alexeedm/pytorch-fortran: Version v0.3

Pytorch Fortran bindings

The goal of this code is to provide Fortran HPC codes with a simple way to use Pytorch deep learning framework.
We want Fortran developers to take advantage of rich and optimized Torch ecosystem from within their existing codes.
The code is very much work-in-progress right now and any feedback or bug reports are welcome.

Features

• Define the model conveniently in Python, save it and open in Fortran
• Pass Fortran arrays into the model, run inference and get output as a native Fortran array
• Train the model from inside Fortran and save it
• Run the model on the CPU or the GPU with the data also coming from the CPU or GPU
• Use OpenACC to achieve zero-copy data transfer for the GPU models
• Focus on achieving negligible performance overhead

Changelog

v0.3

• Changed interface: forward and train routines now accept torch_tensor_wrap instead of just torch_tensor. This allows a user to add multiple inputs consisting of tensors of different size and scalar values
• Fixed possible small memory leaks due to tensor handles
• Fixed build targets in the scripts, they now properly build Release versions by default
• Added a short API help