Automatic productionizability of PyTorch models through Caffe 2 will speed the transition from research to production dramatically - this gives the environment a chance to compete with Tensorflow / Tensorflow Serving.
Frameworks (say, DL4J) have been using Keras as a loose way to share models between frameworks. It'll be fascinating to see if Theano / Tensorflow / DL4J / MXNet walk this path as well.
I was curious about their implementation since PyTorch and Caffe2 semantics are very different. Unfortuantly, the authors write:
> Currently, our tracer works with many common neural networks, but not some of the more advanced programs in PyTorch such as those with dynamic flow control. Over time, we will enhance ONNX and the tracer to support these programs, so that developers can leverage full flexibility of PyTorch with the high-performance robust deployment capabilities of Caffe2.
It is useful, of course. But it's rare for contemporary models to not use dynamic flow. In fact, PyTorch is popular because it encourages this dynamism.
Frameworks (say, DL4J) have been using Keras as a loose way to share models between frameworks. It'll be fascinating to see if Theano / Tensorflow / DL4J / MXNet walk this path as well.