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Elyra

AI‑focused extensions that turn JupyterLab into a pipeline hub

Notebook & Data Science Platforms

Elyra adds visual pipeline editing, batch job execution, code snippets, remote runtimes, debugging, LSP and Git support to JupyterLab, streamlining AI and data‑science workflows.

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Overview

Highlights

Visual Pipeline Editor for drag‑and‑drop workflow design
Hybrid runtime execution via Jupyter Enterprise Gateway
Integrated (experimental) debugger for Python scripts
Built‑in LSP and Git integration for collaborative development

Pros

  • Seamless integration with existing JupyterLab environments
  • Supports both Python and R scripts
  • Enables remote batch execution of notebooks
  • Provides version control directly in the UI

Considerations

  • Debugger is marked experimental and may be unstable
  • Requires specific Node.js and Python versions
  • Remote runtimes depend on Jupyter Enterprise Gateway setup
  • Some advanced features need additional extensions

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Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.

Fit guide

Great for

  • Data scientists building reproducible AI pipelines
  • Teams that need Git versioning inside notebooks
  • Users who want to run notebooks as batch jobs on clusters
  • Developers seeking code snippets and LSP support

Not ideal when

  • Environments that only use classic Jupyter Notebook
  • Projects requiring full‑scale production orchestration tools
  • Users needing a stable, production‑ready debugger
  • Organizations unable to meet Node.js 22 / Python 3.9+ prerequisites

How teams use it

Create and run end‑to‑end ML pipelines

Design visual pipelines, connect notebook steps, and execute them on remote clusters with a single click.

Batch execution of scripts

Run Python or R scripts as scheduled jobs from within JupyterLab, capturing logs and outputs.

Collaborative development with Git

Commit, branch, and merge notebook changes directly from the UI, keeping data‑science work versioned.

Debugging complex notebooks

Use the integrated (experimental) debugger to step through code, inspect variables, and resolve errors faster.

Tech snapshot

Python72%
TypeScript21%
CSS2%
Jinja2%
Jupyter Notebook1%
Makefile1%

Tags

kubeflowaibinderanacondaairflowkubeflow-pipelinesjupyterlab-extensionapache-airflowpypihacktoberfestmachine-learningpipelinesjupyterlab-extensionspythonelyranotebook-jupyternotebooksjupyterlab-notebooksjupyterlabdocker

Frequently asked questions

What JupyterLab versions does Elyra support?

Elyra works with JupyterLab 4.x (latest) and 3.x for earlier releases; install the matching Elyra version per the release notes.

How do I run a notebook as a batch job?

Select the notebook, choose 'Run as batch job' from the Elyra menu, configure the runtime, and submit; the job runs on the configured remote cluster.

Can I use Elyra with Docker?

Yes, Elyra provides ready‑made Docker images (elyra/elyra) that launch JupyterLab with all extensions pre‑installed.

Do I need Jupyter Enterprise Gateway?

Hybrid runtime support leverages Jupyter Enterprise Gateway, but local execution works without it.

Is the debugger production‑ready?

The debugger is marked experimental; it works for many cases but may have limitations and is not recommended for critical production debugging.

Project at a glance

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LicenseApache-2.0
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Last commitlast week
Primary languagePython

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