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JupyterLab Cheatsheet¶

• Installation

Platform	Command
Ubuntu/Debian (pip)	INLINE_CODE_10 HTML_TAG_141 INLINE_CODE_11
Ubuntu/Debian (conda)	INLINE_CODE_12
macOS (Homebrew)	INLINE_CODE_13 HTML_TAG_142 INLINE_CODE_14
macOS (conda)	INLINE_CODE_15 HTML_TAG_143 INLINE_CODE_16
Windows (pip)	INLINE_CODE_17
Windows (Anaconda)	Install Anaconda, then: INLINE_CODE_18
Docker	INLINE_CODE_19 HTML_TAG_144 INLINE_CODE_20
Upgrade	INLINE_CODE_21
Verify Installation	INLINE_CODE_22
_
oder Grundlegende Befehle

Command	Description
INLINE_CODE_23	Start JupyterLab server (opens browser automatically)
INLINE_CODE_24	Start JupyterLab on specific port
INLINE_CODE_25	Start without opening browser
INLINE_CODE_26	Start with specific working directory
INLINE_CODE_27	List all running JupyterLab servers
INLINE_CODE_28	Stop JupyterLab server on port 8888
INLINE_CODE_29	Display JupyterLab version
INLINE_CODE_30	Show all Jupyter paths (config, data, runtime)
INLINE_CODE_31	Generate default configuration file
INLINE_CODE_32	Set password for JupyterLab server
INLINE_CODE_33	Clean up JupyterLab installation
INLINE_CODE_34	List all available kernels
INLINE_CODE_35	List all installed extensions
INLINE_CODE_36	Trust a notebook (allow JavaScript)
INLINE_CODE_37	Display help and all available options
_
Kernel Management

Command	Description
INLINE_CODE_38	List all installed kernels
INLINE_CODE_39	Install Python kernel from environment
INLINE_CODE_40	Install kernel with custom display name
INLINE_CODE_41	Remove a kernel
INLINE_CODE_42	Install R kernel
INLINE_CODE_43	Install Julia kernel
INLINE_CODE_44	Install kernel support in conda environment
INLINE_CODE_45	Remove kernel specification
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Umrechnung von Notebooks (nbconvert)

Command	Description
INLINE_CODE_46	Convert notebook to HTML
INLINE_CODE_47	Convert notebook to PDF
INLINE_CODE_48	Convert notebook to Python script
INLINE_CODE_49	Convert notebook to Markdown
INLINE_CODE_50	Convert notebook to reveal.js slides
INLINE_CODE_51	Execute notebook and save output
INLINE_CODE_52	Execute and convert to HTML
INLINE_CODE_53	Clear all cell outputs
INLINE_CODE_54	Batch convert all notebooks in directory
INLINE_CODE_55	Execute notebook and export to PDF
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Erweiterungsmanagement

Command	Description
INLINE_CODE_56	List all installed extensions
INLINE_CODE_57	Install table of contents extension
INLINE_CODE_58	Uninstall an extension
INLINE_CODE_59	Update specific extension
INLINE_CODE_60	Disable extension without uninstalling
INLINE_CODE_61	Enable previously disabled extension
INLINE_CODE_62	Rebuild JupyterLab (required after extension changes)
INLINE_CODE_63	Check for extension updates
INLINE_CODE_64	Link extension for development
_
Erweiterte Serverkonfiguration

Command	Description
INLINE_CODE_65	Allow connections from any IP address
INLINE_CODE_66	Start server for remote access
INLINE_CODE_67	Set custom authentication token
INLINE_CODE_68	Allow requests from any origin (CORS)
INLINE_CODE_69	Enable remote access explicitly
INLINE_CODE_70	Increase data rate limit for large outputs
INLINE_CODE_71	Increase max upload size (500MB)
INLINE_CODE_72	Start in debug mode with verbose logging
INLINE_CODE_73	Start in development mode with auto-rebuild
INLINE_CODE_74	Display current configuration
INLINE_CODE_75	Set custom base URL (for reverse proxy)
INLINE_CODE_76	Allow running as root user (not recommended)

Arbeitsraummanagement

Command	Description
INLINE_CODE_77	Export current workspace layout
INLINE_CODE_78	Import workspace layout
INLINE_CODE_79	List all saved workspaces
INLINE_CODE_80	Delete a workspace

Konfiguration

Hauptkonfiguration Datei¶

Lage: __INLINE_CODE_81_(Linux/macOS) oder %USERPROFILE%\.jupyter\jupyter_lab_config.py (Windows)

Standardkonfiguration generieren:

jupyter lab --generate-config
```_

### Gemeinsame Konfigurationsoptionen

```python
# ~/.jupyter/jupyter_lab_config.py

c = get_config()

# Server settings
c.ServerApp.ip = '0.0.0.0'                    # Listen on all interfaces
c.ServerApp.port = 8888                       # Default port
c.ServerApp.open_browser = False              # Don't open browser automatically
c.ServerApp.token = 'your-secret-token'       # Authentication token
c.ServerApp.allow_remote_access = True        # Enable remote access
c.ServerApp.allow_origin = '*'                # Allow CORS from any origin
c.ServerApp.allow_root = False                # Prevent running as root

# Directory settings
c.ServerApp.root_dir = '/path/to/notebooks'   # Default working directory
c.ServerApp.preferred_dir = '/path/to/start'  # Preferred starting directory

# Resource limits
c.ServerApp.iopub_data_rate_limit = 10000000  # Data rate limit (bytes/sec)
c.ServerApp.iopub_msg_rate_limit = 1000       # Message rate limit
c.ServerApp.rate_limit_window = 3.0           # Rate limit window (seconds)

# Kernel management
c.MappingKernelManager.default_kernel_name = 'python3'
c.MappingKernelManager.cull_idle_timeout = 3600     # Kill idle kernels after 1 hour
c.MappingKernelManager.cull_interval = 300          # Check for idle kernels every 5 min
c.MappingKernelManager.cull_connected = False       # Don't cull connected kernels

# File management
c.ContentsManager.hide_globs = [
    '__pycache__', '*.pyc', '*.pyo', '.DS_Store', '*.so', '*.dylib'
]
c.FileContentsManager.delete_to_trash = True        # Use trash instead of permanent delete

# Security
c.ServerApp.disable_check_xsrf = False              # Enable XSRF protection
c.ServerApp.tornado_settings = {
    'headers': {
        'Content-Security-Policy': "frame-ancestors 'self'"
    }
}

# Logging
c.ServerApp.log_level = 'INFO'                      # Logging level

Benutzereinstellungen Verzeichnis¶

Lage: __INLINE_CODE_83_(Linux/macOS) oder %APPDATA%\jupyter\lab\user-settings\ (Windows)

Beispielthemeneinstellungen (@jupyterlab/apputils-extension/themes.jupyterlab-settings):

{
    "theme": "JupyterLab Dark",
    "theme-scrollbars": true
}
```_

### Passwort-Schutz

```bash
# Set password interactively
jupyter lab password

# Or generate hash programmatically
python -c "from jupyter_server.auth import passwd; print(passwd('your-password'))"

Dann fügen Sie zu config: python c.ServerApp.password = 'sha1:...' # Hashed password_

Häufige Anwendungsfälle

Use Case 1: Remote Server Setup¶

```bash

Generate config file¶

jupyter lab --generate-config

Set password¶

jupyter lab password

Edit config file (~/.jupyter/jupyter_lab_config.py)¶

Set: c.ServerApp.ip = '0.0.0.0'¶

c.ServerApp.open_browser = False¶

c.ServerApp.allow_remote_access = True¶

Start server¶

jupyter lab --no-browser --port=8888

Access from remote: http://your-server-ip:8888 ¶

```_

Use Case 2: Erstellen einer benutzerdefinierten Kernel-Umgebung¶

```bash

Create conda environment¶

conda create -n myproject python=3.9 pandas numpy matplotlib

Activate environment¶

conda activate myproject

Install ipykernel¶

conda install ipykernel

Register kernel with JupyterLab¶

python -m ipykernel install --user --name myproject --display-name "Python (myproject)"

Start JupyterLab and select the new kernel¶

jupyter lab ```_

Use Case 3: Batch Notebook Ausführung¶

```bash

Execute single notebook¶

jupyter nbconvert --to notebook --execute analysis.ipynb --output results.ipynb

Execute all notebooks in directory¶

for notebook in *.ipynb; do jupyter nbconvert --to notebook --execute "\(notebook" --output "executed_\)" done

Execute with timeout and error handling¶

jupyter nbconvert --to notebook --execute notebook.ipynb \ --ExecutePreprocessor.timeout=600 \ --ExecutePreprocessor.allow_errors=True ```_

Use Case 4: Docker Bereitstellung mit persistentem Speicher¶

```bash

Create directory for notebooks¶

mkdir -p ~/jupyter-notebooks

Run JupyterLab in Docker with volume mount¶

docker run -d \ -p 8888:8888 \ -v ~/jupyter-notebooks:/home/jovyan/work \ -e JUPYTER_ENABLE_LAB=yes \ --name jupyter \ jupyter/datascience-notebook

Get access token¶

docker logs jupyter 2>&1 | grep "token="

Stop container¶

docker stop jupyter

Restart container¶

docker start jupyter ```_

Use Case 5: Converting Notebooks to Presentation¶

```bash

Convert to reveal.js slides¶

jupyter nbconvert --to slides presentation.ipynb

Serve slides with built-in server¶

jupyter nbconvert --to slides presentation.ipynb --post serve

Convert with custom template¶

jupyter nbconvert --to slides --template lab presentation.ipynb

Export as standalone HTML¶

jupyter nbconvert --to slides presentation.ipynb --output-dir=./slides/ ```_

(Befehlsmodus)

Shortcut	Action
INLINE_CODE_86	Enter edit mode
INLINE_CODE_87	Run cell and select below
INLINE_CODE_88	Run selected cells
INLINE_CODE_89	Run cell and insert below
INLINE_CODE_90	Insert cell above
INLINE_CODE_91	Insert cell below
INLINE_CODE_92	Delete selected cell
INLINE_CODE_93	Undo cell deletion
INLINE_CODE_94	Change cell to Markdown
INLINE_CODE_95	Change cell to Code
INLINE_CODE_96	Merge selected cells
INLINE_CODE_97	Save notebook
INLINE_CODE_98	Toggle line numbers
INLINE_CODE_99	Restart kernel

oder Best Practices

**Use Virtual Environments*: Immer in isolierten Umgebungen (conda oder venv) arbeiten, um Abhängigkeiten zu verwalten und Konflikte zwischen Projekten zu vermeiden.
**Regular Kernel Restarts*: Starten Sie während langer Sitzungen regelmäßig Kernel aus, um den Speicher zu löschen und die Reproduzierbarkeit zu gewährleisten. Verwenden Sie "Restart Kernel und Run All Cells" um die Ausführung des Notebooks zu überprüfen.
**Version Control Integration*: Speichern Sie Notebooks in Git, verwenden Sie aber .gitignore, um Checkpoint-Dateien auszuschließen (.ipynb_checkpoints/). Betrachten Sie die Verwendung von __INLINE_CODE_102_, um die Ausgabe vor dem Commiting zu entfernen.
**Secure Remote Access*: Wenn Sie JupyterLab remote ausstellen, verwenden Sie immer starke Passwörter oder Token, aktivieren Sie HTTPS mit SSL-Zertifikaten und betrachten Sie die Verwendung von SSH Tunneling oder VPN für zusätzliche Sicherheit.
Resource Management: Konfigurieren Sie Kernel-Kulling für Leerkerne, um Speicher, vor allem auf freigegebenen Servern, freizugeben. Setzen Sie entsprechende Zeitoutwerte basierend auf einer typischen Workflow-Periode.
** Modular Code Organization*: Konzentrieren Sie sich auf bestimmte Analysen. Extrahieren Sie wiederverwendbare Funktionen in separate .py Module und importieren Sie sie, anstatt Code über Notebooks zu kopieren.
**Documentation Habits*: Verwenden Sie Markdown-Zellen liberal, um Ihren Gedankenprozess, Ihre Methodik und Ergebnisse zu dokumentieren. Fügen Sie die Abhängigkeiten der Zellausführung und die erwartete Laufzeit für lang laufende Zellen ein.
**Extension Discipline*: Installieren Sie nur notwendige Erweiterungen, um Blähungen und mögliche Konflikte zu vermeiden. Aktualisieren Sie regelmäßig Erweiterungen und bauen Sie JupyterLab nach Updates wieder auf.

Fehlerbehebung

Issue	Solution
"Port 8888 is already in use"	Use different port: INLINE_CODE_104 or stop existing server: INLINE_CODE_105
Cannot connect to kernel	Restart kernel from Kernel menu, or restart JupyterLab server. Check INLINE_CODE_106 for valid kernels.
"IOPub data rate exceeded"	Increase limit: INLINE_CODE_107 or add to config file.
Extensions not loading	Rebuild JupyterLab: INLINE_CODE_108. Check compatibility with INLINE_CODE_109.
Notebook won't save	Check file permissions, disk space, and that server has write access. Look for INLINE_CODE_110 directory issues.
ModuleNotFoundError in notebook	Ensure kernel is using correct environment. Reinstall ipykernel in target environment: INLINE_CODE_111
High memory usage	Enable kernel culling in config, restart unused kernels, or use INLINE_CODE_112 magic command to clear variables.
Slow notebook loading	Clear output of large notebooks: INLINE_CODE_113. Reduce cell output size.
Token/password not working	Regenerate token: INLINE_CODE_114. Check INLINE_CODE_115 for conflicts.
"403 Forbidden" errors	Disable XSRF check temporarily: INLINE_CODE_116 (not recommended for production).
Extension installation fails	Update Node.js: INLINE_CODE_117 or use system package manager. Clear npm cache: INLINE_CODE_118.
Kernel dies immediately	Check kernel logs: INLINE_CODE_119 to find log location. May indicate missing dependencies or memory issues.

Nützliche Magic Commands (in Notebooks)

Magic Command	Description
INLINE_CODE_120	List all available magic commands
INLINE_CODE_121	Time execution of single statement
INLINE_CODE_122	Time execution of entire cell
INLINE_CODE_123	Time repeated execution for average
INLINE_CODE_124	Display plots inline in notebook
INLINE_CODE_125	Load external Python file into cell
INLINE_CODE_126	Execute external Python script
INLINE_CODE_127	Print current working directory
INLINE_CODE_128	Change working directory
INLINE_CODE_129	List environment variables
INLINE_CODE_130	Install package in current kernel
INLINE_CODE_131	Install package via conda
INLINE_CODE_132	Execute cell as bash script
INLINE_CODE_133	Activate interactive debugger
_	`%reset`

JupyterLab Cheatsheet¶

Hauptkonfiguration Datei¶

Benutzereinstellungen Verzeichnis¶

Use Case 1: Remote Server Setup¶

Generate config file¶

Set password¶

Edit config file (~/.jupyter/jupyter_lab_config.py)¶

Set: c.ServerApp.ip = '0.0.0.0'¶

c.ServerApp.open_browser = False¶

c.ServerApp.allow_remote_access = True¶

Start server¶

Access from remote: http://your-server-ip:8888¶

Use Case 2: Erstellen einer benutzerdefinierten Kernel-Umgebung¶

Create conda environment¶

Activate environment¶

Install ipykernel¶

Register kernel with JupyterLab¶

Start JupyterLab and select the new kernel¶

Use Case 3: Batch Notebook Ausführung¶

Execute single notebook¶

Execute all notebooks in directory¶

Execute with timeout and error handling¶

Use Case 4: Docker Bereitstellung mit persistentem Speicher¶

Create directory for notebooks¶

Run JupyterLab in Docker with volume mount¶

Get access token¶

Stop container¶

Restart container¶

Use Case 5: Converting Notebooks to Presentation¶

Convert to reveal.js slides¶

Serve slides with built-in server¶

Convert with custom template¶

Export as standalone HTML¶

Access from remote: http://your-server-ip:8888 ¶