httpstan

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

make cython  # genreate 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.

Metadata

Release files for httpstan 0.2.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for httpstan 0.2.5
File	Interpreter	ABI	Platform	Reset
httpstan-0.2.5-cp36-cp36m-manylinux1_x86_64.whl 48.6 MB Jan 10, 2018	CPython 3.6	CPython 3.6 pymalloc	Linux glibc 2.5+ x86-64	Details
httpstan-0.2.5-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 25.3 MB Jan 10, 2018	CPython 3.6	CPython 3.6 pymalloc	macOS 10.9+ Intel (x86-64, i386), macOS 10.10+ Intel (x86-64, i386), macOS 10.9+ x86-64, macOS 10.6+ Intel (x86-64, i386), macOS 10.10+ x86-64	Details

Total release size: 73.9 MB

Release files / httpstan-0.2.5-cp36-cp36m-manylinux1_x86_64.whl

Download URL	httpstan-0.2.5-cp36-cp36m-manylinux1_x86_64.whl
Size	48.6 MB
Tags	CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`4c1490f461bab3b7632f8d659034a750616d5ef246ef2cf99b855faf5db6117f`
BLAKE2b-256 checksum How to use checksums	`bb98e3f927c0e85d756c8e702ca4d59557148f8dde13874ce1b1dff1a71ad5fc`
Upload date	Jan 10, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No

Release files / httpstan-0.2.5-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

Download URL	httpstan-0.2.5-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
Size	25.3 MB
Tags	CPython 3.6 CPython 3.6 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`7c051d63cb080cbc02cbc433f27ac7ff83b35b6f14215f6da087281a9f87e44f`
BLAKE2b-256 checksum How to use checksums	`26f6a8038a2dd7f7f25664a2995da9129958bf8b4e74ee3f449f53947f9b1aa2`
Upload date	Jan 10, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No