workbench

v0.8.89

Workbench: A Dashboard and Python API for creating and deploying AWS SageMaker Model Pipelines For more information about how to use this package see README

Latest version published 4 days ago

License: MIT

PyPI

GitHub

Package Health Score

48 / 100

security
No known security issues
popularity
Limited
maintenance
Inactive
community
Sustainable

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Popularity

Limited

GitHub Stars: 91
Forks: 19
Contributors: 3

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package workbench, we found that it has been starred 91 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Dec 21, 2024
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Inactive

archived

Commit Frequency

No Recent Commits

Open Issues: 91
Open PR: 0
Last Release: 4 days ago
Last Commit: 8 years ago

Further analysis of the maintenance status of workbench based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

We found that workbench demonstrates a positive version release cadence with at least one new version released in the past 3 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 3
Funding: No

This project has seen only 10 or less contributors.

We found a way for you to contribute to the project! Looks like workbench is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.9

Age: 10 years
Latest Release: 4 days ago
Dependencies: 0 Direct
Versions: 12
Maintainers: 0
Wheels: OS Independent

Readme

Recent News

Themes

Everyone knows that good data science requires... Some Awesome Themes!

All of the Dashboard pages, subpages, and plugins use our new ThemeManager() class. See Workbench Themes, also big thanks to our friends at Dash Bootstrap Templates

Workbench up on the AWS Marketplace

Powered by AWS® to accelerate your Machine Learning Pipelines development with our new Dashboard for ML Pipelines. Getting started with Workbench is a snap and can be billed through AWS.

Road Map: v0.9.0

We've used the feedback from our current beta testers to improve the framework and we've constructed a mini road map for the upcoming Workbench version 0.9.0. Please see Workbench RoadMaps

Welcome to Workbench

The Workbench framework makes AWS® both easier to use and more powerful. Workbench handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, Workbench makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Job, Athena, Feature Store, Models, and Endpoints, Workbench makes it easy to build production ready, AWS powered, machine learning pipelines.

Full AWS ML OverView

Health Monitoring 🟢
Dynamic Updates
High Level Summary

Drill-Down Views

Incoming Data
Glue Jobs
DataSources
FeatureSets
Models
Endpoints

Private SaaS Architecture

Secure your Data, Empower your ML Pipelines

Workbench is architected as a Private SaaS (also called BYOC: Bring Your Own Cloud). This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. Workbench deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support. By using Workbench, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. Workbench Private SaaS Architecture

API Installation

pip install workbench Installs Workbench
workbench Runs the Workbench REPL/Initial Setup

For the full instructions for connecting your AWS Account see:

Getting Started: Initial Setup
One time AWS Onboarding: AWS Setup

Workbench Presentations

Even though Workbench makes AWS easier, it's taking something very complex (the full set of AWS ML Pipelines/Services) and making it less complex. Workbench has a depth and breadth of functionality so we've provided higher level conceptual documentation See: Workbench Presentations

Workbench Documentation

The Workbench documentation Workbench Docs covers the Python API in depth and contains code examples. The documentation is fully searchable and fairly comprehensive.

The code examples are provided in the Github repo examples/ directory. For a full code listing of any example please visit our Workbench Examples

Questions?

The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or chat us up on Discord

Workbench Beta Program

Using Workbench will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a Workbench Beta Tester, contact us at workbench@supercowpowers.com.

Using Workbench with Additional Packages

pip install workbench             # Installs Workbench with Core Dependencies
pip install 'workbench[ml-tools]' # + Shap and NetworkX
pip install 'workbench[chem]'     # + RDKIT and Mordred (community)
pip install 'workbench[ui]'       # + Plotly/Dash
pip install 'workbench[dev]'      # + Pytest/flake8/black
pip install 'workbench[all]'      # + All the things :)

*Note: Shells may interpret square brackets as globs, so the quotes are needed

Contributions

If you'd like to contribute to the Workbench project, you're more than welcome. All contributions will fall under the existing project license. If you are interested in contributing or have questions please feel free to contact us at workbench@supercowpowers.com.

® Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates

FAQs

What is workbench?

Workbench: A Dashboard and Python API for creating and deploying AWS SageMaker Model Pipelines. Visit Snyk Advisor to see a full health score report for workbench, including popularity, security, maintenance & community analysis.

Is workbench popular?

The python package workbench receives a total of 142 weekly downloads. As such, workbench popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is workbench well maintained?

We found indications that workbench is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is workbench safe to use?

The python package workbench was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 22 December-2024, at 18:24 (UTC).

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