coeus-python
Tensors, automatic differentiation, and neural-network layers for Python, backed by the Coeus Rust stack.
The API follows PyTorch's shape closely enough to read without a translation
guide — a Tensor that tracks gradients, Linear and convolution layers with
forward, optimizers with step and zero_grad — while the computation runs
in Rust and releases the GIL.
Install
pip install coeus-python
Wheels are published for CPython 3.9 through 3.13 on Linux, Windows, and macOS. There are no runtime dependencies.
Use
import pycoeus
# A tensor is data plus a shape; autograd is opt-in per tensor.
x = pycoeus.Tensor([1.0, 2.0, 3.0, 4.0], shape=[2, 2], requires_grad=True)
y = (x * 2.0 + 1.0).sum()
y.backward()
print(x.grad) # [2.0, 2.0, 2.0, 2.0]
# Layers and optimizers compose the way you would expect.
layer = pycoeus.Linear(2, 1)
opt = pycoeus.SGD(layer.parameters(), lr=0.01)
out = layer.forward(x)
loss = out.sum()
loss.backward()
opt.step()
opt.zero_grad()
The import name is pycoeus; the distribution is coeus-python.
What is exposed
- Tensors — real and complex, N-dimensional, with the five arithmetic operators accepting either a tensor (elementwise) or a Python float (scalar broadcast), plus reductions, indexing, and layout operations.
- Autograd —
requires_grad,backward,grad, and no-grad contexts. - Layers — linear, convolution, normalization, pooling, attention, dropout, bilinear.
- Training — optimizers and learning-rate schedulers, losses, activations, initializers, state dicts.
- Distributed — collective helpers.
The wheel ships a hand-maintained type stub (pycoeus.pyi) covering the whole
surface. It is not yet a PEP 561 inline-stub distribution — the extension is a
single top-level module, which has no package directory for the py.typed
marker to live in — so editors that read a root-level stub will use it and
mypy will not.
Why the numerics are not here
This package is a binding surface: it converts types, maps Rust errors onto Python exceptions, and releases the GIL around compute. Every kernel lives in the Rust crates it wraps. One implementation means one place to verify, and the suite differences the bindings against PyTorch, JAX, and MLX on identical inputs rather than against themselves.
Testing the CTC binding
Use an environment containing the wheel built from the current source and its
test extra dependencies: pytest, NumPy and PyTorch. From the repository root:
python -c "import pycoeus, torch; print(pycoeus.__file__)"
python -m pytest --import-mode=importlib -p no:cacheprovider crates/coeus-python/tests/pytorch_parity/test_ctc.py
Confirm that the import belongs to the intended wheel installation. The suite contains ten tests; missing PyTorch skips the module and does not verify parity.
Links
Licence
MIT or Apache-2.0, at your option.
1 maintainer
Metadata
1 maintainer
Release files for coeus-python 0.10.0
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