Name | tfp-nightly JSON |

Version | 0.14.0.dev20210620 JSON |

download | |

home_page | http://github.com/tensorflow/probability |

Summary | Probabilistic modeling and statistical inference in TensorFlow |

upload_time | 2021-06-20 08:43:05 |

maintainer | |

docs_url | None |

author | Google LLC |

requires_python | |

license | Apache 2.0 |

keywords | tensorflow probability statistics bayesian machine learning |

VCS | |

bugtrack_url | |

requirements | No requirements were recorded. |

Travis-CI | No Travis. |

coveralls test coverage | No coveralls. |

# 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. 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`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/ops/linalg) in core TF. __Layer 1: Statistical Building Blocks__ * Distributions ([`tfp.distributions`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/distributions)): A large collection of probability distributions and related statistics with batch and [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) semantics. See the [Distributions Tutorial](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). * Bijectors ([`tfp.bijectors`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/bijectors)): Reversible and composable transformations of random variables. Bijectors provide a rich class of transformed distributions, from classical examples like the [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution) to sophisticated deep learning models such as [masked autoregressive flows](https://arxiv.org/abs/1705.07057). __Layer 2: Model Building__ * Joint Distributions (e.g., [`tfp.distributions.JointDistributionSequential`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/distributions/joint_distribution_sequential.py)): Joint distributions over one or more possibly-interdependent distributions. For an introduction to modeling with TFP's `JointDistribution`s, check out [this colab](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Modeling_with_JointDistribution.ipynb) * Probabilistic Layers ([`tfp.layers`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/layers)): Neural network layers with uncertainty over the functions they represent, extending TensorFlow Layers. __Layer 3: Probabilistic Inference__ * Markov chain Monte Carlo ([`tfp.mcmc`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/mcmc)): Algorithms for approximating integrals via sampling. Includes [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo), random-walk Metropolis-Hastings, and the ability to build custom transition kernels. * Variational Inference ([`tfp.vi`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/vi)): Algorithms for approximating integrals via optimization. * Optimizers ([`tfp.optimizer`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/optimizer)): Stochastic optimization methods, extending TensorFlow Optimizers. Includes [Stochastic Gradient Langevin Dynamics](http://www.icml-2011.org/papers/398_icmlpaper.pdf). * Monte Carlo ([`tfp.monte_carlo`](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/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/`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/) for end-to-end examples. It includes tutorial notebooks such as: * [Linear Mixed Effects Models](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb). A hierarchical linear model for sharing statistical strength across examples. * [Eight Schools](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Eight_Schools.ipynb). A hierarchical normal model for exchangeable treatment effects. * [Hierarchical Linear Models](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb). Hierarchical linear models compared among TensorFlow Probability, R, and Stan. * [Bayesian Gaussian Mixture Models](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Bayesian_Gaussian_Mixture_Model.ipynb). Clustering with a probabilistic generative model. * [Probabilistic Principal Components Analysis](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_PCA.ipynb). Dimensionality reduction with latent variables. * [Gaussian Copulas](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Copula.ipynb). Probability distributions for capturing dependence across random variables. * [TensorFlow Distributions: A Gentle Introduction](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). Introduction to TensorFlow Distributions. * [Understanding TensorFlow Distributions Shapes](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb). How to distinguish between samples, batches, and events for arbitrarily shaped probabilistic computations. * [TensorFlow Probability Case Study: Covariance Estimation](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Probability_Case_Study_Covariance_Estimation.ipynb). A user's case study in applying TensorFlow Probability to estimate covariances. It also includes example scripts such as: * [Variational Autoencoders](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/vae.py). Representation learning with a latent code and variational inference. * [Vector-Quantized Autoencoder](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/vq_vae.py). Discrete representation learning with vector quantization. * [Disentangled Sequential Variational Autoencoder](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/disentangled_vae.py) Disentangled representation learning over sequences with variational inference. * Latent Dirichlet Allocation ([Distributions version](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py), Mixed membership modeling for capturing topics in a document. * [Bayesian Neural Networks](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/bayesian_neural_network.py). Neural networks with uncertainty over their weights. * [Bayesian Logistic Regression](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/logistic_regression.py). Bayesian inference for binary classification. ## Installation For additional details on installing TensorFlow, guidance installing prerequisites, and (optionally) setting up virtual environments, see the [TensorFlow installation guide](https://www.tensorflow.org/install). ### Stable Builds To install the latest stable version, run the following: ```shell # 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: ```shell python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9" ``` Note: Since [TensorFlow](https://www.tensorflow.org/install) 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](https://github.com/tensorflow/probability/releases) 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](https://www.tensorflow.org/probability/api_docs/python/tfp?version=nightly). ```shell python -m pip install --upgrade --user tf-nightly tfp-nightly ``` ### Installing from Source You can also install from source. This requires the [Bazel]( https://bazel.build/) build system. It is highly recommended that you install the nightly build of TensorFlow (`tf-nightly`) before trying to build TensorFlow Probability from source. ```shell # sudo apt-get install bazel git python-pip # Ubuntu; others, see above links. 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 ``` ## Community As part of TensorFlow, we're committed to fostering an open and welcoming environment. * [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow): Ask or answer technical questions. * [GitHub](https://github.com/tensorflow/probability/issues): Report bugs or make feature requests. * [TensorFlow Blog](https://blog.tensorflow.org/): Stay up to date on content from the TensorFlow team and best articles from the community. * [Youtube Channel](http://youtube.com/tensorflow/): Follow TensorFlow shows. * [tfprobability@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfprobability): Open mailing list for discussion and questions. See the [TensorFlow Community](https://www.tensorflow.org/community/) page for more details. Check out our latest publicity here: + [Coffee with a Googler: Probabilistic Machine Learning in TensorFlow]( https://www.youtube.com/watch?v=BjUkL8DFH5Q) + [Introducing TensorFlow Probability]( https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245) ## Contributing We're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md) for a guide on how to contribute. This project adheres to TensorFlow's [code of conduct](CODE_OF_CONDUCT.md). 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](https://arxiv.org/abs/1711.10604). (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.)

{ "_id": null, "home_page": "http://github.com/tensorflow/probability", "name": "tfp-nightly", "maintainer": "", "docs_url": null, "requires_python": "", "maintainer_email": "", "keywords": "tensorflow probability statistics bayesian machine learning", "author": "Google LLC", "author_email": "no-reply@google.com", "download_url": "", "platform": "", "description": "# TensorFlow Probability\n\nTensorFlow Probability is a library for probabilistic reasoning and statistical\nanalysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow\nProbability provides integration of probabilistic methods with deep networks,\ngradient-based inference via automatic differentiation, and scalability to\nlarge datasets and models via hardware acceleration (e.g., GPUs) and distributed\ncomputation.\n\nOur probabilistic machine learning tools are structured as follows.\n\n__Layer 0: TensorFlow.__ Numerical operations. In particular, the LinearOperator\nclass enables matrix-free implementations that can exploit special structure\n(diagonal, low-rank, etc.) for efficient computation. It is built and maintained\nby the TensorFlow Probability team and is now part of\n[`tf.linalg`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/ops/linalg)\nin core TF.\n\n__Layer 1: Statistical Building Blocks__\n\n* Distributions ([`tfp.distributions`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/distributions)):\n A large collection of probability\n distributions and related statistics with batch and\n [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n semantics. See the\n [Distributions Tutorial](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb).\n* Bijectors ([`tfp.bijectors`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/bijectors)):\n Reversible and composable transformations of random variables. Bijectors\n provide a rich class of transformed distributions, from classical examples\n like the\n [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution)\n to sophisticated deep learning models such as\n [masked autoregressive flows](https://arxiv.org/abs/1705.07057).\n\n__Layer 2: Model Building__\n\n* Joint Distributions (e.g., [`tfp.distributions.JointDistributionSequential`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/distributions/joint_distribution_sequential.py)):\n Joint distributions over one or more possibly-interdependent distributions.\n For an introduction to modeling with TFP's `JointDistribution`s, check out\n [this colab](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Modeling_with_JointDistribution.ipynb)\n* Probabilistic Layers ([`tfp.layers`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/layers)):\n Neural network layers with uncertainty over the functions they represent,\n extending TensorFlow Layers.\n\n__Layer 3: Probabilistic Inference__\n\n* Markov chain Monte Carlo ([`tfp.mcmc`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/mcmc)):\n Algorithms for approximating integrals via sampling. Includes\n [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo),\n random-walk Metropolis-Hastings, and the ability to build custom transition\n kernels.\n* Variational Inference ([`tfp.vi`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/vi)):\n Algorithms for approximating integrals via optimization.\n* Optimizers ([`tfp.optimizer`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/optimizer)):\n Stochastic optimization methods, extending TensorFlow Optimizers. Includes\n [Stochastic Gradient Langevin Dynamics](http://www.icml-2011.org/papers/398_icmlpaper.pdf).\n* Monte Carlo ([`tfp.monte_carlo`](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/monte_carlo)):\n Tools for computing Monte Carlo expectations.\n\nTensorFlow Probability is under active development. Interfaces may change at any\ntime.\n\n## Examples\n\nSee [`tensorflow_probability/examples/`](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/)\nfor end-to-end examples. It includes tutorial notebooks such as:\n\n* [Linear Mixed Effects Models](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb).\n A hierarchical linear model for sharing statistical strength across examples.\n* [Eight Schools](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Eight_Schools.ipynb).\n A hierarchical normal model for exchangeable treatment effects.\n* [Hierarchical Linear Models](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb).\n Hierarchical linear models compared among TensorFlow Probability, R, and Stan.\n* [Bayesian Gaussian Mixture Models](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Bayesian_Gaussian_Mixture_Model.ipynb).\n Clustering with a probabilistic generative model.\n* [Probabilistic Principal Components Analysis](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_PCA.ipynb).\n Dimensionality reduction with latent variables.\n* [Gaussian Copulas](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Copula.ipynb).\n Probability distributions for capturing dependence across random variables.\n* [TensorFlow Distributions: A Gentle Introduction](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb).\n Introduction to TensorFlow Distributions.\n* [Understanding TensorFlow Distributions Shapes](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb).\n How to distinguish between samples, batches, and events for arbitrarily shaped\n probabilistic computations.\n* [TensorFlow Probability Case Study: Covariance Estimation](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Probability_Case_Study_Covariance_Estimation.ipynb).\n A user's case study in applying TensorFlow Probability to estimate covariances.\n\nIt also includes example scripts such as:\n\n* [Variational Autoencoders](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/vae.py).\n Representation learning with a latent code and variational inference.\n* [Vector-Quantized Autoencoder](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/vq_vae.py).\n Discrete representation learning with vector quantization.\n* [Disentangled Sequential Variational Autoencoder](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/disentangled_vae.py)\n Disentangled representation learning over sequences with variational inference.\n* Latent Dirichlet Allocation\n ([Distributions version](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py),\n Mixed membership modeling for capturing topics in a document.\n* [Bayesian Neural Networks](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/bayesian_neural_network.py).\n Neural networks with uncertainty over their weights.\n* [Bayesian Logistic Regression](https://github.com/tensorflow/probability/tree/master/tensorflow_probability/examples/logistic_regression.py).\n Bayesian inference for binary classification.\n\n## Installation\n\nFor additional details on installing TensorFlow, guidance installing\nprerequisites, and (optionally) setting up virtual environments, see the\n[TensorFlow installation guide](https://www.tensorflow.org/install).\n\n### Stable Builds\n\nTo install the latest stable version, run the following:\n\n```shell\n# Notes:\n\n# - The `--upgrade` flag ensures you'll get the latest version.\n# - The `--user` flag ensures the packages are installed to your user directory\n# rather than the system directory.\n# - TensorFlow 2 packages require a pip >= 19.0\npython -m pip install --upgrade --user pip\npython -m pip install --upgrade --user tensorflow tensorflow_probability\n```\n\nFor CPU-only usage (and a smaller install), install with `tensorflow-cpu`.\n\nTo use a pre-2.0 version of TensorFlow, run:\n\n```shell\npython -m pip install --upgrade --user \"tensorflow<2\" \"tensorflow_probability<0.9\"\n```\n\nNote: Since [TensorFlow](https://www.tensorflow.org/install) is *not* included\nas a dependency of the TensorFlow Probability package (in `setup.py`), you must\nexplicitly install the TensorFlow package (`tensorflow` or `tensorflow-cpu`).\nThis allows us to maintain one package instead of separate packages for CPU and\nGPU-enabled TensorFlow. See the\n[TFP release notes](https://github.com/tensorflow/probability/releases) for more\ndetails about dependencies between TensorFlow and TensorFlow Probability.\n\n\n### Nightly Builds\n\nThere are also nightly builds of TensorFlow Probability under the pip package\n`tfp-nightly`, which depends on one of `tf-nightly` or `tf-nightly-cpu`.\nNightly builds include newer features, but may be less stable than the\nversioned releases. Both stable and nightly docs are available\n[here](https://www.tensorflow.org/probability/api_docs/python/tfp?version=nightly).\n\n```shell\npython -m pip install --upgrade --user tf-nightly tfp-nightly\n```\n\n### Installing from Source\n\nYou can also install from source. This requires the [Bazel](\nhttps://bazel.build/) build system. It is highly recommended that you install\nthe nightly build of TensorFlow (`tf-nightly`) before trying to build\nTensorFlow Probability from source.\n\n```shell\n# sudo apt-get install bazel git python-pip # Ubuntu; others, see above links.\npython -m pip install --upgrade --user tf-nightly\ngit clone https://github.com/tensorflow/probability.git\ncd probability\nbazel build --copt=-O3 --copt=-march=native :pip_pkg\nPKGDIR=$(mktemp -d)\n./bazel-bin/pip_pkg $PKGDIR\npython -m pip install --upgrade --user $PKGDIR/*.whl\n```\n\n## Community\n\nAs part of TensorFlow, we're committed to fostering an open and welcoming\nenvironment.\n\n* [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow): Ask\n or answer technical questions.\n* [GitHub](https://github.com/tensorflow/probability/issues): Report bugs or\n make feature requests.\n* [TensorFlow Blog](https://blog.tensorflow.org/): Stay up to date on content\n from the TensorFlow team and best articles from the community.\n* [Youtube Channel](http://youtube.com/tensorflow/): Follow TensorFlow shows.\n* [tfprobability@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfprobability):\n Open mailing list for discussion and questions.\n\nSee the [TensorFlow Community](https://www.tensorflow.org/community/) page for\nmore details. Check out our latest publicity here:\n\n+ [Coffee with a Googler: Probabilistic Machine Learning in TensorFlow](\n https://www.youtube.com/watch?v=BjUkL8DFH5Q)\n+ [Introducing TensorFlow Probability](\n https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245)\n\n## Contributing\n\nWe're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md)\nfor a guide on how to contribute. This project adheres to TensorFlow's\n[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to\nuphold this code.\n\n## References\n\nIf you use TensorFlow Probability in a paper, please cite:\n\n+ _TensorFlow Distributions._ Joshua V. Dillon, Ian Langmore, Dustin Tran,\nEugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt\nHoffman, Rif A. Saurous.\n[arXiv preprint arXiv:1711.10604, 2017](https://arxiv.org/abs/1711.10604).\n\n(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.)\n\n\n", "bugtrack_url": null, "license": "Apache 2.0", "summary": "Probabilistic modeling and statistical inference in TensorFlow", "version": "0.14.0.dev20210620", "split_keywords": [ "tensorflow", "probability", "statistics", "bayesian", "machine", "learning" ], "urls": [ { "comment_text": "", "digests": { "md5": "61e480a5919313159b4ca4d6557010a2", "sha256": "e1bb4c8d3fbc170f90aab80450b3941fdfdc94431fc609ffac25faadfd6be10b" }, "downloads": -1, "filename": "tfp_nightly-0.14.0.dev20210620-py2.py3-none-any.whl", "has_sig": false, "md5_digest": "61e480a5919313159b4ca4d6557010a2", "packagetype": "bdist_wheel", "python_version": "py2.py3", "requires_python": null, "size": 5485592, "upload_time": "2021-06-20T08:43:05", "upload_time_iso_8601": "2021-06-20T08:43:05.776684Z", "url": "https://files.pythonhosted.org/packages/39/46/6930f53184fa07fb4ad1a3753e3c135ca2dc39536a893dc862484fddd792/tfp_nightly-0.14.0.dev20210620-py2.py3-none-any.whl", "yanked": false, "yanked_reason": null } ], "upload_time": "2021-06-20 08:43:05", "github": true, "gitlab": false, "bitbucket": false, "github_user": "tensorflow", "github_project": "probability", "travis_ci": false, "coveralls": false, "github_actions": true, "lcname": "tfp-nightly" }

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