torch::deploy::InterpreterManager manager(4);
// access one of the 4 interpreters
auto I = manager.acquireOne();
// run infer from your_model.py
I.global("your_model", "infer")({at::randn({10, 240, 320})});
```
Learn more here.
(Beta) CUDA/ROCm/CPU Backends
torch::deploy now links against standard PyTorch Python distributions so all accelerators that PyTorch core supports such as CUDA and AMD/HIP work out of the box.

Can install any device variant of PyTorch via pip/conda like normal.
https://pytorch.org/get-started/locally/

(Prototype) aarch64/arm64 support
torch::deploy now has basic support for aarch64 Linux systems.

We're looking to gather feedback on it and learn more about arm use cases for eager PyTorch models.
Learn more / share your use case at https://github.com/pytorch/multipy/issues/64

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