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HTTP-based interface to Stan, a package for Bayesian inference.

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HTTP interface to Stan, a package for Bayesian inference.

An HTTP 1.1 interface to the Stan C++ package, httpstan is a shim that allows users to interact with the Stan C++ library using a Web API. The package is intended for use as a universal backend for frontends which know how to make HTTP requests. The primary audience for this package is developers.

In addition to providing all the functionality of the command-line interface to Stan (CmdStan) over HTTP 1.1, httpstan provides:

  • Automatic caching of compiled Stan models
  • Automatic caching of samples from Stan models
  • Parallel sampling

Important Disclaimer

httpstan is experimental software. This software is not intended for general use.

httpstan currently requires Python 3.6 as it uses asynchronous generators (PEP525). Support for Python 3.5 may emerge at some point. No older versions of Python will be supported.

Background

httpstan is a shim allowing clients speaking HTTP to call functions in the Stan C++ package’s stan::services namespace. httpstan was originally developed as a “backend” for a Python interface to Stan, PyStan.

Install

python3 -m pip install httpstan

Install from Source

A working copy of protoc is needed to build from source. One way to get this is to install grpcio-tools from PyPI with python3 -m pip install grpcio-tools.

make cython  # generate C++ code from Cython .pyx files
make protos  # generate Python modules for protocol buffer schemas
python3 setup.py install

Usage

After installing httpstan, running the module will begin listening on localhost, port 8080:

python3 -m httpstan

In a different terminal, make a POST request to http://localhost:8080/v1/models with Stan program code to compile the program:

curl -X POST -H "Content-Type: application/json" \
    -d '{"program_code":"parameters {real y;} model {y ~ normal(0,1);}"}' \
    http://localhost:8080/v1/models

This request will return a model id similar to the following:

{"model": {"id": "8137474d19926b0aa8efd4f1d3944131d59269d97a7bd8dab8e79d667eb314df"}}

(The model id will be different on different platforms and with different versions of Stan.)

To draw samples from this model using default settings, we make the following request:

curl -X POST -H "Content-Type: application/json" \
    -d '{"type":"stan::services::sample::hmc_nuts_diag_e_adapt"}' \
    http://localhost:8080/v1/models/8137474d19926b0aa8efd4f1d3944131d59269d97a7bd8dab8e79d667eb314df/actions

This request will return samples from the normal distribution. The output is taken directly from the output of the relevant function defined in the Stan C++ package. Consult the Stan C++ documentation for details.

Contribute

Contribution guidelines are described in CONTRIBUTE.rst.

License

ISC License.

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Filename, size & hash SHA256 hash help File type Python version Upload date
httpstan-0.4.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (30.0 MB) Copy SHA256 hash SHA256 Wheel cp36 Jul 7, 2018
httpstan-0.4.0-cp36-cp36m-manylinux1_x86_64.whl (29.6 MB) Copy SHA256 hash SHA256 Wheel cp36 Jul 7, 2018
httpstan-0.4.0.tar.gz (17.1 MB) Copy SHA256 hash SHA256 Source None Jul 7, 2018

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