项目作者: indigo-dc

项目描述 :
A REST API to serve machine learning and deep learning models
高级语言: Python
项目地址: git://github.com/indigo-dc/DEEPaaS.git
创建时间: 2018-05-28T17:32:58Z
项目社区:https://github.com/indigo-dc/DEEPaaS

开源协议:Apache License 2.0

下载


DEEPaaS

fair-software.eu
OpenSSF Best Practices
GitHub license
GitHub release
PyPI
Python versions
Build Status
Documentation Status
DOI
Zenodo DOI

AI4EOSC logo
DEEP-Hybrid-DataCloud logo

DEEP as a Service API (DEEPaaS API) is a REST API built on
aiohttp that allows to provide easy access to
machine learning, deep learning and artificial intelligence models. By using
the DEEPaaS API users can easily run a REST API in front of their model, thus
accessing its functionality via HTTP calls. DEEPaaS API leverages the OpenAPI
specification
.

Documentation

The DEEPaaS documentation is hosted on Read the Docs.

Quickstart

The best way to quickly try the DEEPaaS API is through:

  1. make run

This command will install a virtualenv (in the virtualenv directory) with
DEEPaaS and all its dependencies and will run the DEEPaaS REST API, listening
on 127.0.0.1:5000. If you browse to http://127.0.0.1:5000 you will get the
Swagger documentation page (i.e. the Swagger web UI).

Develop mode

If you want to run the code in develop mode (i.e. pip install -e), you can
issue the following command before:

  1. make develop

Citing

DOI

If you are using this software and want to cite it in any work, please use the
following:

Lopez Garcia, A. “DEEPaaS API: a REST API for Machine Learning and
Deep Learning models”. In: Journal of Open Source Software 4(42) (2019),
pp. 1517. ISSN: 2475-9066. DOI: 10.21105/joss.01517

You can also use the following BibTeX entry:

  1. @article{Lopez2019DEEPaaS,
  2. journal = {Journal of Open Source Software},
  3. doi = {10.21105/joss.01517},
  4. issn = {2475-9066},
  5. number = {42},
  6. publisher = {The Open Journal},
  7. title = {DEEPaaS API: a REST API for Machine Learning and Deep Learning models},
  8. url = {http://dx.doi.org/10.21105/joss.01517},
  9. volume = {4},
  10. author = {L{\'o}pez Garc{\'i}a, {\'A}lvaro},
  11. pages = {1517},
  12. date = {2019-10-25},
  13. year = {2019},
  14. month = {10},
  15. day = {25},}

Acknowledgements

This software has been developed within the DEEP-Hybrid-DataCloud (Designing
and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud)
project that has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 777435.