FTorch

Brief description

It is desirable to be able to run machine learning (ML) models directly in Fortran. ML models are often trained in some other language (say, Python) using a popular frameworks (say, PyTorch) and saved. We want to run inference on this model without having to call a Python executable. To achieve this we use the existing Torch C++ interface, LibTorch.

FTorch provides a library enabling a user to directly couple their PyTorch models to Fortran code. There are also installation instructions for the library and examples of performing coupling.

We support running on both CPU and GPU, and have tested the library on UNIX and Windows based operating systems

Citing FTorch

FTorch is published in JOSS. To cite it in your work please refer to:

Atkinson et al., (2025). FTorch: a library for coupling PyTorch models to Fortran. Journal of Open Source Software, 10(107), 7602, https://doi.org/10.21105/joss.07602

News

Some recent updates about FTorch:

• FTorch developers Jack Atkinson and Joe Wallwork were recently awarded funding from C2D3-Accelerate for a project entitled Online training of large-scale Fortran-based hybrid computational science models, with applications in climate science. This grant will support development for online training functionality for FTorch, as well as research visits, FTorch training tutorials, and organisation of a workshop on hybrid modelling.
• FTorch developer Jack Atkinson will be presenting FTorch in a SciML seminar at CEMAC in Leeds on 18th July 2025.

FTorch training

We offer training on FTorch in the form of tutorials and workshops. The companion repository https://github.com/Cambridge-ICCS/FTorch-workshop provides a set of exercises and solutions to help users get started with FTorch.

Select Presentations

The following presentations contain information about FTorch:

• FTorch: Facilitating Hybrid Modelling
  N8-CIR Seminar, Leeds - July 2025
  Slides - Recording
• Facilitating machine learning in Fortran using FTorch
  Seminars at the University of Reading Data Assimilation Research Centre and ECMWF, Reading - May 2025
  Slides
• FTorch: Enabling Online Training for Large-Scale Fortran Models
  CCfCS Polar Symposium 2025, British Antarctic Survey, Cambridge - May 2025
  Poster
  
• Coupling Machine Learning to Numerical (Climate) Models
  Platform for Advanced Scientific Computing, Zurich - June 2024
  Slides

See the presentations page for a full list of presentations on FTorch.

License

The FTorch source code, related files and documentation are distributed under an MIT License which can be viewed here.

Projects using FTorch

The following projects make use of FTorch.
If you use our library in your work please let us know.

• DataWave CAM-GW - Using FTorch to couple neural net parameterisations of gravity waves to the CAM atmospheric model.
• MiMA Machine Learning - Implementing a neural net parameterisation of gravity waves in the MiMA atmospheric model. Demonstrates that nets trained near-identically offline can display greatly varied behaviours when coupled online. See Mansfield and Sheshadri (2024) - DOI: 10.1029/2024MS004292
• Convection parameterisations in ICON - Implementing machine-learnt convection parameterisations in the ICON atmospheric model showing that best online performance occurs when causal relations are eliminated from the net. See Heuer et al (2024) - DOI: 10.1029/2024MS004398
• In the GloSea6 Seasonal Forecasting Model - Replacing a BiCGStab bottleneck in the code with a deep learning approach to speed up execution without compromising model accuracy. See Park and Chung (2025) - DOI: 10.3390/atmos16010060
• Emulation of cloud resolving models to reduce computational cost in E3SM. See Hu et al. (2025) - DOI: 10.1029/2024MS004618 (and code)