Metadata-Version: 2.4
Name: lx-ai-core
Version: 0.0.1
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Dist: pydantic>=2.10
Requires-Dist: pyyaml>=6.0
Requires-Dist: pytest>=8.3 ; extra == 'dev'
Requires-Dist: numpy>=2.2 ; extra == 'dev'
Requires-Dist: pillow>=11.0 ; extra == 'dev'
Requires-Dist: torch>=2.5,<3 ; extra == 'dev'
Requires-Dist: make>=0.1.6.post2 ; extra == 'dev'
Requires-Dist: maturin>=1.13.1 ; extra == 'dev'
Requires-Dist: ziglang>=0.16.0 ; extra == 'dev'
Requires-Dist: numpy>=2.2 ; extra == 'torch'
Requires-Dist: pillow>=11.0 ; extra == 'torch'
Requires-Dist: torch>=2.5,<3 ; extra == 'torch'
Provides-Extra: dev
Provides-Extra: torch
License-File: LICENSE
Summary: Standalone local AI runtime core for LX medical model workflows.
Author: WG Lux
Requires-Python: >=3.12, <3.13
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# lx-active-learning

Standalone AI runtime and deployment core for the LX ecosystem.

This is a inference module that provides active learning functionalities on data imported into endoreg-db. It provides optimizations for various types of models and will be ready to host models and efficiently run them while actively updating their weights if that is possible. 

This package intentionally contains no Django models, no Celery tasks, no direct
database access, and no network transfer logic. Integrating callers such as
`endoreg-db` own storage, provenance persistence, queueing, and security policy.

