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jupyter-notebook

Execute Jupyter notebooks on local Python kernels and Databricks clusters.

11|Updated Mar 12, 2023
Search Tags:#execution#kernel#jupyter#notebook#databricks
One-click install
npx skills add https://github.com/i9wa4/dotfiles --skill jupyter-notebook
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-notebook
Source: https://github.com/i9wa4/dotfiles/tree/main/dot.config/claude/skills/jupyter-notebook
Command: npx skills add https://github.com/i9wa4/dotfiles --skill jupyter-notebook

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill guides running Jupyter notebooks, including using Databricks kernels and integrating with Databricks runtime.

Core Features & Use Cases

  • Kernel guidance: Instructions for Databricks kernel and local kernels.
  • Notebook execution: Standard method to execute a notebook, with uv.
  • Databricks integration: Steps to run notebooks on Databricks clusters.

Quick Start

uv run jupyter execute <notebook_path> --inplace --timeout=300

Frequently Asked Questions about jupyter-notebook

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I execute a Jupyter notebook with a local Python kernel?▼

Execute a Jupyter notebook using uv with the command `uv run jupyter execute <notebook_path>`. This runs the notebook against your local Python kernel. Use `--inplace` to write results back to the file and `--timeout=300` to set execution limits.

Can I run Jupyter notebooks on a Databricks cluster?▼

Yes. Jupyter notebook execution supports Databricks kernels by setting DATABRICKS_HOST, DATABRICKS_TOKEN, and DATABRICKS_CLUSTER_ID environment variables, then specifying the kernel with `--kernel_name`. This enables full-notebook execution on Databricks clusters.

What are the prerequisites for running notebooks with this execution method?▼

You need uv or pip installed and a Jupyter notebook file. For Databricks execution, configure DATABRICKS_HOST, DATABRICKS_TOKEN, and DATABRICKS_CLUSTER_ID environment variables beforehand.

How do I control timeout behavior when executing notebooks?▼

Use the `--timeout` parameter to set the total execution limit and `--startup_timeout` to control kernel startup time. Both accept seconds as values, allowing fine-grained control over long-running notebook executions.

Does notebook execution work with both local and remote kernels?▼

Yes. Notebook execution supports local Python kernels and Databricks cluster kernels. Select your target with `--kernel_name` and configure credentials for remote execution, or use defaults for local kernel execution.

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