Installation

Installation#

raptor is pure Python with zero hard dependencies — nothing CUDA-specific, no GPU required to install or import it.

Install#

pip install raptor-core

The demo extra pulls in numpy and torch, needed only to run the examples:

pip install "raptor-core[demo]"

Building from source#

To work on the code, install from a clone:

git clone https://github.com/amasat01/raptor.git
cd raptor
pip install -e .

Requirements#

  • Python 3.9 or newer

  • No GPU, no CUDA toolkit, no C or C++ compiler.

Verify#

python -c "import raptor; print(raptor.__version__)"

Naming#

The package’s distribution name is raptor-core — raptor on PyPI is an unrelated package (a Python 2-era deploy tool), so the family’s distribution names carry the raptor- prefix to avoid that collision. The import name stays flat and unchanged regardless: import raptor always works once the package is installed.

Running the family together#

raptor alone gives you the schema, the protocol contracts and the conformance declarations — enough to validate manifests and check implementations against the family’s contracts. To compile and run a kernel end to end you also need a kernel producer and an executor: hawk and eagle. Both are on PyPI.

pip install "raptor-core[demo]"
pip install "raptor-hawk[cuda12]" "raptor-eagle[cuda12]"   # or [cuda13] on both

hawk (kernel authoring) pulls aether-dsc, the sealed C++ headers it compiles against, automatically, and needs a host g++ 11 or newer. eagle (execution) runs the kernels on the GPU. No nvcc or CUDA toolkit is needed. hawk and eagle need Python 3.10 or newer (hawk: Linux x86_64, CPython 3.9-3.14 including free-threaded 3.13t and 3.14t).

NVIDIA packages come only through the extras

raptor-hawk[cuda12] pulls cuda-bindings 12, nvidia-cuda-nvrtc-cu12 and nvidia-cuda-cccl-cu12; raptor-hawk[cuda13] pulls cuda-bindings 13, nvidia-cuda-nvrtc 13 and nvidia-cuda-cccl 13 (CUDA 13’s wheels have no -cu13 suffix); raptor-eagle[cuda12] / [cuda13] pull CuPy (cupy-cuda12x / cupy-cuda13x, with the CUDA headers CuPy compiles against). Without an extra pip installs no NVIDIA package: you get the CPU route, or the GPU route through a CUDA setup you already have. Pick the extra matching the CUDA version your driver reports (nvidia-smi, top right).

Platforms: built and tested on Linux x86_64 only so far (CPython 3.9–3.14, including free-threaded 3.13t and 3.14t), on NVIDIA GPUs from Pascal (Quadro P2000) and Turing (Tesla T4). There are no wheels for macOS, Windows or ARM yet, and WSL2 is untested. raptor-core and aether-dsc are pure Python and install anywhere. On free-threaded 3.13t and 3.14t, hawk and eagle run GIL-free.

See Examples for what the three pieces look like together.