FTorch has supported offline training of ML models for some time. We are currently working on extending its functionality to support online training, too. This will involve exposing the automatic differentiation and back-propagation functionality in PyTorch/LibTorch.
In the following, we document a workplan of the related functionality. Each step below will be updated upon completion.
Mathematical operators involving Tensors are overloaded, so that we can compute expressions involving outputs from one or more ML models.
Whilst it's possible to import such functionality with a bare
use ftorch
statement, the best practice is to import specifically the operators that you
wish to use. Note that the assignment operator = has a slightly different
notation:
use ftorch, only: assignment(=), operator(+), operator(-), operator(*), &
operator(/), operator(**)
For a concrete example of how to compute mathematical expressions involving Torch tensors, see the associated worked example.
requires_grad propertyNot yet implemented.
backward operatorNot yet implemented.