v0.1.2 — Initial production release

The first production-grade release of KANX.

pip install kanx

What's in this release

Two backends, one API
TensorFlow (primary) and PyTorch (secondary) with identical configuration
semantics. Both tested in CI on Python 3.10, 3.11, and 3.12.

ONNX export
Dynamic batch axis. Numerical parity within 1e-5 of the eager model.
Unlocks TensorRT, OpenVINO, CoreML, and every other deployment runtime.

REST API
FastAPI service with thread-safe ModelRegistry, health/info/predict/load/reset
endpoints, and checkpoint-with-fallback. Ready to serve.

Deployment
Dockerfile + docker-compose. Kubernetes manifests with Deployment (rolling
updates), Service, Ingress, HPA, and PVC.

CLI
python -m kanx {info,train,predict} with YAML configs.

Tests
95 tests across unit, integration, end-to-end, property-based (Hypothesis),
and performance regression. 94% library coverage. Numerical contracts
(partition of unity, non-negativity, ONNX parity) asserted by tests.

Documentation
MkDocs Material site. Eight long-form docs covering philosophy, architecture,
system design, build, security, API, testing, and deployment.

Benchmarks
KAN[2,32,1]: 1.7×10⁻⁵ MSE with 864 parameters.
MLP[2,64,64,1]: 4.5×10⁻³ MSE with 4,417 parameters.

Full changelog: https://github.com/Mattral/KANX/blob/main/CHANGELOG.md