Question 1

How to install Torch.rb on Ubuntu with GPU support?

Accepted Answer

First, install CUDA and cuDNN, then download the matching LibTorch version for your CUDA setup, configure the gem with the LibTorch path, and reinstall. The README provides specific commands and notes on checking CUDA availability with Torch::CUDA.available?

Question 2

Torch.rb vs PyTorch: which is better for Ruby projects?

Accepted Answer

Torch.rb is ideal if you're committed to Ruby and need deep learning within that ecosystem, offering a similar API. However, PyTorch in Python has a larger community, more models, and better documentation, so switch if ecosystem breadth is critical.

Question 3

Does Torch.rb work on Mac M1 or M2 chips?

Accepted Answer

Yes, it supports Apple silicon via Metal Performance Shaders (MPS). Check availability with Torch::Backends::MPS.available? and move models to the GPU using net.to(device) with Torch.device("mps"), as explained in the Mac performance section.

Question 4

Can I use pre-trained PyTorch models with Torch.rb?

Accepted Answer

Yes, but with caveats. You can load PyTorch-saved state dictionaries, but due to LibTorch bugs, you need to convert parameters to tensors in Python first, as described in the saving and loading models section.

Question 5

What is the performance difference between Torch.rb and Python PyTorch?

Accepted Answer

Performance is comparable since both use LibTorch for tensor computations. The main differences arise from Ruby's runtime overhead, but GPU acceleration via CUDA or MPS helps mitigate this, especially for training neural networks.

Question 6

How to save and load models in Torch.rb for deployment?

Accepted Answer

Use Torch.save(net.state_dict, "net.pth") to save and Torch.load("net.pth") with net.load_state_dict to load. Ensure the model is in eval mode with net.eval, and follow the conversion steps if loading from Python.

Question 7

Is Torch.rb suitable for production machine learning pipelines?

Accepted Answer

It can be, especially for Ruby-centric deployments, but consider the maturity: limited Windows support, dependency on LibTorch compilation, and a smaller ecosystem might require extra effort compared to Python-based solutions.

torch-rb

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