A Rust library that provides Rust to WebAssembly developers with syntax for using tensorflow functionality when their Wasm is being executed on SecondState's SSVM.
From a high-level overview here, we are essentially building a tensorflow interface that will allow the native operating system (which SSVM is running on) to play a part in the runtime execution. Specifically, play a part in using tensorflow with graphs and input and output tensors as part of Wasm execution.
Developers will add the ssvm_interface_interface crate as a dependency to their Rust -> Wasm applications. For example, add the following line to the application's Cargo.toml file.
[dependencies]
ssvm_tensorflow_interface = "^0.1.0"
Developers will bring the functions of ssvm_process_interface into scope within their Rust -> Wasm application's code. For example, adding the following code to the top of their `main.rs
use ssvm_process_interface;
rust
// The mod_buf is a vec<u8> which contains model data.
// The flat_img is a vec<f32> which contains normalized image in rgb32f format.
let res_str = ssvm_tensorflow_interface::run_tensorflow_vision(&mod_buf, &flat_img, 224, 224, "input", &["MobilenetV2/Predictions/Softmax"]);
// The res_str is the result in json format.
rust
let mut file_img = File::open("sample.jpg").unwrap();
let mut img_buf = Vec::new();
file_img.read_to_end(&mut img_buf).unwrap();
let flat_img = ssvm_tensorflow_interface::load_jpg_image_to_rgb32f(&img_buf, 224, 224);
// The flat_img is a vec<f32> which contains normalized image in rgb32f format and resized to 224x224.
bash
$ cargo build --target=wasm32-wasi
The output WASM file will be at target/wasm32-wasi/debug/ or target/wasm32-wasi/release.
Please refer SSVM with tensorflow extension for WASM execution.
The official crate is available at crates.io.