. FastAPI vs. Flask: Database Support

Although FastAPI lacks an integrated ORM, it is compatible with Pydantic ORM mode in SQLAlchemy. Numerous NoSQL databases are supported by the Fast API, including MongoDB, ElasticSearch, Cassandra, CouchDB, and ArangoDB.

There is no built-in ORM framework in Flask. Many open source libraries or extensions are available for developers, including Flask-SQLAlchemy, Flask-Pony, etc. The use of open source libraries or extensions supports NoSQL databases, and Flask-PyMong is an excellent option for integrating MongoDB with Flask.

5. FastAPI vs. Flask: Admin Dashboard

FastAPI includes an admin dashboard. It makes use of Swagger as the web user interface for API documentation.

There isn't a built-in admin panel in Flask, but you can use the Flask-Admin extension instead. It supports a variety of backends, including Peewee, MongoEngine, and SQLAlchemy.

Best Practices When Using FastAPI

  • Use Pydantic for data validation.

Pydantic includes a wide range of features for data transformation and validation. Regular features like required and non-required fields with default values are available. Still, Pydantic also includes extensive data processing capabilities like regex, enums for options with a limited range of values, length validation, email validation, etc.

  • Dissociate and reuse dependencies.

You can use dependencies repeatedly without recalculating them since FastAPI caches the results of dependencies inside the scope of a request by default. For example, if you have a dependency that calls the service get post by id, only the first function call will require a database visit.

  • Utilize dependencies when validating data.

Pydantic can validate only the values of client input. Use dependencies to check data against database constraints like "user not found" and "email already exists."Writing tests to verify the post id for each of these routes is no longer necessary due to the use of a shared dependency.

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Best Practices When Using Flask

  • Use Flask-WTF extension for activating CSRF protection.

To secure the app from CSRF, you must globally enable CSRF protection. A hidden input field in each form will include our CSRF protection token, created randomly by the Flask-WTF.

  • Use Jinja2 Auto Escape.

If users follow the status feed page in their browsers, an attacker can run arbitrary JavaScript code on their computers. Set the flask Jinja2 to escape all inputs to mitigate this attack automatically. This is deactivated by default; thus, you are responsible for turning on the Jinja2 auto escape.

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