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.
