README

This example project demonstrates using the wasi-nn API to perform PyTorch based inference. It consists of Rust code that is built using the wasm32-wasip1 target.

To run this example:

1. Ensure you set appropriate Libtorch environment variables according to tch-rs instructions.
   • Requires the C++ PyTorch library (libtorch) in version v2.4.0 to be available on your system.
   • export LIBTORCH=/path/to/libtorch
2. Build Wasmtime with wasmtime-wasi-nn/pytorch feature.
3. Navigate to this example directory crates/wasi-nn/examples/classification-example-pytorch.
4. Download squeezenet1_1.pt model

curl https://github.com/rahulchaphalkar/libtorch-models/releases/download/v0.1/squeezenet1_1.pt --output fixture/model.pt -L

4. Build this example cargo build --target=wasm32-wasip1.
5. Run the generated wasm file with wasmtime after mapping the directory containing squeezenet1.1 model.pt and sample image kitten.png
   Copy

   ${Wasmtime_root_dir}/target/debug/wasmtime -S nn --dir ${Wasmtime_root_dir}/crates/wasi-nn/examples/classification-example-pytorch::. ${Wasmtime_root_dir}/crates/wasi-nn/examples/classification-example-pytorch/target/wasm32-wasip1/debug/wasi-nn-example-pytorch.wasm
   

6. Check that result 281 has highest probability, which corresponds to tabby cat.