The Pytorch vs. TensorFlow debate has been a hot topic for deep learning engineers. Making the right choice for your project can be the difference between success and failure. But with so many personal preferences and biases in mind, how can you make an informed decision?
First, TensorFlow and PyTorch provide access to a wide range of pre-trained models, which are stored in TensorFlow Hub and PyTorch Hub, respectively. These are repositories of trained models that can be inferred, fine-tuned, and deployed. With just a few imports and a few lines**, you can use the pre-trained Mask RCNN for image segmentation and select your preferred backbone model.
It's clear,PyTorch is the preferred choice for most researchers。SwithCode's data shows that 68% of all published articles use the framework. Only about 30% of the cases have at least one implementation on GitHub repositories, and it's reasonable to assume that this distribution is consistent across frameworks.
PyTorch is the leader in the Transformer space on Huggingface. Consider that 64% of all available TensorFlow and Keras models are already available for PyTorch.
It is a testament to PyTorch's dominance in the field and its ability to future-proof itself with a large selection of transformer models. This makes a significant difference when you want to fine-tune and train a model with a custom loop;Otherwise, the HuggingFace API can solve most of the difficulties.
Both TensorFlow and PyTorch provide useful abstractions that simplify model development by reducing boilerplate. They differ in that PyTorch has a more "Pythonic" approach and is object-oriented, while TensorFlow offers a variety of options.
PyTorch is used in many deep learning projects today, and it is gaining popularity among AI researchers, although it is the least popular of the three main frameworks. Trends suggest that this may soon change.
When researchers need flexibility, debugging capabilities, and short training times, they choose PyTorch. It can run on Linux, macOS, and Windows.
With its well-documented framework, as well as its abundance of well-trained models and tutorials, TensorFlow is a favorite tool for many industry professionals and researchers. TensorFlow provides better visualization that enables developers to better debug and track the training process. However, PyTorch only provides limited visualization.
Thanks to the TensorFlow Serving framework, TensorFlow also beats PyTorch when it comes to deploying trained models to production. PyTorch doesn't provide such a framework, so developers need to use Django or Flask as backend servers.
When it comes to data parallelism, PyTorch relies on Python's native support for asynchronous execution for the best performance. However, with TensorFlow, you have to manually code and optimize each operation that runs on a particular device to allow for distributed training. In summary, you can copy everything in Pytorch in TensorFlow;You just have to work harder.
If you're just starting to explore deep learning, you should learn PyTorch first, as it's popular in the research community. However, if you're familiar with machine learning and deep learning, and you're focused on finding a job in the industry as quickly as possible, learn TensorFlow first.
Beginners: Starting with keras is the most suitable option. It saves you from dealing with many of the difficulties of deep learning. However, if you want to dig deeper (which you have to do at some point), go for pytorch.
Researcher: Pytorch might be a better choice - unless you're working in a niche where adoption rates can vary!
Working in a production environment:
If you're deploying a model in production, TensorFlow and its TFX-TFLite ecosystem offer a more comprehensive solution and are more widely recognized in the industry.
However, if you're willing to have a second ecosystem in your tech stack, you can still model it with Pytorch and convert it to TensorFlow using Onnx.