[snendroid-ai]

I'm using Keras from last 3 years. Most of the time where I have to deal with core TF code is when I have to write some custom layers. I totally agree on a part where hacking together TF code seems nightmare (well, initially.. but not once you know what you're doing), where PyTorch more looks like blissful experience (I have not tried PyT yet, just speaking from reading all these comments). I'm genuinely curious about how one can use the trained PyTorch models in production? For example, I got 6 TF based translation models + 1 classification model running on single AWS instance with TensorFlow Serving with 1 GPU and 8 CPU cores. These 7 models are deployed to take advantage of all the resources of this instance and everything runs smoothly. Now considering I got these same models in PyTorch, what are my options to do the same?

[ericd]

What challenges are you worried about with transferring PyTorch to production? It’s been wonderful to work with, but I haven’t put a PyTorch model in high volume production yet, so I’m curious too.

[snendroid-ai]

1) My laziness to look for what’s available to do this. 2) Core belief to NOT use any product backed by FB.

But anyway, at this point I got so many things already running over TF + Keras that I don’t see any use case of reverting back the entire code base written over 3-4 years to other platform just because new grads from university are using some library more over other. I got everything I need, so why to suffer unnecessarily? I can just spend the same amount of time polishing existing things rather than spending time after something which has less probability to be at same level as existing things.

[ericd]

Yeah, I guess if TF works for you, stick with it. I started learning with Theano+Keras, then with TF, and finally PyTorch, and was much happier when I switched to PyTorch, FWIW. I think it’s worth trying if you haven’t.