FTorch and fortran-tf-lib
These are complementary tools that enable Fortran applications to leverage different deep learning backends—one for PyTorch models and one for TensorFlow/Keras models—allowing developers to choose or combine frameworks based on their specific ML needs.
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 fortran-tf-lib
Cambridge-ICCS/fortran-tf-lib
A library for directly calling TensorFlow / Keras ML models from Fortran.
This tool helps scientific researchers and engineers integrate pre-trained machine learning models, specifically those developed with TensorFlow, directly into their existing Fortran applications. You can take a TensorFlow model saved in Keras's 'tf' format, feed it input data from your Fortran code, and receive the model's predictions back within Fortran. This is ideal for Fortran developers working in fields like computational science or engineering who want to embed ML inference capabilities into their high-performance Fortran simulations.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work