tynx

The ONNX runtime that trains. Python bindings for Tynx, a lightweight neural network runtime built on Burn.

Tynx loads ONNX models at runtime with no code generation step, runs on CPU and GPU through Burn backends, and is growing into a full eager training library with a PyTorch-shaped API.

This is an early alpha. What works in this release:

  • eager tensors with autograd: arithmetic, matmul, activations, reductions, backward(), .grad, no_grad();
  • Parameter values for building trainable modules;
  • loading ONNX models and inspecting their inputs and outputs.

Model execution from Python, NumPy interop, layers, and optimizers land in upcoming releases. See the repository for the roadmap, benchmarks, and the Rust API.

import tynx

x = tynx.Tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True)
loss = (x @ x).sum()
loss.backward()
print(x.grad.tolist())

session = tynx.Session("model.onnx")
print(session.inputs, session.outputs)

License

MIT or Apache-2.0, at your option.

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Data verified by PyPI on Jul 22, 2026
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  • Released:
Newer release available (0.2.0)

1 maintainer

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Data verified by PyPI on Jul 22, 2026
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License expression

MIT OR Apache-2.0
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Requires

Python >=3.9

Tags

onnx machine-learning neural-network inference training burn

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