roaffix:

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?


You could just create several different models, push them onto the GPU and feed your data.
I suppose Tensorflow is doing the same.
However, since the GPU has limited resources, your performance might be limited, since the models might have to wait for each other.
You could try to use the CPU instead, if you have single input images for example.
As a side note: maybe glow 49 might be interesting for you.



 roaffix:

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 


Yeah, that’s also how I understand it.