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cuvis-ai-schemas

Lightweight schema definitions for the cuvis-ai ecosystem.

PyPI version CI Status codecov License Python

Overview

cuvis-ai-schemas is a centralized, dependency-light package of schema definitions used across the cuvis-ai ecosystem. It enables type-safe communication between services without heavy runtime requirements.

Key points:

  • Minimal deps (pydantic + pyyaml)
  • Full Pydantic validation
  • Optional extras for proto, torch, numpy, lightning, testing

Installation

uv add cuvis-ai-schemas
uv add "cuvis-ai-schemas[proto]"
uv add "cuvis-ai-schemas[full]"

Extras:

  • proto: gRPC and protobuf support
  • torch: PyTorch dtype handling (validation only)
  • numpy: NumPy array support
  • lightning: PyTorch Lightning training configs
  • testing: Hypothesis strategies + round-trip assertions (cuvis_ai_schemas.testing)
  • full: All features
  • dev: Development dependencies

Usage

from cuvis_ai_schemas.pipeline import PipelineConfig, NodeConfig

pipeline = PipelineConfig(
    nodes=[NodeConfig(name="node_1", class_name="my_pkg.DataLoader", hparams={"batch_size": 32})],
    connections=[],
)

pipeline_json = pipeline.to_json()
pipeline = PipelineConfig.from_json(pipeline_json)

Development

uv sync --all-extras --dev
uv run pytest tests/ -v
uv run ruff check cuvis_ai_schemas/ tests/
uv run ruff format cuvis_ai_schemas/ tests/
uv run mypy cuvis_ai_schemas/

Contributing

Contributions are welcome. Please:

  1. Ensure tests pass
  2. Run ruff format and ruff check
  3. Keep type hints and update docs as needed

License

Licensed under the Apache License 2.0. See LICENSE for details.

Metadata

Release files for cuvis-ai-schemas 0.12.1

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