horos

End-to-end tooling for perception tasks: annotate → train → evaluate → deploy.

• Models are adapters behind horos/backends/; nothing above that layer knows which architecture is underneath.
• License metadata travels with every model, run, and export artifact.
• Target deployment platform is NVIDIA Jetson.

Install

The install scripts detect your platform (OS, NVIDIA GPU, Jetson) and install the matching torch build plus horos into ./.venv:

# Ubuntu / macOS / Jetson
./install.sh

# Windows
install.bat

What they decide for you:

Or simply:

pip install horos
horos doctor        # verifies the environment; `horos doctor --fix` installs what's missing

pip install horos does the right thing per platform: on Linux/aarch64 (Jetson) it deliberately skips rfdetr/torch so pip can never replace the CUDA JetPack torch — horos doctor --fix completes the install there with the right sources.

Use a dedicated environment. horos pins rfdetr exactly (upstream has had silent annotation-corruption bugs; reproducibility wins) and requires transformers >= 5.1 — installing into a shared ML environment will upgrade transformers, supervision, huggingface-hub and friends, which can break other projects living in that environment. The install scripts create an isolated ./.venv for exactly this reason.

Jetson (read this — it matters)

On Jetson, torch must come from NVIDIA's JetPack-matched wheel. The PyPI torch has no CUDA support on Jetson, and a plain pip install horos may silently replace your CUDA-enabled torch with a CPU-only build — everything still runs, just an order of magnitude slower.

./install.sh handles this automatically on Jetson: it creates the venv with --system-site-packages, verifies the existing torch has CUDA (warning loudly if not), and installs horos with --no-deps so pip can never swap torch out.

Doing it by hand:

pip install horos --no-deps
pip install pydantic flask pyyaml pillow "transformers>=5.1,<6" supervision
# torch/torchvision: use the NVIDIA wheel matching your JetPack version

horos warns at backend load time if it detects a Jetson platform where torch.cuda.is_available() is False.

Development

python -m venv .venv && . .venv/bin/activate
pip install -e . --no-deps
pip install pydantic flask pyyaml pillow pytest ruff
pytest tests/test_invariants.py && pytest