Skip to main content

Turns DLHub metadata into functional Python objects

Project description

# DLHub home_run

[![Build Status](https://travis-ci.org/DLHub-Argonne/home_run.svg?branch=master)](https://travis-ci.org/DLHub-Argonne/home_run) [![Coverage Status](https://coveralls.io/repos/github/DLHub-Argonne/home_run/badge.svg?branch=master)](https://coveralls.io/github/DLHub-Argonne/home_run?branch=master) [![PyPI version](https://badge.fury.io/py/home-run.svg)](https://badge.fury.io/py/home-run)

home_run is a tool used by [the Data and Learning Hub for Science](https://www.dlhub.org) internally to turn a bunch of files and a recipe into an functional Python object.

## Installation

home_run is on PyPi. Install it by calling

`bash pip install home_run `

home_run is designed to be as light-weight as possible, and has only requests as a dependency.

## Technical Details

The key ingredients for using home_run are files describing a function that will be served by DLHub. These include a metadata file describing the servable (see [dlhub_sdk](http://github.com/dlhub-argonne/dlhub_sdk) for tools for creating these files, and [dlhub_schemas](http://github.com/dlhub-argonne/dlhub_schemas) for the schemas), and the actual files that make up the servable (e.g., a Keras hdf5 file).

Each particular type of servable has its own recipe for going from these files to a Python object. All recipes are a subclass of BaseServable, which provides the general framework for defining a servable object. Each subclass has a matching BaseMetadataModel class in dlhub_sdk. For example, the type of servable that can be described by the PythonStaticMethodModel can be run by the PythonStaticMethodServable.

## Project Support This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

home_run-0.3.0.tar.gz (9.2 kB view details)

Uploaded Source

Built Distribution

home_run-0.3.0-py2.py3-none-any.whl (10.7 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file home_run-0.3.0.tar.gz.

File metadata

  • Download URL: home_run-0.3.0.tar.gz
  • Upload date:
  • Size: 9.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.3

File hashes

Hashes for home_run-0.3.0.tar.gz
Algorithm Hash digest
SHA256 483a2c818bc70da19982420f06df375cb0132017d6abf930f51ff4db90b129e0
MD5 1031bf028ecb25d7e4dc71d509701e5b
BLAKE2b-256 3e3f2546b45aff77a4873972011ec19d614521f28a41997856a64565227d6ebb

See more details on using hashes here.

File details

Details for the file home_run-0.3.0-py2.py3-none-any.whl.

File metadata

  • Download URL: home_run-0.3.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 10.7 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.3

File hashes

Hashes for home_run-0.3.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 c146b7bf4dadb0e390b90cc816276ebc2985d12abec186dad681564e48f04467
MD5 9be885c2593ecbe1feff604e7631377b
BLAKE2b-256 29f5741e1bf74359d1c0f75f8e5fa79efd750e323f7a98cdad95f7ba2e4c2e81

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page