Rust Bindings for PyTorch | Generated by AI
Introduction to tch-rs
tch-rs is a Rust crate that provides bindings for the C++ API of PyTorch, specifically wrapping around the libtorch library. Its main purpose is to enable Rust developers to leverage PyTorch’s powerful tensor computations, automatic differentiation, and machine learning capabilities directly in Rust applications. By offering thin, low-level wrappers that mirror the original C++ API, tch-rs allows for the creation of more idiomatic Rust abstractions on top of it, making it easier to perform tasks like model training, inference, and tensor manipulations without leaving the Rust ecosystem.
Key Features
- Tensor Operations and Autograd: Supports core tensor arithmetic, gradient computation, and backpropagation for training models via optimizers like Adam.
- Neural Network API: Includes tools for building and training neural architectures, with examples such as a simple feedforward network on the MNIST dataset.
- Model Loading: Allows importing pre-trained PyTorch models using the safetensors format, which is efficient and avoids Python dependencies.
- Examples and Use Cases: Comes with practical demos for basics like tensor creation, gradient descent training, custom neural nets, and running pre-trained models (e.g., ResNet18 for image classification).
- Cross-Platform Support: Works on various systems, with options for CPU or GPU (CUDA) acceleration.
The project is actively maintained and licensed under Apache-2.0 and MIT, drawing some code generation from related projects like ocaml-torch.
Getting Started
To use tch-rs, you’ll need libtorch (PyTorch’s C++ distribution) installed on your system. Add it to your Rust project via Cargo:
[dependencies]
tch = "0.17.2" # Check for the latest version
Set up libtorch by downloading it manually, using a system installation, or enabling features like download-libtorch for automatic fetching. For GPU support, specify CUDA versions (e.g., via TORCH_CUDA_VERSION=cu117). Run examples with commands like cargo run --example basics. Note that Windows users should prefer the MSVC toolchain, and static linking is available via environment variables.
For more details, explore the documentation and examples provided in the repository.
tch-rs GitHub Repository
tch-rs Documentation