COC
CoCalc
Collaborative cloud notebooks (Jupyter, LaTeX, SageMath) with real-time editing
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
AI‑focused extensions that turn JupyterLab into a pipeline hub
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.
When teams consider Elyra, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
Elyra works with JupyterLab 4.x (latest) and 3.x for earlier releases; install the matching Elyra version per the release notes.
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.
Yes, Elyra provides ready‑made Docker images (elyra/elyra) that launch JupyterLab with all extensions pre‑installed.
Hybrid runtime support leverages Jupyter Enterprise Gateway, but local execution works without it.
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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