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If youre familiar with deep learning, youll have likely heard the phrase PyTorch vs. TensorFlow more than once.
PyTorchandTensorFloware two of the most popular deep learning frameworks.
In this article, well first introduce the two frameworks: PyTorch and TensorFlow.
And then sum up the features they offer.
What Is PyTorch?
Recently, Meta AI released PyTorch 2.0.
What Is TensorFlow?
Introduced in 2014,TensorFlowis an open-source end-to-end machine learning framework by Google.
It comes packed with features for data preparation, model deployment, and MLOps.
With TensorFlow, you get cross-platform development support and out-of-the-box support for all stages in the machine learning lifecycle.
PyTorch vs. TensorFlow
Both PyTorch and TensorFlow are super popular frameworks in the deep learning community.
For most applications that you want to work on, both these frameworks provide built-in support.
#1.Library of Datasets and Pretrained Models
A deep learning framework should come with batteries included.
Oftentimes, you wouldnt want to code a model from scratch.
Rather you’re able to leverage pre-trained models and fine-tune them to your use case.
Similarly, wed want commonly used datasets to be readily available.
PyTorch has libraries such astorchtext,torchaudio, andtorchvisionfor NLP, audio, and image processing tasks, respectively.
A machine learning model that works great in your local development environment is a good starting point.
TensorFlow Extended, abbreviated astfx, is a deployment framework that is based on TensorFlow.
It provides functionality that helps you orchestrate and maintain machine learning pipelines.
It provides features for data validation and data transformation, amongst others.
WithTensorFlow Serving, it’s possible for you to deploy machine learning models in production environments.
Theres a common opinion that PyTorch is popular in the research community while TensorFlow is popular in the industry.
However, recently, both these frameworks have found widespread use.
In addition, you might also useTensorFlow Litefor deploying machine learning models on mobile and other edge devices.
Despite both frameworks providing deployment support, TensorFlow natively supports model deployment.
It is, therefore, the preferred choice in production environments.
Therefore, interpretability is super important to deep learning and to better understanding the working of neural networks.
And well see what features PyTorch and TensorFlow offer for the same.
PyTorch Captum, the model interpretability library for PyTorch, provides several features for model interpretability.
Lets proceed to another important aspect privacy.
However, this comes with the downside that the privacy of the data is lost.
Recently, thereve been significant advancements around privacy-preserving machine learning techniques such asdifferential privacyandfederated learning.
Differentially private model training ensures that individual records while still learning useful information about the dataset as a whole.
AndPyTorch Opacusallows you to train models with differential privacy.
To learn how to implement differentially private model training, check out theintroduction to Opacus.
Federated learning removes the need for a centralized data collection and processing entity.
In a federated setting, the data never leaves the owner or premise.
Therefore, federated learning facilitates better data governance.
TensorFlow Federatedprovides functionality to train machine learning models on decentralized data.
#5.Ease of Learning
PyTorch is a Pythonic deep-learning framework.
On the other hand, with TensorFlow, you’re free to use the Keras API.
This high-level API abstracts away some of the low-level implementation details.
PyTorch vs. TensorFlow: An Overview
So far, weve discussed the features of PyTorch and TensorFlow.
Choosing the optimal framework will depend on the project youre working on.
In addition, this would require you to factor in the support for deployment, explainability, and more.
Are you aPython programmerlooking to learn these frameworks?
If so, you’re able to consider exploring one or more of the resources shared above.
And if youre interested in NLP, check out this list of natural language processing courses to take.
