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Not a machine learning guy, but are the internals of the popular frameworks (TensorFlow, PyTorch, etc.) vastly different? Are they mostly different in algorithm

Not a machine learning guy, but are the internals of the popular frameworks (TensorFlow, PyTorch, etc.) vastly different? Are they mostly different in algorithm implementations, or are there more philosophical differences?

p1esk 7y ago
Short answer: they started out conceptually different (static vs dynamic graph), but seem to be converging lately (no pun intended!)
- amelius 7y ago
  As in PT moving to TF, or TF moving to PT, or both?
  - m0zg 7y ago
    More TF moving to PT IMO. Dynamic graph is a superior paradigm for research (where the users of these frameworks tend to spent the vast majority of their time), and it can be traced and exported for inference as needed.