tch-rs

Provides thin wrappers to PyTorch's C++ API, enabling tensor operations, automatic differentiation, and neural network building, as shown in the MNIST training example.

Combines Rust's memory safety with PyTorch's optimized backend, allowing efficient model training and inference without sacrificing reliability, leveraging libtorch's performance.

Includes utilities to load and run models like ResNet with extracted weights, and supports SafeTensors for secure, zero-copy imports from PyTorch, as detailed in the examples.

Comes with diverse examples for tasks such as MNIST classification, style transfer, and reinforcement learning, facilitating quick adoption and experimentation.