Interesting. If I understand correctly, one way of training the jit model is by defining the identical header file in C and then load the jit model with load instead of jit load.
However, I am also suffering by just using the jit model and train it. The example that @markl shows loaded the parameters, from jit model to the optimizer, and then update it with optimizer. I am wondering whether this could be done as well and whether this is the direction of pytorch.
If this could be done, I think it will be great as any other software could just call the libtorch and perform training and inferencing, making it outstanding compare to tensorflow.
just my 2 cents, apologize me if I have made any statements wrong above.
rgds,
CL