cuvis-ai-core 0.3.2

Cuvis-AI Core Framework

Cuvis-AI Core Framework

Overview

cuvis-ai-core is the foundational framework that powers the cuvis-ai machine learning pipeline for hyperspectral image analysis. This repository provides the core infrastructure and building blocks that enable flexible, extensible AI workflows for hyperspectral measurements.

While the cuvis-ai repository contains domain-specific nodes and pre-built models for tasks like anomaly detection and classification, cuvis-ai-core provides the essential framework components:

• Node System: Base classes for creating processing nodes with typed input/output ports
• Pipeline Infrastructure: Graph-based pipeline orchestration, execution, and visualization
• Plugin System: Dynamic node loading from Git repositories or local filesystem paths
• Serialization & Restoration: Save and restore complete pipeline states and configurations
• Type Safety: Strongly-typed port system with runtime validation
• gRPC Services: Remote pipeline management, training, and inference APIs
• Training Framework: Integration with PyTorch Lightning for model training workflows

This separation allows the core framework to evolve independently while the catalog of domain-specific nodes grows through a plugin architecture.

• Website: https://www.cubert-hyperspectral.com/
• Support: http://support.cubert-hyperspectral.com/

Installation

Prerequisites

If you want to directly work with cubert session files (.cu3s), you need to install cuvis C SDK from here.

Local development now relies on uv for Python and dependency management. If uv is not already available on your system you can install it following their installation instructions.

Install from PyPI (Recommended)

# Install cuvis-ai-core from PyPI
uv add cuvis-ai-core

PyTorch will be installed automatically with CUDA support if available on your system.

Local development with uv

Create or refresh a development environment at the repository root with:

uv sync --all-extras --dev

This installs the runtime dependencies declared in pyproject.toml. uv automatically provisions the Python version declared in the project metadata, so no manual interpreter management is required.

Enable Git Hooks (Required)

After cloning the repository, enable the git hooks for code quality enforcement:

git config core.hooksPath .githooks

This configures Git to use the version-controlled hooks in .githooks/ which automatically enforce code formatting, linting, and testing standards before commits and pushes. See docs/development/git-hooks.md for details.

Advanced environment setup

When you need the reproducible development toolchain (JupyterLab, TensorBoard, etc.) from the lock file, run:

uv sync --locked --extra dev

Use uv run to execute project tooling without manually activating virtual environments, for example:

uv run pytest

Collect coverage details (the dev extra installs pytest-cov) with:

uv run pytest --cov=cuvis_ai --cov-report=term-missing

Ruff handles both formatting and linting. Format sources and check style with:

uv run ruff format .
uv run ruff check .

The configuration enforces import ordering, newline hygiene, modern string formatting, safe exception chaining, and practical return type annotations while avoiding noisy Any policing.

Validate packaging metadata and build artifacts before publishing:

uv build

To build the documentation, add the docs extra:

uv sync --locked --extra docs

Combine extras as needed (e.g. uv sync --locked --extra dev --extra docs). Whenever the pyproject.toml or uv.lock changes, rerun uv sync --locked with the extras you need to stay up to date.