native_neural_network_std

Purpose

native_neural_network_std is a std-oriented ergonomic wrapper around the native_neural_network crate. It provides owned types and helper utilities for Rust standard users while fully leveraging the underlying no_std core.

Prerequisites

Rust stable (latest recommended)
.rnn model file
No UI included; no pre-trained models bundled

Installation

Add via cargo:

cargo add native_neural_network_std

Import in code:

use native_neural_network_std::ModelStd;

Minimal Workflow

Load a .rnn model via ModelStd::from_file().
Inspect model metadata for buffer requirements.
Allocate caller-owned input/output buffers.
Prepare input tensors using TensorStd.
Execute inference with NeuralNetworkStd or RnnStd.
Retrieve outputs from the output buffer.

Accessing Metadata

Inspect layer counts, weight/bias arrays.
Retrieve kernel-level execution plans.
Export internal structures for analysis or integration.

What this Quickstart Does Not Cover

Training loops and optimizer tuning
Low-level profiling or quantization pipelines
FFI integration details (see main README)
Advanced visualization or external tooling

Philosophy

Modular, deterministic building blocks for inference
Predictable memory usage, fully owned APIs
Designed for easy wrapping and multi-language integration
100% Clippy clean

Example Usage (abridged)

use native_neural_network_std::{ModelStd, NeuralNetworkStd};

let model = ModelStd::from_file("/tmp/sample.rnn");
let mut nn = NeuralNetworkStd::new(&model);
nn.forward(&input_buffer, &mut output_buffer);

For detailed examples, see the examples/ folder.

Documentation

For full low-level details, refer to native_neural_network documentation: https://docs.rs/native_neural_network

License

MIT

native_neural_network_std 0.2.1