tfp-nightly
Probabilistic modeling and statistical inference in TensorFlow
Decision gist · record as of 2026-08-14
Yes, if you are building probabilistic or Bayesian machine learning models in TensorFlow or JAX. The library is actively maintained, has no known vulnerabilities, and integrates with TensorFlow's ecosystem. Install with caution only if your project requires stable APIs, as the package is marked Beta and interfaces may change; verify compatibility with your TensorFlow version for production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorFlow or JAX installed separately; TensorFlow Probability is a library layer, not a standalone numerical backend.
- Low install friction with a pure-Python wheel and common dependencies.
- The package is actively maintained with recent commits and no known vulnerabilities, though it is marked Beta and interfaces may change.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions beyond attribution and liability disclaimers.
last release 2026-08-14 (0 days) · last repo commit 2026-08-06 · 4,425 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 274,867 downloads/mo, #8,184 on PyPI
Alternatives
Verify before relying
pip install tfp-nightly
import tensorflow_probability as tfp
dist = tfp.distributions.Normal(loc=0., scale=1.)
samples = dist.sample()- Whether JAX substrate support requires separate JAX installation or is bundled
- Performance characteristics and scalability limits for large-scale probabilistic models
- Compatibility with specific TensorFlow versions beyond Python 3.9, 3.10, 3.11, 3.12 support
What it is and what it does
TensorFlow Probability is a library for building and reasoning about probabilistic models within the TensorFlow ecosystem. It layers statistical building blocks—distributions, bijectors, and transformations—on top of TensorFlow's numerical operations, then adds higher-level tools for model construction (joint distributions, probabilistic layers) and inference (MCMC, variational inference, stochastic optimizers). The library also works as a JAX substrate, letting you use the same probabilistic API with JAX's functional paradigm instead of TensorFlow's graph execution.
Typical use involves defining a probabilistic model using distributions and layers, then performing inference via gradient-based optimization or sampling-based methods. It is designed for researchers and practitioners building Bayesian neural networks, hierarchical models, and other probabilistic systems that benefit from automatic differentiation and hardware acceleration. The package depends on absl-py, six, numpy, decorator, cloudpickle, gast, and dm-tree.
Use it for
- Build Bayesian neural networks with weight uncertainty and quantify prediction confidence
- Perform hierarchical statistical modeling and inference on grouped or nested data
- Implement Markov chain Monte Carlo or variational inference for posterior approximation
- Define and sample from complex joint probability distributions with dependencies
- Combine probabilistic layers with standard deep learning models for uncertainty-aware inference
- Conduct dimensionality reduction or clustering using probabilistic generative models
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building probabilistic or Bayesian machine learning models in TensorFlow or JAX.
The library is actively maintained, has no known vulnerabilities, and integrates with TensorFlow's ecosystem. Install with caution only if your project requires stable APIs, as the package is marked Beta and interfaces may change; verify compatibility with your TensorFlow version for production use.
Install
tfp-nightly on PyPI
Before you install
Low install friction with a pure-Python wheel and common dependencies. The package is actively maintained with recent commits and no known vulnerabilities, though it is marked Beta and interfaces may change.
Requires TensorFlow or JAX installed separately; TensorFlow Probability is a library layer, not a standalone numerical backend.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install tfp-nightly
import tensorflow_probability as tfp
dist = tfp.distributions.Normal(loc=0., scale=1.)
samples = dist.sample()Verify before relying
- Whether JAX substrate support requires separate JAX installation or is bundled
- Performance characteristics and scalability limits for large-scale probabilistic models
- Compatibility with specific TensorFlow versions beyond Python 3.9, 3.10, 3.11, 3.12 support
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesabsl-pysixnumpydecoratorcloudpicklegastdm-tree |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 274,867 / month, #8,184 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tfp_nightly-0.26.0.dev20260814-py2.py3-none-any.whl
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See also tensorflow-probability · distrax · pyjpt · problog · tf-estimator-nightly · pytensor-distributions · pyro-api · pyro-ppl · pyAgrum-nightly · tf-nightly