dl4bi-sps 0.1.2

Deep Learning for Bayesian Inference: Stochastic Process Simulators

Stochastic Process Simulators (dl4bi-sps)

Install

Install with the appropriate command. If JAX isn't installed already, we recommend using one of the dl4bi-sps[<jax-version>] installs. The distribution name is dl4bi-sps, and the import package is dl4bi_sps.

pip install dl4bi-sps # import dl4bi_sps
pip install dl4bi-sps[cpu] # import dl4bi_sps + jax for CPU
pip install dl4bi-sps[cuda12] # import dl4bi_sps + jax for CUDA-12
pip install dl4bi-sps[cuda13] # import dl4bi_sps + jax for CUDA-13

View Documentation (Locally)

git clone [e-mail]:MLGlobalHealth/sps.git
cd sps
uv sync --extra {cpu,cuda12,cuda13}
uv run --with pdoc pdoc --docformat google --math dl4bi_sps

Demo

import matplotlib.pyplot as plt

import jax
from jax import random

from dl4bi_sps.gp import GP
from dl4bi_sps.priors import Prior
from dl4bi_sps.utils import build_grid
from dl4bi_sps.kernels import matern_3_2, matern_5_2

rng = random.key(42)

s_1d = build_grid([{"start": -2, "stop": 2, "num": 128}])
s_2d = build_grid([{"start": -1.5, "stop": 1.5, "num": 300}, {"start": -2.5, "stop": 2.5, "num": 500}])
batch_size = 1
approx = True
lengthscales = [0.05, 0.1, 0.2]
for name, s in zip(["1d", "2d"], [s_1d, s_2d]):
    fig, axes = plt.subplots(len(lengthscales), 1)
    for i, ls in enumerate(lengthscales):
        gp = GP(matern_3_2, ls=Prior("fixed", {"value": ls}))
        f, *_ = gp.simulate(rng, s, batch_size, approx)
        axes[i].set_title(f"ls={ls}")
        if name == "1d":
            axes[i].plot(s, f.squeeze().T)
        else:
            axes[i].imshow(f.squeeze().reshape(300, 500), cmap="Spectral_r")
    plt.tight_layout()
    plt.savefig(f"{name}_gp.png", dpi=150)
    plt.clf()

# create a simple (forever) dataloader
def dataloader(rng, gp, s, batch_size=64, approx=False):
    while True:
        rng_i, rng = random.split(rng)
        yield gp.simulate(rng_i, s, batch_size, approx)


gp = GP(matern_5_2, ls=Prior("beta", {"a": 2.5, "b": 5}))
loader = dataloader(rng, gp, s, batch_size, approx=True)
f, var, ls, period, z = next(loader)


# within IPython, speed test Kronecker (approx) vs. Cholesky methods 
rng, batch_size = random.key(42), 1024
s = build_grid([{"start": 0, "stop": 1, "num": 64}] * 2) # 64x64 grid
%timeit gp.simulate(rng, s, batch_size, approx=True) # ~5 ms
%timeit gp.simulate(rng, s, batch_size, approx=False) # ~50 ms

More examples can be found here.

Gotchas

Small lengthscales can cause numerical instability. Enabling 64-bit floating operations often helps, but it roughly doubles memory usage and may reduce throughput on accelerators.

import jax
# use 64-bit precision globally
jax.config.update("jax_enable_x64", True)
# use 64-bit precision only inside this context manager
with jax.enable_x64():
    # Do something in 64-bit precision
    ...
# Back to default 32-bit precision

Development Setup

• Install uv.
• Clone the repository and cd into it: git clone [e-mail]:MLGlobalHealth/sps.git && cd sps
• Install the pinned Python version if needed: uv python install
• Sync the project, development dependencies, and one JAX extra: uv sync --extra {cpu,cuda12,cuda13}
• Run the test suite: uv run pytest

uv sync creates a local .venv/ and installs the project in editable mode, so changes in dl4bi_sps/ are reflected immediately.

Build and Publish to PyPI

Create a local .env file with the publish tokens:

TEST_PYPI_TOKEN=pypi-...
PYPI_TOKEN=pypi-...

Run the release helper from a clean main checkout:

uv run python scripts/release.py .env "Release notes"

The helper bumps the patch version, commits and tags v<version> <message>, rebuilds dist/, publishes to TestPyPI and PyPI, pushes main and the tag, and smoke-tests the published install targets for dl4bi-sps.