jupyter-deploy 0.6.5

CLI based tool to deploy Jupyter applications that integrates with infrastructure as code frameworks.

Jupyter Deploy

Jupyter deploy is an open-source command line interface tool (CLI) to deploy and manage interactive applications such as JupyterLab to the Cloud.

Documentation: jupyter-deploy.readthedocs.io

Templates

The jupyter-deploy CLI interacts with templates: infrastructure-as-code packages that you can use to create your own project and deploy resources in your own cloud provider account.

Templates are nominally python packages distributed on PyPI. You can install and manage templates in your virtual environment with pip or uv. jupyter-deploy automatically finds the templates installed in your Python environment.

jupyter-deploy ships with a default template: jupyter-deploy-tf-aws-ec2-base. Refer to jupyter-deploy-tf-aws-ec2-base on PyPI for instructions on setting up the AWS infrastructure needed for your deployment.

Installation

Consider creating or activating a virtual environment.

We recommend using uv.

From your uv environment:

uv add jupyter-deploy

Or with pip:

pip install jupyter-deploy

The CLI

Entry points

From a terminal, run:

jupyter-deploy --help

# or use the alias
jd --help

# or use the jupyter CLI
jupyter deploy --help

Start a project

First create a new project directory:

mkdir my-jupyter-deployment
cd my-jupyter-deployment

In the rest of this page, we will use the default template.

# Get started with the default template
jupyter-deploy init .

# Or use the init flags to select another template that you installed in your virtual environment
jupyter-deploy init --help

# For example, the AWS EC2 base template
jupyter-deploy init -E terraform -P aws -I ec2 -T base . 

Configure your project

There are two ways to configure your project:

---

File based: Edit the variables.yaml file:

• add required variable values in the required and required_sensitive section
• optionally override default values in the overrides section

Then run:

jupyter-deploy config

---

Interactive experience: Alternatively, fill in the variable values from your terminal with:

# Discover the variables available for your specific template
jupyter-deploy config --help

# Run the interactive configuration and set the variables values as prompted
jupyter-deploy config

# Optionally save sensitive values to your project files
# Sensitive values are passwords, secret keys or API tokens that your applications
# need to access at runtime.
jupyter-deploy config -s

# Update a variable value afterwards (variable names depends on the template you use).
jupyter-deploy config --instance-type t3.small

Deploy your project

The next step is to actually create your infrastructure

jupyter-deploy up

Access your application

Once the project was successfully deployed, open your application in your web browser with:

jupyter-deploy open

You will be prompted to authenticate. You can share this URL with collaborators, they will prompted to authenticate on their own web browser.

Turn on and off your compute instance

The default template supports temporarily turning off your instance to reduce your cloud bill.

# Retrieve the current status of your compute instance
jupyter-deploy host status

# Stop an instance
jupyter-deploy host stop

# Restart it
jupyter-deploy host start

# You may also need to start the containers that run your application
jupyter-deploy server start

Winddown your resources

To delete all the resources, run:

jupyter-deploy down

License

The jupyter-deploy CLI is licensed under the MIT License.