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TensorFlow vs PyTorch: Performance Analysis on Large-Scale Datasets(datascience-blog.com)

160 points by ml_researcher 1 year ago | flag | hide | 13 comments

  • hnuser1 1 year ago | next

    [Opening Comment] TensorFlow vs PyTorch: Performance Analysis on Large-Scale Datasets is an important topic in the Machine Learning community. I'm excited to see the results.

    • tfadvocate 1 year ago | next

      TensorFlow has a more mature ecosystem and offers features such as TensorBoard, which helps create visualizations. This leads to a better overall experience while monitoring.

      • tfadvocate 1 year ago | next

        Sure, PyTorch is easy to learn, but TensorFlow's performance is more consistent during inference, especially in large-scale production environments, making it a better choice.

    • pytorchfan 1 year ago | prev | next

      While TensorFlow is strong with tooling and visualization, PyTorch provides an easier to learn and use API without compromising performance. PyTorch has great community support too.

      • newtoml 1 year ago | next

        Does anyone have concrete performance data that supports either TensorFlow or PyTorch on large-scale datasets like ImageNet?

        • performancedataguru 1 year ago | next

          Yes, I did an analysis with ImageNet recently, and results show that TensorFlow has a model loading time & inference edge over PyTorch, but PyTorch handles complex models better with comparable speed.

          • perfdatainterested 1 year ago | next

            Do you have a link to your analysis? I'd be curious to read what results you obtained.

            • performancedataguru 1 year ago | next

              Please find the analysis at [URL] . I tried to include the most relevant information I could find.

  • hnuser2 1 year ago | prev | next

    What we really need is an industry standard for comparing performance between ML frameworks. It's difficult to get good benchmarks.

    • standardizedbenchmarker 1 year ago | next

      There are already several projects aiming to do just that. MLPerf (<https://mlperf.org/>) is one of the forerunners.

  • hnuser3 1 year ago | prev | next

    TensorFlow was created by the research team behind DeepMind at Google, while PyTorch's creators are at Facebook Research. I wonder if this duality of origins implies the difference between choices.

    • originsagreed 1 year ago | next

      I agree with the difference in origin. Perhaps, TensorFlow focuses on optimizing for large-scale and heavily parallelized calculation whereas PyTorch targets ease of use and research.

    • divideandconquer 1 year ago | prev | next

      Both have their advantages and disadvantages, but a researcher or developer should choose based on their particular needs.