https://github.com/bitgn/ml-pipelines

Application for managing machine learning pipelines and human workflows around them.
https://github.com/bitgn/ml-pipelines

django machine-learning python

Last synced: 8 days ago
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Application for managing machine learning pipelines and human workflows around them.

• Host: GitHub
• URL: https://github.com/bitgn/ml-pipelines
• Owner: bitgn
• License: bsd-2-clause
• Created: 2019-07-24T21:32:00.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-02-24T19:28:53.000Z (over 1 year ago)
• Last Synced: 2024-09-22T09:20:16.799Z (about 2 months ago)
• Topics: django, machine-learning, python
• Language: Python
• Size: 6.88 MB
• Stars: 7
• Watchers: 3
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ml-pipelines

Applications for managing machine learning pipelines and human

workflows around them, published under BSD-2 license.

At the moment of writing this repository includes only the MLP

Catalog - a web aplication for exploring projects and datasets stored

within the metadata library.

To understand context of the project, check out [STUDY.md](./STUDY.md).

## Explore Datasets

Find relevant data by searching across all datasets.



    





    



## View Projects

Organize elements of ML Pipelines into projects.



    





    



## View Datasets

View dataset properties and relations.



    





    



## Specs

Application functionality is being covered with [event-driven specs](https://abdullin.com/sku-vault/event-driven-verification/). This captures business logic and UX flows in non-fragile way.

## Getting started

Application is build and tested with Python 3.7.

Prerequisites:

- Python 3.7 with dev libraries: `apt install python3.7 python3.7-dev`

- [graphviz](https://www.graphviz.org): `apt install graphviz`

- [virtualenv](https://virtualenv.pypa.io/en/latest/)

To get started, go to the `mlp` folder and:

1) set up a _virtualenv_ in `mlp` folder and activate it;

3) `pip install -r requirements.txt` - install all the dependencies;

4) `python manage.py specs` - to run tests;

5) `python manage.py demo && python manage.py runserver` to fill up DB with demo data and launch the web UI (available at localhost:8000)