Intro

Rok Roškar, Swiss Data Science Center, ETH Zürich

30 May 2023

Outline

2

Renku Walkthrough

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What’s ahead?

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What is Renku?

Demo

Renku enables “sustainable” data science

1. Done today with tomorrow in mind
2. Individual work benefits institution and community
3. All components form a functioning ecosystem

Demo

4

a story…

Development

Publication

Option 2

Renku streamlines the behind-the-scenes work, so you can focus on the fun parts of data science!

Demo

5

Development

Publication

Option 2

Renku streamlines the behind-the-scenes work, so you can focus on the fun parts of data science!

What is Renku?

Making sustainable data science practices accessible & easy

Reusable Data

Containerized Environments

Comprehensible Workflows

Renku CLI

Renku on your own machine

Metadata-first data packages

Simple package setup for popular languages

Encode relationships between code and data

RenkuLab

Flexible & efficient compute infrastructure

Shareable Code

The foundation for collaborative Data Science

Demo

walkthrough

7

Demo

Takeaways

• A Renku Project encapsulates all relevant code and data.
• All activity on a project is tracked via git and viewable in GitLab.
• Projects are shareable and searchable across an organization.

Versioned Code and Data

The tools you need for collaborative Data Science

Takeaways

• A Renku Dataset is a collection of data useful for the project, wrapped in metadata.
• Datasets can be imported from and exported to e.g. Dataverse
• The data itself does not have to live on our servers; can be backed by external storage
• A Dataset already in Renku can quickly be added to other projects

Reusable Data

Metadata-first data packages

Takeaways

• All Renku Projects are fully containerized out of the box.
• Renku Projects can be opened in the browser - no need to install anything!
• Use conda or pip to manage the packages in your Renku Project (or other language-specific tooling)
• Use built-in templates or bring your own

Containerized Environments

Simple package setup for popular languages

Takeaways

• Workflows encode how all the code and data files in a project are related, making it faster to find and use what you need.
• You can encode your workflows as-you-go on the command line, or via a file.
• Renku has its own workflow system, or we can suggest a tool that fits your use case!

The Renku Stack

https://renkulab.io

command-line interface (CLI)

The Renku stack compared with other tools

Version control

Interactive sessions

Environment Management

Workflows

Renku: A preconfigured and integrated cloud environment

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Harness code & data versioning

Use the knowledge graph for <creativity>

Containerize your compute environment

Track & show file lineage via a workflow tool

Renku helps data scientists improve their computational skills, wherever they are on their journey.

Why use Renku?

What’s ahead?

15

Demo

Coming up

• More flexible/easier “on-boarding” of users, projects, data
• More comprehensive overviews of where data is used
• Renku as the “middle layer”/connector of code, compute, data
• Better integration with data providers!
• “One-click” access to compute with data? Getting hands-on with data quickly
• More direct visibility for datasets with DOIs
• Publishing datasets from renkulab directly (only using CLI atm)
• Our focus is making data “alive” – how can we do better? What would you imagine to be useful for your community?

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Demo

We want to hear from you!

🙋 Try out Renku

• renkulab.io - Public

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📃 Renku Docs

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❓ Run into a problem?

• Post on Discourse (our forum)
• Submit a bug report

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💡 Feature Request?

• Discourse!

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Demo