Metadata-Version: 2.1
Name: modeldb-community
Version: 1.2
Summary: A system to manage machine learning models for xgboost pyspark tensorflow sklearn keras
Home-page: https://github.com/mitdbg/modeldb/tree/master/client
Author: Manasi Vartak, MIT DB Group
Author-email: modeldb@csail.mit.edu
License: MIT
Keywords: machine learning ML model catalog
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: statsmodels
Requires-Dist: matplotlib
Requires-Dist: patsy
Requires-Dist: scikit-learn
Requires-Dist: sklearn
Requires-Dist: thrift
Requires-Dist: pyyaml
Requires-Dist: requests
Requires-Dist: dpath
Requires-Dist: future
Requires-Dist: pymongo
Requires-Dist: xgboost
Requires-Dist: pymysql

=======
modeldb
=======

A python client has xgboost support for working with `ModelDB machine learning management system <http://modeldb.csail.mit.edu>`_.

This library makes it easy for users of the `ModelDB ML management system <http://modeldb.csail.mit.edu>`_ to automatically catalog models built with `xgboost pyspark tensorflow scikit-learn <scikit-learn.org>`_.

Extend by muller https://github.com/mullerhai/tsxgb.git

ModelDB is an end-to-end system for managing machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB runs on Python 2.X and 3.X and can be used with any ML environment via the ModelDB Light API.


Quick start
===========

Install
-------

You can install it using ``pip3`` directly from PyPI::


    pip3 install modeldb-community  #suggest python 3.6


Custom Configuration
--------------------

Once installed, you can create a custom syncing scheme setup by typing::

    python3 -m modeldb create_config

Unless an alternative syncing scheme is specialized, modeldb will use the packaged `syncer.json` defaults.


Use
---

This library requires a connection to a ModelDB server to work. You can see the `getting started docs here <https://github.com/mitdbg/modeldb/blob/master/docs/getting_started/scikit_learn.md>`_.

Additional documentation on the `light_api <light_api.md>`_ and `scikit-learn client <scikit_learn.md>`_ is also available.


