Torch.rb brings PyTorch’s powerful deep learning framework to Ruby, providing bindings to LibTorch for advanced AI model training and inference.
It allows Rails developers to experiment with neural networks, tensors, and GPU-accelerated computations without leaving the Ruby ecosystem.
Setup requires adding the gem and installing the LibTorch library, after which you can define models and run training loops in Ruby code.
Strengths include giving Ruby developers access to PyTorch-level performance and flexibility, while weaknesses include a steeper learning curve and smaller community compared to Python.
Alternatives include exporting trained Python models to ONNX and consuming them in Rails, or using cloud APIs for inference instead of running models locally.
It allows Rails developers to experiment with neural networks, tensors, and GPU-accelerated computations without leaving the Ruby ecosystem.
Setup requires adding the gem and installing the LibTorch library, after which you can define models and run training loops in Ruby code.
Strengths include giving Ruby developers access to PyTorch-level performance and flexibility, while weaknesses include a steeper learning curve and smaller community compared to Python.
Alternatives include exporting trained Python models to ONNX and consuming them in Rails, or using cloud APIs for inference instead of running models locally.