TensorFlow vs PyTorch

Building machine learning(ML) models from scratch can turn out to be a daunting task, especially for newbies in the field. As a result, ML frameworks came to the rescue by simplifying the process of creating and productionising ML models.

Due to the varieties of frameworks available in the market, all posing as the best fit for our task, there is a need to equip ourselves with the knowledge of the abilities of these frameworks of interest to be able to make the right decision.

In this article, we are going to explore the worlds of two popular machine learning frameworks — TensorFlow and PyTorch. Also, we will be examining their core features as well as their differences.

TensorFlow

Google's brain team built the DistBelief(a deep neural networks-based system)for internal use at Google. The team decided to refactor the code base of the DistBelief into a faster, more robust application-grade library which became TensorFlow. It was later open-sourced in 2015 and since then, it has been used by developers all over the world for research and development of machine learning applications. TensorFlow is mainly suited for production workflows.

Advantages of Tensorflow

TensorFlow has some impressive benefits as listed below:

• Speed - TensorFlow has a pretty fast execution speed compared to some other frameworks because of its static computational graph. It has custom application-specific integrated circuits known as Tensor Processing Unit(TPU) to speed up computations.

• Keras support - Tensorflow has a high-level Application Programming Interface(API) called “Keras”. It enables you to build machine learning models without knowing too much about what is under the hood. Keras simplifies the complexity involved in building ML models.

• Large community support- Tensorflow has a large community of developers and industry professionals actively contributing to the project and utilizing it.

• Visualization capabilities - Tensorflow has a visualization tool called "tensorboard" which allows developers to visualize training.

• Ease of deployment - Tensorflow has made deployment to production environments easier through the use of its custom-built integration known as Tensorflow serving.

• Scalability- Tensorflow has developed an end-to-end platform called Tensorflow Extended (TFX). It aids ingestion of large datasets and deployment of ML models for production. It can scale to large distributed clusters of machines making it possible for you to train it on very massive datasets.

• Compatibility - Many languages, including C++, JavaScript, Python, C#, Ruby, and Swift are supported by TensorFlow. This enables a user to operate in a setting that they are familiar with.

Disadvantages of TensorFlow

Every tool has drawbacks; here are some of TensorFlow’s:

• Debugging - It is not easy to debug as many of the implementations are hidden at lower levels of abstraction.

• Static computational graph- Tensorflow graphs are compiled before the data is fed into the model, thereby making them less flexible — they can not be changed on the fly.

• GPU Support -TensorFlow only supported NVIDIA GPUs and Python for GPU programming, which is a limitation given the rise of alternative deep learning languages.

Let us discuss in detail what is PyTorch, its advantages and disadvantages

PyTorch

PyTorch is also another powerful deep-learning framework based on the Torch library. It was released by the Facebook research team in 2018.

PyTorch appears to be best suited for significantly decreasing the design, training, and testing cycles for new neural networks. As a result, it quickly gained popularity in academic circles. Pytorch is mainly suited for research and where rapid prototyping is required.

Advantages of PyTorch

• Pythonic - Coding in PyTorch is quite easy as the codes are written in Python programming language rather than being an interface to a library written in some language. It was developed for deep integration in Python code.

• Object-oriented Programming- It makes use of a lot of object-oriented programming, thereby making the code cleaner.

• Dynamic graph - It makes use of a dynamic execution graph–the graph can be changed during run time. They are more flexible and easier.

• Debugging- Pytorch allows you to debug easily with popular Python tools.

• ONNX support - ONNX stands for Open Neural Network Exchange. It is an open format for ML models, allowing you to convert between different ML frameworks. For example, TensorFlow to PyTorch, PyTorch to TFlite, etc.

• Torchserve - Torchserve was built as an add-in to PyTorch by the Amazon Web Service to help deploy PyTorch models for serving predictions.

Disadvantages of Pytorch

• Reduced execution speed- Pytorch execution speed is quick as opposed to some other frameworks because of the dynamic nature of its computational graph.

• Visualization - Pytorch has low visualization and monitoring features.

• Scalability - It doesn't have high scalability with very large datasets.

The differences between TensorFlow and PyTorch

TensorFlow and PyTorch are both deep-learning frameworks but they have different features and capabilities. Here are some unique differences between the two frameworks:

Conclusion

In conclusion, choosing the right framework depends on your project needs and what you want to achieve.

TensorFlow and PyTorch have advantages, drawbacks, and use cases for different tasks. TensorFlow is more suited for production use cases while PyTorch is a go-to framework for the research community.

Now that you've understood the differences you can make decisions to fit your project's needs. I can't wait to see the amazing things you will be building.