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My very biased opinion: you start with PyTorch because it's easy to develop and debug, and there's no point in having the fastest tools for a model that you can
by probably_wrong 7y ago
My very biased opinion: you start with PyTorch because it's easy to develop and debug, and there's no point in having the fastest tools for a model that you can't train properly.
Once your model is running, and if/when you start hitting performance bottlenecks, then you consider migrating your model to TensorFlow.
- acgan 7y agoYup, this echoes the philosophy at e.g. Tesla. Development speed matters more than performance at first.
- albertzeyer 7y agoBut isn't TF eager mode just as easy to develop and debug, and the migration of TF eager mode to TF static mode is then probably simpler?
- reubenmorais 7y agoTF eager mode has been a stable/supported thing for 10 days, since the release of 2.0. Before then it was available as opt-in behavior that once enabled meant all bets were off for if things would work or explode. So I think it's too early to answer your question. Maybe 2.0 bridges the gap to PyTorch in development speed. But maybe the momentum has already shifted to PyTorch.