TensorFlow Probability
TensorFlow Probability is a library for probabilistic reasoning and statistical
analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow
Probability provides integration 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.
TFP also works as "Tensor-friendly Probability" in pure JAX!:
from tensorflow_probability.substrates import jax as tfp
--
Learn more here.
Our probabilistic machine learning tools are structured as follows.
Layer 0: TensorFlow. Numerical operations. In particular, the LinearOperator
class enables matrix-free implementations that can exploit special structure
(diagonal, low-rank, etc.) for efficient computation. It is built and maintained
by the TensorFlow Probability team and is now part of
tf.linalg
in core TF.
Layer 1: Statistical Building Blocks
Layer 2: Model Building
- Joint Distributions (e.g.,
tfp.distributions.JointDistributionSequential
):
Joint distributions over one or more possibly-interdependent distributions.
For an introduction to modeling with TFP's JointDistribution
s, check out
this colab - Probabilistic Layers (
tfp.layers
):
Neural network layers with uncertainty over the functions they represent,
extending TensorFlow Layers.
Layer 3: Probabilistic Inference
- Markov chain Monte Carlo (
tfp.mcmc
):
Algorithms for approximating integrals via sampling. Includes
Hamiltonian Monte Carlo,
random-walk Metropolis-Hastings, and the ability to build custom transition
kernels. - Variational Inference (
tfp.vi
):
Algorithms for approximating integrals via optimization. - Optimizers (
tfp.optimizer
):
Stochastic optimization methods, extending TensorFlow Optimizers. Includes
Stochastic Gradient Langevin Dynamics. - Monte Carlo (
tfp.monte_carlo
):
Tools for computing Monte Carlo expectations.
TensorFlow Probability is under active development. Interfaces may change at any
time.
Examples
See tensorflow_probability/examples/
for end-to-end examples. It includes tutorial notebooks such as:
It also includes example scripts such as:
Representation learning with a latent code and variational inference.
Installation
For additional details on installing TensorFlow, guidance installing
prerequisites, and (optionally) setting up virtual environments, see the
TensorFlow installation guide.
Stable Builds
To install the latest stable version, run the following:
# Notes:
# - The `--upgrade` flag ensures you'll get the latest version.
# - The `--user` flag ensures the packages are installed to your user directory
# rather than the system directory.
# - TensorFlow 2 packages require a pip >= 19.0
python -m pip install --upgrade --user pip
python -m pip install --upgrade --user tensorflow tensorflow_probability
For CPU-only usage (and a smaller install), install with tensorflow-cpu
.
To use a pre-2.0 version of TensorFlow, run:
python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9"
Note: Since TensorFlow is not included
as a dependency of the TensorFlow Probability package (in setup.py
), you must
explicitly install the TensorFlow package (tensorflow
or tensorflow-cpu
).
This allows us to maintain one package instead of separate packages for CPU and
GPU-enabled TensorFlow. See the
TFP release notes for more
details about dependencies between TensorFlow and TensorFlow Probability.
Nightly Builds
There are also nightly builds of TensorFlow Probability under the pip package
tfp-nightly
, which depends on one of tf-nightly
or tf-nightly-cpu
.
Nightly builds include newer features, but may be less stable than the
versioned releases. Both stable and nightly docs are available
here.
python -m pip install --upgrade --user tf-nightly tfp-nightly
Installing from Source
You can also install from source. This requires the Bazel build system. It is highly recommended that you install
the nightly build of TensorFlow (tf-nightly
) before trying to build
TensorFlow Probability from source. The most recent version of Bazel that TFP
currently supports is 6.4.0; support for 7.0.0+ is WIP.
# sudo apt-get install bazel git python-pip
python -m pip install --upgrade --user tf-nightly
git clone https://github.com/tensorflow/probability.git
cd probability
bazel build --copt=-O3 --copt=-march=native :pip_pkg
PKGDIR=$(mktemp -d)
./bazel-bin/pip_pkg $PKGDIR
python -m pip install --upgrade --user $PKGDIR/*.whl
As part of TensorFlow, we're committed to fostering an open and welcoming
environment.
See the TensorFlow Community page for
more details. Check out our latest publicity here:
Contributing
We're eager to collaborate with you! See CONTRIBUTING.md
for a guide on how to contribute. This project adheres to TensorFlow's
code of conduct. By participating, you are expected to
uphold this code.
References
If you use TensorFlow Probability in a paper, please cite:
- TensorFlow Distributions. Joshua V. Dillon, Ian Langmore, Dustin Tran,
Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt
Hoffman, Rif A. Saurous.
arXiv preprint arXiv:1711.10604, 2017.
(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.)