Jupyter Integration

Run and monitor Jupyter notebook servers directly from Stanza’s CLI. The integration provides persistent server management, real-time notebook monitoring, and automatic cell output logging - all designed to streamline interactive development workflows.

Quick Start

Installation Requirements

Jupyter integration requires additional dependencies:

$
pip install "cq-stanza[notebook]"

This installs:

• jupyter_server for server management
• jupyter_core for runtime discovery
• Required dependencies for log streaming and monitoring

Start a Jupyter server in your project directory:

$
# Start server on default port 8888
>
stanza jupyter start
>
>
# Start in specific directory with custom port
>
stanza jupyter start /path/to/notebooks --port 8889
>
>
# Check server status
>
stanza jupyter status
>
>
# Open Jupyter in browser
>
stanza jupyter open

The server runs in the background and persists across terminal sessions, making it ideal for long-running experiments on remote machines.

Server Management

Starting the Server

Launch a persistent Jupyter server that survives terminal closure:

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from stanza.jupyter import start
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from pathlib import Path
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# Start server programmatically
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state = start(notebook_dir=Path.cwd(), port=8888)
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print(f"Server running at: {state['url']}")
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print(f"PID: {state['pid']}")

Or via CLI:

$
stanza jupyter start

The server automatically configures:

• Auto-logging of cell outputs to notebook_name.log files
• Authentication tokens for secure access
• Background process management with graceful shutdown

Checking Status

Get server information including uptime and URL:

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from stanza.jupyter import status
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server_status = status()
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if server_status:
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    print(f"PID: {server_status['pid']}")
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    print(f"URL: {server_status['url']}")
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    print(f"Uptime: {server_status['uptime_seconds']:.0f}s")
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    print(f"Root: {server_status['root_dir']}")
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else:
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    print("No server running")

Via CLI:

$
stanza jupyter status

Stopping the Server

Gracefully shutdown using escalating strategies (REST API CMD → SIGTERM → SIGKILL):

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from stanza.jupyter import stop
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stop()

Via CLI:

$
stanza jupyter stop

Notebook Monitoring

Listing Active Sessions

View all running notebook kernels with their log file metadata:

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from stanza.jupyter import list_sessions
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sessions = list_sessions()
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for session in sessions:
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    print(f"Notebook: {session['notebook_path']}")
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    print(f"Log: {session['log_path']}")
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    print(f"Lines: {session['line_count']}")

Via CLI:

$
stanza jupyter list

Viewing Logs

Monitor notebook output in real-time without interrupting execution:

$
# List all notebook logs
>
stanza jupyter logs
>
>
# Tail a specific notebook's output (Ctrl+C to detach)
>
stanza jupyter logs my_notebook.ipynb
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>
# Show last 20 lines of context
>
stanza jupyter logs my_notebook.ipynb -n 20

The logs command is useful for monitoring long-running experiments while preserving the ability to detach without affecting the notebook kernel.

Attaching to Notebooks

Connect directly to a notebook kernel with interactive control:

$
# Attach with kernel control (Ctrl+C kills kernel, ESC exits)
>
stanza jupyter attach my_notebook.ipynb
>
>
# Show more initial context
>
stanza jupyter attach my_notebook.ipynb -n 30

When attached:

• Ctrl+C: Kills the notebook kernel and exits
• ESC (twice): Exits without killing the kernel
• Output streams in real-time directly to your terminal

This mode is ideal for active development and debugging when you need full control over kernel lifecycle.

Python API Examples

Complete Server Workflow

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from pathlib import Path
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from stanza.jupyter import start, status, list_sessions, stop
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# Start server
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notebook_dir = Path("./experiments")
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state = start(notebook_dir=notebook_dir, port=8888)
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print(f"Started: {state['url']}")
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# Check status
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server_status = status()
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print(f"Uptime: {server_status['uptime_seconds']:.0f}s")
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# List running notebooks
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sessions = list_sessions()
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print(f"Active notebooks: {len(sessions)}")
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# Cleanup
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stop()

Programmatic Log Streaming

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from pathlib import Path
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from stanza.jupyter import log_stream
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# Follow a notebook's log file
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log_file = Path("./experiments/analysis.log")
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log_stream.follow(log_file, lines=10)

Kernel Management

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from stanza.jupyter import kill_kernel
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# Stop a specific notebook's kernel
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kill_kernel("analysis.ipynb")

Auto-Logging

When you start the Jupyter server through Stanza, cell outputs are automatically logged to {notebook_name}.log files in the same directory as each notebook. This enables:

• Persistent record of all cell executions
• Real-time monitoring via stanza jupyter logs
• Post-experiment analysis without re-running cells

Log files capture stdout, stderr, and display outputs, providing a complete record of your notebook’s execution history.

Remote Development

For SSH sessions to lab computers, use port forwarding to access Jupyter in your local browser:

$
# On your local machine
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ssh -L 8888:localhost:8888 user@lab-computer
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# On the remote machine
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stanza jupyter start
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stanza jupyter status  # Copy the URL
>
>
# Open the URL in your local browser

The persistent server ensures your notebooks keep running even if your SSH connection drops.

Tips and Best Practices

Background execution: The server runs as a detached process, so you can close your terminal without stopping Jupyter. Use stanza jupyter status to rediscover the URL after reconnecting.

Monitor long experiments: Use stanza jupyter logs to check progress on multi-hour measurements without opening the browser or disrupting execution.

Clean shutdown: Always use stanza jupyter stop to gracefully shutdown the server. This ensures proper cleanup of runtime files and state.

Port conflicts: If port 8888 is already in use, specify a different port with --port 8889 when starting the server.

Next Steps

• Data Logging - Configure automatic logging for routine outputs
• Sessions - Organize experiments with session management
• Live Plotting - Visualize data in real-time as measurements run
• Jupyter Integration - Run and monitor Jupyter notebook servers