A Comparative Survey of PyTorch vs TensorFlow for Deep Learning: Usability, Performance, and Deployment Trade-offs

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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PyTorch vs. TensorFlow: A Developer's Dilemma

This paper presents a comparative analysis of two prominent deep learning frameworks, PyTorch and TensorFlow, exploring their usability, performance, and deployment aspects. It finds that PyTorch prioritizes ease of use and dynamic model building while TensorFlow excels in production deployment and ecosystem support. The survey suggests choosing a framework based on project-specific needs, acknowledging strengths in both.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Lack of Original Research

The survey relies heavily on external sources and does not involve new benchmarking or experimentation, potentially limiting the novelty of insights.

Limited Scope of Framework Comparison

Focusing primarily on PyTorch and TensorFlow overlooks other relevant frameworks like JAX, potentially biasing the scope of comparison.

Potential for Rapid Obsolescence

Rapid advancements in the field might render some of the surveyed information quickly outdated given the fast release cycles of both frameworks.

Rating Explanation

This survey offers a comprehensive comparison of PyTorch and TensorFlow, covering key aspects relevant to developers. While it doesn't present new experimental findings, its synthesis of information from various sources provides valuable insights. The lack of original research and focus on only two frameworks are minor limitations, but the overall quality and depth make it a strong resource.

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