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A system to manage machine learning models for xgboost pyspark tensorflow sklearn keras

Project description

A python client has xgboost support for working with ModelDB machine learning management system.

This library makes it easy for users of the ModelDB ML management system to automatically catalog models built with xgboost pyspark tensorflow scikit-learn.

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.

Additional documentation on the light_api and scikit-learn client is also available.

Project details


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