semvec/pypi-packages huggingface dataset:
dj-dash-repo 0.1.0
This is package for django site analytics
License: MIT
Keywords: dj-dash-repo

=============================
dj-dash
=============================

.. image:: https://badge.fury.io/py/dj-dash-repo.svg
    :target: https://badge.fury.io/py/dj-dash-repo

.. image:: https://travis-ci.org/rahul1809/dj-dash-repo.svg?branch=master
    :target: https://travis-ci.org/rahul1809/dj-dash-repo

.. image:: https://codecov.io/gh/rahul1809/dj-dash-repo/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/rahul1809/dj-dash-repo

This is package for django site analytics

Documentation
-------------

The full documentation is at https://dj-dash-repo.readthedocs.io.

Quickstart
----------

Install dj-dash::

    pip install dj-dash-repo

Add it to your `INSTALLED_APPS`:

.. code-block:: python

    INSTALLED_APPS = (
        ...
        'dashboard.apps.DashboardConfig',
        ...
    )

Add dj-dash's URL patterns:

.. code-block:: python

    from dashboard import urls as dashboard_urls


    urlpatterns = [
        ...
        url(r'^', include(dashboard_urls)),
        ...
    ]

Features
--------

* TODO

Running Tests
-------------

Does the code actually work?

::

    source <YOURVIRTUALENV>/bin/activate
    (myenv) $ pip install tox
    (myenv) $ tox

Credits
-------

Tools used in rendering this package:

*  Cookiecutter_
*  `cookiecutter-djangopackage`_

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`cookiecutter-djangopackage`: https://github.com/pydanny/cookiecutter-djangopackage




History
-------

0.1.0 (2018-11-18)
++++++++++++++++++

* First release on PyPI.
modeldb-community 1.4.1
A system to manage machine learning models for xgboost pyspark tensorflow sklearn keras
License: MIT
Keywords: machine learning ML model catalog

=======
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.