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PyTorch vs. TensorFlow: A Comprehensive Comparison in 2024

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When it comes to deep learning frameworks, PyTorch and TensorFlow are two of the most prominent tools in the field. Both have been widely adopted by researchers and developers alike, and while they share many similarities, they also have key differences that make them suitable for different use cases.

We thought this blog would be timely especially with the PyTorch 2024 Conference right around the corner.

In this blog, we’ll explore the main differences between PyTorch and TensorFlow across several dimensions such as ease of use, dynamic vs. static computation, ecosystem, deployment, community, and industry adoption. In a follow-on blog, we will describe how Rafay’s customers use both PyTorch and TensorFlow for their AI/ML projects.

PyTorch vs TensorFlow

Announcing Rafay's Templates for AI and Generative AI

We constantly hear from our customers about wanting their developers to experiment with Generative AI. No organization wants to be left behind and they are all trying to find ways to empower their developers and application teams to be able to experiment with use cases powered especially by Generative AI.

According to recent Gartner research, >80% of enterprises will have used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026.

We have been listening to our customers and are happy to announce Rafay's Templates for AI & Generative AI. Platform teams can now provide their developers with a self service experience for infrastructure so that developers can experiment with new and innovative AI and Generative AI use cases.

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