This package collects tools for probabilistic reasoning in TensorFlow. It is intended to serve as a hub for development of modeling tools, inference algorithms, useful models, and general statistical computation. Taking advantage of the TensorFlow ecosystem allows straightforward combination of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

Contents of this package currently include:

• Sampling algorithms E.g., tfp.mcmc.sample_chain, tfp.mcmc.HamiltonianMonteCarlo, tfp.monte_carlo.expectation.
• Example models (tfp.examples): implementations of common probability models in TensorFlow using tools from this package and from tf.contrib.distributions.

Contents of this repository should be considered under active development; interfaces may change at any time. We welcome external contributions! See Contributing for details.

As simple as:

pip install tfp-nightly --user --upgrade     # depends on tensorflow (CPU-only)

We also provide a GPU-enabled flavor:

pip install tfp-nightly-gpu --user --upgrade # depends on tensorflow-gpu (GPU enabled)

TensorFlow Probability does not currently contain any GPU-specific code; the primary difference between these packages is that tensorflow-probability-gpu depends on a GPU-enabled version of TensorFlow.

To force a Python 3-specific install, replace pip with pip3 in the above commands. For additional installation help, guidance installing prerequisites, and (optionally) setting up virtual environments, see the TensorFlow installation guide.

You can also install from source. This requires the Bazel build system.

git clone https://github.com/tensorflow/probability.git
cd probability
bazel build --config=opt --copt=-O3 --copt=-march=native :pip_pkg
./bazel-bin/pip_pkg /tmp/tensorflow_probability_pkg
pip install /tmp/tensorflow_probability_pkg/*.whl --user --upgrade

It is often easiest to learn by example. The examples/ directory contains reference implementations of common probabilistic models and demonstrates idiomatic styles for building probability models in TensorFlow. Example code may be run directly from the command line, e.g.,

python -m tensorflow_probability.examples.weight_uncertainty.mnist_deep_nn

to train a Bayesian deep network to classify MNIST digits. See the examples directory for the list of example implementations.

After you've installed tensorflow_probability, functions can be accessed as:

import tensorflow_probability as tfp

We're eager to collaborate with you! Feel free to open an issue on GitHub and/or send us your pull requests. See our contribution doc for more details.