Metadata-Version: 2.1
Name: pipedown
Version: 0.0.4
Summary: A data science pipelining framework for Python
Home-page: https://github.com/brendanhasz/pipedown
Author: Brendan Hasz
Author-email: winsto99@gmail.com
License: MIT
Description: # Pipedown :shushing_face:
        
        Pipedown is a machine learning model pipelining package for Python.  It doesn't
        really *do* much, it just lets you define a directed acyclic graph (DAG) of
        modeling steps, each of which is defined as a class with `fit` and `run`
        methods (like [scikit-learn](https://scikit-learn.org)), and it runs your nodes
        in order and provides a visualization of the DAG.
        
        Pipedown is designed around:
        
        * A **single code path**: use the same code for training, validation, and inference on new test data.
        * **Modularity** and **testability**: each node is defined as its own class with `fit()` and `run()` methods, making it easy to unit test each node.
        * **Visibility**: pipedown comes with an html viewer to explore the structure of your DAGs, and shows links to the source code associated with each node.
        * **Portability**: pipedown models can easily be trained in one environment (e.g. a batch job), serialized, and then loaded into another environment (e.g. a model server) for inference.
        * **State**: DAG nodes can store state; they aren't just stateless functions.
        * **Flexibility**: pipedown allows you to define models as DAGs instead of just linear pipelines (like [scikit-learn](https://scikit-learn.org/)), but doesn't force your project to have a specific file structure (like [Kedro](https://github.com/quantumblacklabs/kedro)).
        
        Pipedown is NOT an ETL / data engineering / task scheduler tool - for that use
        something like Airflow, Argo, Dask, Prefect, etc.  You can do some basic and
        inefficient data processing with Pipedown, but really it's focused on creating
        portable model pipelines.
        
        
        * Git repository: [http://github.com/brendanhasz/pipedown](http://github.com/brendanhasz/pipedown)
        * Documentation:
        * Bug reports: [http://github.com/brendanhasz/pipedown/issues](http://github.com/brendanhasz/pipedown/issues)
        
        Still in the super early stages - don't use this yet!
        
        ## Requirements
        
        To use the visualization tools, you need to have
        [graphviz](https://graphviz.org/) installed.  On Ubuntu, you can install with:
        
        ```bash
        sudo apt-get install graphviz
        ```
        
        ## Installation
        
        Just use pip!
        
        ```bash
        pip install pipedown
        ```
        
        ## Getting Started
        
        Todo...
        
        ## Thanks
        
        Syntax higlighting in the DAG viewer is done using [higlight.js](https://highlightjs.org/)
        with the great Atom One Dark ReasonML theme by Gidi Morris / Daniel Gamage.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Description-Content-Type: text/x-rst
Provides-Extra: dev
