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As ML becomes "commoditized" easy integration with other tools will likely enable completely new applications. In this sense pytorch has an edge as far the pyth

As ML becomes "commoditized" easy integration with other tools will likely enable completely new applications. In this sense pytorch has an edge as far the python ecosystem goes, where it is more "native". But ultimately it will depend on what kind of such ML enabled "killer apps" become popular. Maybe they live in the browser or in some IoT and thus give the edge to js or c++ implementations rather than python.

jstx1 4y ago
I don't get the more native part - you can call a TensorFlow or a PyTorch model and get results from either in Python - what kind of integration are you imagining where the internal implementation of the model makes a difference?
- college_physics 4y ago
  I think people mostly refer to the debugging part while developing, not any other capabilities or API issues. So for example building a larger (monolithic) application entirely in python, where ML is only a part might be easier using pytorch.
  - jstx1 4y ago
    Why though? What specific thing does PyTorch do that TensorFlow doesn't do in a large monolithic Python application?
    - college_physics 4y ago
      Like said, debugging that application is one difference [0] [0] https://sites.google.com/site/wildml2016nips/SculleySlides2.pdf https://sites.google.com/site/wildml2016nips/SculleySlides2....
      - albertzeyer 4y ago
        This is from 2016, i.e. really ages ago. This is for TensorFlow 1, graph mode. Now with eager mode TensorFlow 2, there is not much difference between debugging TensorFlow vs debugging PyTorch.
albertzeyer 4y ago
But it's easy to develop any such killer apps in any of the ML frameworks. And there will not be a single such killer app, but there are many. It's more a question what most of the community uses, esp the research community uses, as that is what gets into production first at some point. If you have your nice model already in PyTorch, why recode it in TF later? So, the ML framework adoption always has a bias towards what's more friendly for the researchers, and developers.

jstx1 4y ago

I don't get the more native part - you can call a TensorFlow or a PyTorch model and get results from either in Python - what kind of integration are you imagining where the internal implementation of the model makes a difference?

college_physics 4y ago
I think people mostly refer to the debugging part while developing, not any other capabilities or API issues. So for example building a larger (monolithic) application entirely in python, where ML is only a part might be easier using pytorch.
- jstx1 4y ago
  Why though? What specific thing does PyTorch do that TensorFlow doesn't do in a large monolithic Python application?
  - college_physics 4y ago
    Like said, debugging that application is one difference [0] [0] https://sites.google.com/site/wildml2016nips/SculleySlides2.pdf https://sites.google.com/site/wildml2016nips/SculleySlides2....
    - albertzeyer 4y ago
      This is from 2016, i.e. really ages ago. This is for TensorFlow 1, graph mode. Now with eager mode TensorFlow 2, there is not much difference between debugging TensorFlow vs debugging PyTorch.

albertzeyer 4y ago

But it's easy to develop any such killer apps in any of the ML frameworks. And there will not be a single such killer app, but there are many. It's more a question what most of the community uses, esp the research community uses, as that is what gets into production first at some point. If you have your nice model already in PyTorch, why recode it in TF later? So, the ML framework adoption always has a bias towards what's more friendly for the researchers, and developers.