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workbench

Workbench: An easy to use Python API for creating and deploying AWS SageMaker Models

https://github.com/supercowpowers/workbench

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.6%) to scientific vocabulary

Keywords

aws big-data data-engineering machine-learning pandas python spark
Last synced: 5 months ago · JSON representation

Repository

Workbench: An easy to use Python API for creating and deploying AWS SageMaker Models

Basic Info
Statistics
  • Stars: 43
  • Watchers: 5
  • Forks: 2
  • Open Issues: 185
  • Releases: 7
Topics
aws big-data data-engineering machine-learning pandas python spark
Created about 3 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Security

README.md

Recent News

Themes

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

theme_dark theme_light
theme_quartz theme_quartz_dark

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.

Coming Soon: v0.9

We're getting ready for our v0.9 release. Here's the road map: 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.

workbench_new_light

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

private_saas_compare

API Installation

  • pip install workbench Installs Workbench

  • workbench Runs the Workbench REPL/Initial Setup

For the full instructions for connecting your AWS Account see:

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_api

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[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

Owner

  • Name: SuperCowPowers
  • Login: SuperCowPowers
  • Kind: organization
  • Email: info@supercowpowers.com
  • Location: United States of America

A cow that is super and has powers.

GitHub Events

Total
  • Create event: 77
  • Issues event: 47
  • Watch event: 2
  • Delete event: 14
  • Issue comment event: 11
  • Push event: 418
  • Pull request review comment event: 1
  • Pull request review event: 4
  • Gollum event: 1
  • Pull request event: 22
Last Year
  • Create event: 77
  • Issues event: 47
  • Watch event: 2
  • Delete event: 14
  • Issue comment event: 11
  • Push event: 418
  • Pull request review comment event: 1
  • Pull request review event: 4
  • Gollum event: 1
  • Pull request event: 22

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 4,880
  • Total Committers: 6
  • Avg Commits per committer: 813.333
  • Development Distribution Score (DDS): 0.017
Past Year
  • Commits: 2,649
  • Committers: 2
  • Avg Commits per committer: 1,324.5
  • Development Distribution Score (DDS): 0.001
Top Committers
Name Email Commits
Brian Wylie b****e@g****m 4,796
ScottKolmarWFI s****r@w****m 41
giupb g****i@g****m 27
luiz l****r@h****m 11
Sébastien DIDIER s****v@g****m 3
Andrew Huynh a****h@g****m 2
Committer Domains (Top 20 + Academic)
workflowinformatics.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 105
  • Total pull requests: 56
  • Average time to close issues: 11 days
  • Average time to close pull requests: about 6 hours
  • Total issue authors: 1
  • Total pull request authors: 3
  • Average comments per issue: 0.41
  • Average comments per pull request: 0.48
  • Merged pull requests: 52
  • Bot issues: 0
  • Bot pull requests: 4
Past Year
  • Issues: 38
  • Pull requests: 23
  • Average time to close issues: 8 days
  • Average time to close pull requests: about 8 hours
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 0.13
  • Average comments per pull request: 0.22
  • Merged pull requests: 20
  • Bot issues: 0
  • Bot pull requests: 4
View more stats
Top Authors
Issue Authors
  • brifordwylie (104)
Pull Request Authors
  • brifordwylie (51)
  • dependabot[bot] (4)
  • Ayub-Khan (1)
Top Labels
Issue Labels
usability (47) feature (43) code_quality (17) documentation (13) bug (11) performance (8) api (8) testing (7) model (7) on_deck (6) research (3) plugins (3) dash (3) critical (3) repl (2) endpoint (2) metadata (2) domain (1) glue (1) spark (1) application (1) ci_cd (1) algorithm (1) user_issue (1) transform (1) architecture (1) pandas (1) feature_set (1) in_progress (1)
Pull Request Labels
dependencies (4) python (2) usability (1)

Packages

  • Total packages: 3
  • Total downloads:
    • pypi 3,277 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 8
    (may contain duplicates)
  • Total versions: 116
  • Total maintainers: 2
pypi.org: workbench

Workbench: A Dashboard and Python API for creating and deploying AWS SageMaker Model Pipelines

  • Versions: 95
  • Dependent Packages: 0
  • Dependent Repositories: 4
  • Downloads: 3,248 Last month
Rankings
Stargazers count: 7.4%
Dependent repos count: 7.5%
Forks count: 8.6%
Average: 9.2%
Dependent packages count: 10.0%
Downloads: 12.3%
Maintainers (2)
briford Ankush.Chadda
Last synced: 6 months ago
pypi.org: workbench_web

Command Line Interface for Workbench

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 2
Rankings
Stargazers count: 7.4%
Forks count: 8.4%
Average: 9.4%
Dependent packages count: 10.0%
Dependent repos count: 11.6%
Maintainers (1)
briford
Last synced: about 1 year ago
pypi.org: workbench_cli

Command Line Interface for Workbench

  • Versions: 20
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 29 Last month
Rankings
Stargazers count: 7.4%
Forks count: 8.6%
Dependent packages count: 10.0%
Dependent repos count: 11.6%
Average: 17.2%
Downloads: 48.5%
Maintainers (1)
briford
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • numpy >=1.21.5
  • pandas >=1.3.5
  • scikit-learn >=1.1.1
setup.py pypi
  • pandas *
  • sagemaker *
  • scikit-learn *