---

name: pyro description: "Use Pyro probabilistic programming APIs for models, distributions, SVI, MCMC, poutine, enumeration, and contrib workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pyro Repo Skill

Use this repo skill when the task involves the pyro-ppl package imported as pyro: probabilistic models, distributions, stochastic variational inference, HMC/NUTS/MCMC, posterior prediction, poutine effect handlers, discrete enumeration, reparameterizers, PyroModule, or Pyro contributed modules.

This skill targets the Pyro 1.9.1 API family. It is self-contained for package use; do not depend on a source checkout, original examples, original tests, or notebooks at runtime.

Install and Import Check

Pyro's distribution name is pyro-ppl and the import name is pyro:

python -m pip install pyro-ppl
python - <<'PY'
import pyro, torch
print(pyro.__version__)
print(torch.__version__, torch.cuda.is_available())
PY

For a safe diagnostic that also reports optional integrations, run scripts/check_pyro_environment.py. Start with python scripts/check_pyro_environment.py --help; use --smoke to run a tiny CPU SVI check.

Route by Task

• Basic stochastic functions, pyro.sample, pyro.param, observed sites, pyro.plate, parameter-store state, validation, RNG seeds, PyroModule, PyroParam, and PyroSample: read sub-skills/modeling-basics/SKILL.md.
• Distribution selection, constraints/transforms, .to_event(), Independent, HMM/zero-inflated/stable/matching distributions, and event/batch/plate shape debugging: read sub-skills/distributions-and-shapes/SKILL.md.
• SVI training loops, ELBO choice, autoguides, Pyro optimizers, vanilla PyTorch optimizer loops, minibatching, JIT/vectorized particles, and SVI troubleshooting: read sub-skills/svi-and-autoguides/SKILL.md.
• HMC/NUTS/MCMC runs, initialization, warmup/sample/chain choices, diagnostics, Predictive, WeighedPredictive, and posterior/prior predictive shapes: read sub-skills/mcmc-and-prediction/SKILL.md.
• Poutine handlers, trace/condition/replay/block/scale/mask/seed/substitute, discrete enumeration, TraceEnum_ELBO, infer_discrete, config_enumerate, reparameterizers, and inference-tied pyro.ops: read sub-skills/effect-handlers-and-enumeration/SKILL.md.
• pyro.contrib, MiniPyro/generic backend, forecasting, GP, epidemiology, tracking, easyguide, CEVAE, Funsor, Horovod, Lightning, domain examples, and optional dependency policy: read sub-skills/contrib-and-domain-workflows/SKILL.md.

For a compact root API map and verified signature highlights, read references/api-cheatsheet.md. For install/import, backend, optional dependency, validation, and routing failures, read references/troubleshooting.md. For staleness checks against a repository checkout, read references/repo-provenance.md.

Fast Decision Rules

1. If the user has an error, ask first for the Pyro version, PyTorch version, CPU/CUDA backend, and a minimal model/guide snippet unless already present.
2. Turn on validation while debugging: pyro.enable_validation(True).
3. Clear parameter state between independent experiments: pyro.clear_param_store().
4. For any shape or plate problem, trace the model and inspect trace.format_shapes() before changing model structure.
5. Do not use HMC/NUTS directly on discrete latent variables. Enumerate, marginalize, or use another inference strategy.
6. Treat CUDA, Funsor, Horovod, Lightning, Graphviz, torchvision, pandas, scanpy, and long example/tutorial dependencies as optional until the user's active environment proves support.
7. Prefer tiny synthetic tensors and this skill's bundled smoke scripts before attempting long training, downloads, plotting, or GPU-specific runs.

What Not to Use This Skill For

• Generic PyTorch training that does not use Pyro primitives or distributions.
• PyMC, NumPyro, Bean Machine, Stan, or TensorFlow Probability code unless the task is explicitly translating concepts to Pyro.
• Full scientific reproduction of every Pyro tutorial/example; use this skill to implement or debug the package workflow, then request task-specific data, runtime, and budget.
• Maintainer release, benchmark, profiling, Docker, or documentation-build work unless the user explicitly asks to edit the Pyro repository.