In this article, we’ll aim to analyze some of the most significant similarities and differences between PyTorch and TensorFlow, two well-known deep learning frameworks. Why just those two instead of the others?
The two Deep Learning frameworks with the greatest popularity now are PyTorch and TensorFlow. There has long been a heated argument over which framework is best, and both sides have their share of ardent adherents.
The argument landscape is always changing since PyTorch and TensorFlow have both evolved so swiftly throughout the course of their very brief lives. There is a lot of outdated or missing material, which further muddles the debate about whether framework is superior in a particular field.
While PyTorch and TensorFlow have a reputation for being research- and industry-focused frameworks, respectively, we’ll discover that these perceptions are partially based on out-of-date knowledge. The debate about which framework is superior is significantly more complex.
Tensorflow
TensorFlow is a Google-developed end-to-end open-source deep learning framework that was launched in 2015. It’s well-known for its documentation and training assistance, scalable production and deployment choices, many abstraction levels, and support for various platforms including Android.
Pros
- A straightforward built-in high-level API.
- Tensorboard: Training visualization.
- Production-ready.
- Simple mobile support.
- Open source.
- Excellent documentation and community support
Cons
- Unintuitive method of debugging
- It is difficult to make quick modifications.
PyTorch
PyTorch is a new deep learning framework built on Torch. It was created by Facebook’s AI research team and released on GitHub in 2017. PyTorch is known for its ease of use, simplicity, flexibility, efficient memory utilization, and dynamic computational graphs. It also feels more natural, making coding easier and enhancing processing speed.
Pros
- Python-style coding
- Editing is simple and quick.
- Excellent documentation and community support
- Open source
- There are numerous projects that make use of PyTorch.
Cons
- Not production ready
- Need third party app for training visualization
Final words
The PyTorch vs TensorFlow discussion is multifaceted, with a continually shifting landscape, and outdated knowledge makes grasping this landscape even more challenging. PyTorch and TensorFlow are both fairly mature frameworks in 2022, with significant overlap in their basic Deep Learning functionality. Today, the practical issues of each framework, such as model availability, deployment time, and associated ecosystems, trump technical distinctions.