pyro-ppl
A Python library for probabilistic modeling and inference
Decision gist · record as of 2026-08-14
Yes. Pyro is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It is the right choice if you need to build or fit probabilistic models in Python and want a mature, well-documented framework that abstracts away inference complexity while remaining flexible for expert customization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch as a runtime dependency; ensure torch is available in your environment.
- Installation is straightforward with low friction—the package is a pure Python wheel with five runtime dependencies (numpy, opt-einsum, pyro-api, torch, tqdm).
- Maintenance is active; the repository shows recent commits and the project is backed by community contributors including a team at the Broad Institute.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), so you can use Pyro in commercial and proprietary projects without restriction, though you must include a copy of the license.
last release 2024-06-02 (803 days) · last repo commit 2026-08-04 · 9,038 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,232,081 downloads/mo, #4,186 on PyPI
Alternatives
Verify before relying
pip install pyro-ppl
import pyro
import pyro.distributions as dist
def model():
x = pyro.sample('x', dist.Normal(0, 1))
return x- Whether the package's inference algorithms scale efficiently to the specific data sizes and model complexity you plan to use.
- Performance characteristics and memory overhead compared to hand-written code for your particular use case.
What it is and what it does
Pyro is a probabilistic programming library that lets you express Bayesian models and perform inference on them using PyTorch as its computational backbone. It abstracts away the complexity of building custom inference algorithms by providing high-level primitives for sampling, conditioning, and inference, while still allowing expert users to customize inference strategies when needed.
The library is designed around the principle that you write generative models as ordinary Python functions, then use Pyro's inference engines to fit those models to data. It handles both discrete and continuous random variables, supports automatic differentiation for gradient-based inference, and scales to large datasets. The five core runtime dependencies (numpy, opt-einsum, pyro-api, torch, tqdm) keep the footprint minimal while providing the numerical and progress-tracking infrastructure needed for practical probabilistic modeling.
Use it for
- Build Bayesian regression or classification models where you need posterior distributions over parameters, not just point estimates.
- Perform approximate inference on complex generative models using variational inference or other automated inference strategies.
- Design and run experimental designs or sensitivity analyses by sampling from posterior predictive distributions.
- Model time-to-event data or other censored observations in survival analysis or reliability engineering.
- Prototype custom probabilistic models for scientific research without writing low-level inference code from scratch.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Pyro is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It is the right choice if you need to build or fit probabilistic models in Python and want a mature, well-documented framework that abstracts away inference complexity while remaining flexible for expert customization.
Install
pyro-ppl on PyPI
Before you install
Installation is straightforward with low friction—the package is a pure Python wheel with five runtime dependencies (numpy, opt-einsum, pyro-api, torch, tqdm). Maintenance is active; the repository shows recent commits and the project is backed by community contributors including a team at the Broad Institute.
Requires torch as a runtime dependency; ensure torch is available in your environment.
License in practice
Licensed under Apache 2.0 (permissive), so you can use Pyro in commercial and proprietary projects without restriction, though you must include a copy of the license.
Quickstart
pip install pyro-ppl
import pyro
import pyro.distributions as dist
def model():
x = pyro.sample('x', dist.Normal(0, 1))
return xVerify before relying
- Whether the package's inference algorithms scale efficiently to the specific data sizes and model complexity you plan to use.
- Performance characteristics and memory overhead compared to hand-written code for your particular use case.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpyopt-einsumpyro-apitorchtqdm |
| Maintenance | Actively maintained 803 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,232,081 / month, #4,186 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: pyro_ppl-1.9.1-py3-none-any.whl
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