Installation

The base package needs Python 3.9 or later and NumPy. Release CI targets CPython 3.9–3.13; newer interpreters are not yet part of that matrix.

Install from Git

python -m pip install 'flashjet @ git+https://github.com/jet-universe/FlashJet.git'
python -m pip install 'flashjet[torch] @ git+https://github.com/jet-universe/FlashJet.git'
python -m pip install 'flashjet[triton,data] @ git+https://github.com/jet-universe/FlashJet.git'

Choose the command for your workload. These commands install the current default branch. For reproducible work, append @<full-commit-sha> to the Git URL, replacing the placeholder with the revision you want. PyPI publishing is deferred.

The extras are:

Extra	Adds	Use
`torch`	PyTorch	CPU or CUDA tensor batches
`triton`	PyTorch and Linux x86_64 Triton	CUDA clustering
`data`	PyTorch, Awkward, PyArrow	Ragged events and Parquet files
`test`	pytest, FastJet, Awkward	Validation; also install `torch` for the full suite

Install the CUDA-enabled PyTorch build suitable for your machine using the PyTorch instructions before adding FlashJet. Keep the Triton version selected by that PyTorch release. Installing an extra cannot provide an NVIDIA driver or GPU.

The Triton dependency marker only installs Triton on Linux x86_64. On macOS, [triton] installs PyTorch but does not enable CUDA. Windows, ROCm, MPS, and Linux ARM GPU execution are not validated release targets.

Wheels from CI

The Distributions workflow builds wheels for CPython 3.9–3.13 on Linux x86_64 and macOS Intel/Apple Silicon. Choose a successful run for your intended commit, download its platform artifact, and extract the ZIP. Install the wheel matching your Python version and platform:

python -m pip install '/path/to/flashjet-<version>-<python>-<abi>-<platform>.whl'

Replace the example path with the extracted wheel’s filename. Wheels contain the native CPU kernel; PyTorch and Triton remain separate dependencies. See the artifact guide for artifact names and checks.

Install from a checkout

From the repository root, use python -m pip install ., or python -m pip install '.[torch]' for tensor batches. Use -e for an editable development install.

Native CPU kernel

Release wheels must contain a loadable C++ kernel. Source installs try to build it with a C++17 compiler, then fall back to NumPy if compilation fails. OpenMP is optional; without it the native kernel runs on one thread.

from flashjet import _native
print(_native.HAS_NATIVE, _native.HAS_OPENMP)

To build without the native kernel:

FLASHJET_NO_NATIVE=1 python -m pip install --no-cache-dir .

Use a clean build directory when switching native build modes. macOS builds use a single-threaded C++ kernel by default: PyTorch bundles an OpenMP runtime, and loading a second Homebrew runtime can abort the process. FLASHJET_OPENMP=1 opts into a probe when you control that runtime setup. FLASHJET_OPENMP=0 disables OpenMP on any platform. Linux builds probe for it by default.