FTorch and pytorch-fortran
These are competing approaches to the same problem: both provide Fortran interfaces to PyTorch models, with FTorch offering a more feature-complete library-based solution while pytorch-fortran provides lower-level bindings, making them alternative choices rather than tools meant to be used together.
About FTorch
Cambridge-ICCS/FTorch
A library for directly calling PyTorch ML models from Fortran.
This library helps scientists and engineers who use Fortran for high-performance computing integrate modern machine learning models into their existing simulations. You can take a PyTorch model trained in Python, feed it into your Fortran code, and get predictions back, all within the same Fortran application. This is ideal for researchers in fields like physics, climate modeling, or aerospace where Fortran is prevalent.
About pytorch-fortran
alexeedm/pytorch-fortran
Pytorch bindings for Fortran
This project helps Fortran HPC developers integrate PyTorch deep learning capabilities directly into their existing Fortran codebases. You can define a neural network model in Python, load it into Fortran, pass in Fortran arrays as input, run inference or training, and receive the output as native Fortran arrays. This allows Fortran developers to leverage PyTorch's optimized deep learning framework without rewriting their high-performance computing applications.
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