genjax 1.0.14

• Purpose: JAX probabilistic programming with generative functions, structured traces, vectorized programmable inference, MCMC, SMC, VI, and ADEV.
• POPL 2026 artifact: v1.0.10

Use

git clone https://github.com/a-tiny-project/genjax.git
cd genjax
uv sync --locked --group test
uv run --locked --group test pytest tests/ -o addopts="" -q -n 4
uv run --locked --group test xdoctest src/genjax

from genjax import gen, normal

@gen
def model():
    return normal(0.0, 1.0) @ "x"

trace = model.simulate()
choices = trace.get_choices()

• Generative functions expose simulate, generate, assess, and update.
• vmap and modular_vmap lift model and inference structure over explicit array axes.

Development

The lock covers Linux x86_64 and macOS arm64. The test group runs on Python 3.12. Coverage uses the same group:

uv run --locked --group test pytest tests/ -v --cov=src/genjax --cov-report=xml --cov-report=html --cov-report=term
uv run --locked --group test coverage report --show-missing

The format group holds the formatting tools. Run the hooks with:

uv run --locked --group format pre-commit run --all-files
uv run --locked --group format pre-commit run prettier --hook-stage manual --all-files

Pre-commit supplies Node for Prettier. To remove generated figures:

find examples -type f -path '*/figs/*' \( -name '*.pdf' -o -name '*.png' -o -name '*.jpg' -o -name '*.svg' \) -delete

Paper cases

Run these commands from the project root. Each example has its own dependency group, which uv installs from uv.lock as needed.

Create the shared output directory with mkdir -p figs, then run the fair coin, curve fitting, Game of Life, and localization commands in the table to produce the paper figures. The AIR training command is uv run --locked python -m examples.air.main train --estimator enum --small-config --num-examples 256 --epochs 2. The cone figures use uv run --locked --extra viz python -m examples.cone.main fig2 and uv run --locked --extra viz python -m examples.cone.main table4.

The Game of Life raster is an untracked input. Fetch the pinned copy before running that example and verify its digest:

mkdir -p examples/gol/assets
curl -fsSL --retry 3 -o examples/gol/assets/wizards.jpg \
  https://raw.githubusercontent.com/a-tiny-project/genjax/fec393b015e6113585f1cd425d2a6817a842b1d3/examples/gol/assets/wizards.jpg
printf '%s\n' 'c680998ab5c25a505fdf0e1e03181d4275fbde66e1f1eb1da4cad13bf8db5cb1  examples/gol/assets/wizards.jpg' | sha256sum -c -

Fetch the multi-MNIST data for AIR with uv run --locked --group perfbench-pyro python -m examples.air.main fetch-data --output examples/air/data/multi_mnist_uint8.npz --cache-root /tmp/air-data. For GPU figures, add --group cuda to the corresponding uv command; the perfbench CUDA pipeline also takes --mode cuda. GPU AIR runs keep their shared-memory temporary directory and XLA flags:

TMPDIR=/dev/shm XLA_FLAGS='--xla_gpu_enable_triton_gemm=false --xla_gpu_autotune_level=0' uv run --locked --group cuda python -m examples.air.main train --dataset multi-mnist --data-path examples/air/data/multi_mnist_uint8.npz --num-examples 2048 --epochs 10 --batch-size 32 --eval-batch-size 128 --learning-rate 1e-4 --estimator enum
TMPDIR=/dev/shm XLA_FLAGS='--xla_gpu_enable_triton_gemm=false --xla_gpu_autotune_level=0' uv run --locked --group cuda python -m examples.air.main compare --dataset multi-mnist --data-path examples/air/data/multi_mnist_uint8.npz --num-examples 2048 --epochs 10 --batch-size 32 --eval-batch-size 128 --learning-rate 1e-4

CPU and GPU execute the same models but have different scaling curves. Figure 19 and paper-scale curve fitting require CUDA-like throughput to match the published timing/ESS panels. Gen.jl benchmark lanes require Julia 1.10 or newer. Generated figures are saved in figs/; perfbench outputs to separate CPU and CUDA directories.

Code

• Public package
• Generative-function core
• Distributions
• Probabilistic vectorization
• Inference
• ADEV
• Tests
• Examples
• Performance benchmark
• Citation metadata
• Package references

References

• Probabilistic Programming with Vectorized Programmable Inference
• Artifact DOI
• Gen: programmable inference
• ADEV
• Programmable variational inference

Acknowledgments

GenJAX 1.0 continues the GenJAX project, whose 0.x releases were developed in genjax-community/genjax from 2022 to 2025. GenJAX thanks the 22 people other than the maintainer who contributed commits to that codebase:

Matthew Brulhardt, Jacob Burnim, Guillaume Dalle, Arijit Dasgupta, Cameron Freer, Matin Ghavami, Alex Hiser, Matt Huebert, Mathieu Huot, Mirko Klukas, Urs Köster, Ben Lee, Ian Limarta, Joao Loula, David R. MacIver, George Matheos, Jay Pottharst, Sam Ritchie, Rif A. Saurous, Colin Smith, Xiaoyan Wang, Fabian Zaiser.

License

Apache-2.0. See LICENSE.