TensorFlow and PyTorch are two of the most popular frameworks in deep learning. Both are open-source software. TensorFlow is based on Theano and is developed by Google’s brain team. PyTorch on the other hand is based on Torch and was initialized in Facebook’s AI Research Lab.
Ease of Use
TensorFlow was a low-level deep learning framework. Now it has upgraded to TensorFlow 2.0 by introduction of Keras. Now both frameworks are more user-friendly and easy to apply for daily jobs. PyTorch has a more flexible environment. The difference between the two being: PyTorch requires few more lines of code than it’s counterpart. For example, a small training loop may require five lines of code in PyTorch, whereas only one line code in TensorFlow.
Defining Graph with TensorFlow and PyTorch
TensorFlow helps in drawing a computation graph with the help of TensorBoard. It also observes the behaviour of the training parameters over time and also helps in debugging. Computation graphs are defined statically when code is executed in TensorFlow. The main advantage of having a computational graph is allowing parallelism or dependency driving scheduling. This makes training faster and more efficient.
PyTorch, on the other hand can generate dynamic graphs on-the-go. It can also provide static graphic framework when required. The framework is more tightly integrated in PyTorch due to it’s pythonic structure. PyTorch uses Matplotlib and it feels more native.
Distributed Training with TensorFlow and PyTorch
Data parallelism is a feature which can distinguish PyTorch from TensorFlow. TensorFlow requires manual coding and fine-tuning for every operation. But we can replicate everything in PyTorch from TensorFlow.
Production Deployment in TensorFlow and PyTorch
When it comes to deployment in production, TensorFlow aces the game. It is production-ready. It runs smoothly in cloud. TensorFlow works well on lightweight platforms such as mobile phones or IoT devices. This is possible with the help of TensorFlow Lite. Model is implemented without much loss in model accuracy. It is easy to run on Tensor Processing Unit (TPU).
However, PyTorch is not yet ready with such a versatile tool yet. Thus here, TensorFlow is a clear winner.
Both TensorFlow and PyTorch are great in their own ways. When picking on the better one, we need to focus on the type of job that needs to be done. The main difference between TensorFlow and PyTorch is the execution of code. If we want to build AI-related products, then TensorFlow is a good choice. Whereas, research-oriented developers prefer PyTorch as it supports dynamic training.
