{ "info": { "author": "Manasi Vartak, MIT DB Group", "author_email": "modeldb@csail.mit.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Topic :: Scientific/Engineering" ], "description": "=======\nmodeldb\n=======\n\nA python client has xgboost support for working with `ModelDB machine learning management system `_.\n\nThis library makes it easy for users of the `ModelDB ML management system `_ to automatically catalog models built with `xgboost pyspark tensorflow scikit-learn `_.\n\nExtend by muller https://github.com/mullerhai/tsxgb.git\n\nModelDB 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.\n\n\nQuick start\n===========\n\nInstall\n-------\n\nYou can install it using ``pip3`` directly from PyPI::\n\n\n pip3 install modeldb-community #suggest python 3.6\n\n\nCustom Configuration\n--------------------\n\nOnce installed, you can create a custom syncing scheme setup by typing::\n\n python3 -m modeldb create_config\n\nUnless an alternative syncing scheme is specialized, modeldb will use the packaged `syncer.json` defaults.\n\n\nUse\n---\n\nThis library requires a connection to a ModelDB server to work. 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