burn-python

Python inference frontend for the Burn deep learning framework.

Load an ONNX model and run inference from Python — numpy in, numpy out. No Rust required.

import burn_python as burn
import numpy as np

model = burn.load_onnx("model.onnx")
x = np.random.randn(1, 3, 224, 224).astype(np.float32)
output = model([x])[0]

Status

Early development. The numpy ↔ Burn tensor bridge is done, and the ONNX interpreter currently supports:

• Linear algebra: Gemm, Linear
• Activations: Relu, Sigmoid, Tanh, Gelu, Softmax, LogSoftmax
• Elementwise: Add, Sub, Mul, Div
• Shape ops: Reshape, Flatten, Transpose
• Conv/pooling: Conv2d, BatchNormalization (fused), MaxPool2d, AveragePool2d, GlobalAveragePool

Enough to run simple MLPs and small CNNs; more ops are being added incrementally.

Building

pip install maturin
maturin develop --release

Testing

python tests/make_test_model.py   # generates tests/mlp.onnx
python tests/test_bridge.py       # numpy <-> Burn tensor bridge
python tests/compare_ort.py       # correctness + perf vs ONNX Runtime

License

MIT OR Apache-2.0