Is Theano a Dying Deep Learning Library?

As of my last update in August 2023, Theano has largely been considered deprecated. The official development of Theano was halted in 2017, and although some forks and community-driven versions have emerged, the library is not actively maintained by its original developers. This article aims to provide an unbiased perspective on the status of Theano and its relevance in the current deep learning landscape.

Is Theano Really Dying?

The real debate seems to stem from differing views on Theano's future. Some argue that Theano is already dead, while others believe it has potential for continued use. For instance, Developer123 (from the referenced Google Groups and MILA’s Theano Google group), expressed deep sadness upon reading about the end of Theano development. Personally, he/ she stated, "I built my first DNNs with Theano/Lasagne. Other libraries do it better these days."

Official Announcements and Update

The official stance on Theano is clear: the developers have announced that they will no longer actively maintain or develop Theano any further. This news may come as a blow to those who relied on Theano, but it leaves important questions unanswered. Has Theano truly reached its end, or is it simply being phased out in favor of newer, more robust frameworks?

Advantages of Theano

Despite the deprecation announcement, many still find value in using Theano. Data Scientist JohnDoe from a company that uses Theano and Lasagne, offered a detailed counter-argument. He noted that:

The Theano API has remained stable for a long time, making it easy to maintain and transition code. Neural networks written on Theano can work seamlessly with newer versions, without encountering bugs. Many papers can be implemented using Theano, with few features missing. Github activity for Theano remains high, indicating ongoing contributions and user engagement. Theano was significant in democratizing deep learning before TensorFlow's rise and had support when TensorFlow did not. While TensorFlow received more marketing and hype, Theano's true fanbase remains dedicated and active. MILA's recent funding of 300 million dollars could sustain Theano’s development. There is a strong community supporting Theano, which attracts developers and maintains active efforts.

JohnDoe's sentiments echo those of many researchers and practitioners who have invested significant time and resources into the Theano ecosystem.

Transition to TensorFlow and PyTorch

While Theano remains a viable option for some, it is important to consider the broader deep learning ecosystem. TensorFlow and PyTorch, which are now widely adopted, offer several advantages:

Robust Ecosystem: TensorFlow and PyTorch have extensive community support and a wide range of functionalities. Ongoing Support: These frameworks continue to receive active development, with regular updates and improvements. Ease of Use: TensorFlow and PyTorch are designed to be user-friendly, especially for beginners. Comprehensive Documentation: Both frameworks have extensive documentation, making it easier for developers to get started and integrate new features.

Given the current landscape, for new projects, it is generally advisable to consider TensorFlow or PyTorch, as they better align with modern deep learning practices and community support.

Theano in 2020

JohnDoe acknowledged the practical considerations but shared that he and his startup had shifted completely to PyTorch by 2020. Despite the initial advocacy for Theano, personal experiences and the evolving technical landscape often drive these transitions. PyTorch has shown a significant advantage in terms of ease of use and community adoption.

In conclusion, while Theano holds historical significance and has a strong community base, the question of whether it is dying is multifaceted. For new projects, the choice between Theano, TensorFlow, and PyTorch should be guided by the specific needs of the project, the community support available, and the developer’s familiarity with the framework.

Key Takeaways:

Theano is largely considered deprecated due to lack of active development and maintenance. Despite its deprecation, Theano remains a stable and feature-rich option for existing projects. TensorFlow and PyTorch are widely adopted due to their active development and extensive community support.