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The deno jupyter command provides integration with Jupyter notebooks, allowing you to use Deno as a JavaScript and TypeScript kernel.

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Usage

deno jupyter [OPTIONS]

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Description

This command installs and manages the Deno kernel for Jupyter notebooks. Once installed, you can create and run Jupyter notebooks that execute JavaScript and TypeScript code using Deno. Without any flags, the command displays the current installation status.

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Options

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--install
boolean
default:"false"
Install the Deno kernel for Jupyter.
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--kernel
boolean
default:"false"
Start the Deno kernel (typically called by Jupyter, not directly by users).
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--conn-file
string
Path to the connection file (used internally when starting the kernel).
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--name
string
Custom name for the kernel installation.
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--display
string
Custom display name for the kernel.
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--force
boolean
default:"false"
Force reinstallation even if already installed.

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Examples

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Check installation status

deno jupyter

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Install the Deno kernel

deno jupyter --install

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Install with a custom name

deno jupyter --install --name deno-custom

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Install with a custom display name

deno jupyter --install --display "Deno (Custom)"

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Force reinstall

deno jupyter --install --force

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Using Jupyter with Deno

After installation, you can use the Deno kernel in Jupyter:
  1. Install the kernel: 
    deno jupyter --install
  2. Start Jupyter: 
    jupyter notebook
    or 
    jupyter lab
  3. Create a new notebook and select “Deno” as the kernel
  4. Write and execute Deno code in notebook cells

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Features

  • Full TypeScript support
  • Access to Deno APIs
  • npm package support
  • JSR package support
  • Top-level await
  • Rich display output
  • REPL-like experience

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Example Notebook Code

// Import from npm
import chalk from "npm:chalk@5";

console.log(chalk.blue("Hello from Deno in Jupyter!"));

// Use Deno APIs
const data = await Deno.readTextFile("./data.txt");
console.log(data);

// Display rich output
const result = { message: "Success", value: 42 };
result

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Notes

  • The command is marked as unstable and may change in future versions
  • Requires Jupyter to be installed separately (pip install jupyter)
  • The kernel runs with all permissions by default (for notebook convenience)
  • Test output capturing is currently not fully functional

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