however I think you could lose a lot of performance, since the models would have to wait for each other to finish the processing.


What kind of deployment environment do you have?

I used a local machine and it was OK for other frameworks. E.g., in Tensorflow I created two different sessions and used them in the “production pipeline”. So each session was used individually and they were manually allocated in memory. Is it possible to make such a trick with pytorch or onnx + caffe2?

PyTorch 1.0 will support easy deployment with Caffe2 as stated here.

As I understood, release 1.0 will just simplify the onnx+caffe2 conversion. Am I wrong and it will be another solution? Anyway, I would like to find an alternative 1.0 solution now, if it possible