pywebnn

Thin Python bindings to RustNN's runtime-bound builder and persistent tensor API. RustNN owns graph recording, validation, compilation, execution, and saving. Python converts arguments and NumPy buffers and retains native object owners.

Install and develop

pip install pywebnn
# From a checkout, with a Python virtual environment active:
pip install maturin numpy pytest
maturin develop

Select a compiled RustNN backend with backend="onnx", "coreml", "trtx", "litert", or "cann"; "auto" uses RustNN's selection. Extra runtime features must be enabled when building, for example maturin develop --features trtx-runtime. Provide the runtime libraries required by that backend.

Build and execute

import numpy as np
import webnn

context = webnn.ML().create_context(accelerated=False, backend="onnx")
builder = context.create_graph_builder()
x = builder.input("x", [2, 3], "float32")
y = builder.relu(builder.add(x, x))
output_shape = builder.operand_shape(y)
graph = builder.build({"y": y})
source = context.create_tensor([2, 3], "float32", writable=True)
target = context.create_tensor(output_shape, "float32", readable=True)
context.write_tensor(source, np.arange(-3, 3, dtype=np.float32).reshape(2, 3))
context.dispatch(graph, {"x": source}, {"y": target})
print(context.read_tensor(target))

Allocate tensor bindings once and reuse them across dispatches. There is no compute() method or automatic per-dispatch tensor allocation.

Save before compilation

outputs = {"y": y}
builder.save(outputs, "model.webnn")
graph = builder.build(outputs)

Saving forwards to RustNN's rustnn_save_webnn() and writes .webnn text plus .safetensors weights. Reload with graph = context.load_graph("model.webnn"), which delegates loading and compilation to RustNN. The graph's native input_descriptors and output_descriptors describe the tensors to allocate.

Documentation

• Getting started
• Python API
• Examples
• RustNN