Why Developers Liked Pytorch
Python developers have long been looking for the perfect deep learning library that allows them to build and train neural networks with ease. Two of the most popular deep learning frameworks are PyTorch and TensorFlow. While TensorFlow has been around longer, PyTorch has gained a significant following and even surpassed TensorFlow in terms of popularity among developers.
But, what is it that makes PyTorch so attractive to developers? What does it offer that TensorFlow doesn’t? Let’s dive into the reasons behind PyTorch’s rise in popularity.
Imperative Style Programming 🎯
One of the main reasons developers prefer PyTorch over TensorFlow is its imperative style programming, also known as eager execution or dynamic computation graph. This means that operations are executed as they are called, and the computation graph is built dynamically during runtime. This makes it easier to debug and understand your code, as it behaves just like regular Python code.
In contrast, TensorFlow 1.x used a declarative programming style, where developers had to first define the computation graph and then execute it. This made it less intuitive, harder to debug, and more difficult to understand.
With TensorFlow 2.x, eager execution was introduced as the default behavior, making it more similar to PyTorch. However, PyTorch’s simplicity and ease of use have already won the hearts of many developers. ❤️
Pythonic API 🐍
PyTorch’s API is designed to be more Pythonic and closely aligned with the Python programming style. This means that developers can easily understand and use PyTorch without having to learn a completely new way of thinking or coding.
TensorFlow, on the other hand, had a steeper learning curve due to its less Pythonic API, which made it harder for developers to pick up and start using right away. While TensorFlow 2.x has made significant improvements in this regard, PyTorch still holds an edge in terms of simplicity and ease of use.
Dynamic Computation Graphs 🌐
PyTorch’s support for dynamic computation graphs allows developers to change the graph structure during runtime. This is particularly helpful for tasks involving variable-length input sequences or recurrent neural networks (RNNs), as the graph can be adjusted on-the-fly to accommodate different input sizes.
TensorFlow 1.x used static computation graphs, which could be limiting for certain types of models and use cases. However, TensorFlow 2.x introduced support for dynamic computation graphs, making it more competitive with PyTorch in this aspect. Nonetheless, PyTorch’s earlier adoption of this feature has contributed to its popularity.
Community and Research Adoption 🌍
PyTorch has been widely adopted by the research community, thanks to its flexibility, ease of use, and dynamic computation graphs. Many cutting-edge research papers now release their code in PyTorch, which has helped fuel its growth among developers.
The strong community support also means it’s easy to find resources, tutorials, and pre-trained models to get started with PyTorch quickly. While TensorFlow also has a large community, the research community’s shift towards PyTorch has definitely played a role in its rise in popularity.
Conclusion 🏁
In summary, developers prefer PyTorch over TensorFlow due to its imperative style programming, Pythonic API, dynamic computation graphs, and strong community support. While TensorFlow has made significant improvements in version 2.x to address these issues, PyTorch’s simplicity and ease of use have endeared it to the hearts of many developers. As deep learning continues to evolve, it will be interesting to see how these two frameworks continue to compete and adapt to the needs of the community.