Jupyter Lab

version: '3.8'

services:
  jupyter:
    image: jupyter/scipy-notebook:latest
    container_name: dxflow-jupyter

    # Web interface port
    ports:
      - "8888:8888"

    # Volumes for persistent data
    volumes:
      - ./notebooks:/home/jovyan/work
      - ./data:/home/jovyan/data

    # Environment variables
    environment:
      - JUPYTER_ENABLE_LAB=yes
      - JUPYTER_TOKEN=your-secret-token
      - GRANT_SUDO=yes

    # Resource limits
    deploy:
      resources:
        limits:
          cpus: '8'
          memory: 16G

    # User permissions
    user: root
    command: start-notebook.sh --NotebookApp.token='your-secret-token'

# Create directories
mkdir -p notebooks data

# Deploy Jupyter Lab
dxflow compose create --identity jupyter jupyter.yml
dxflow compose start jupyter

# Access Jupyter Lab
# Open browser: http://localhost:8888
# Token: your-secret-token

# Example Python notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('/home/jovyan/data/dataset.csv')

# Analyze
data.describe()

# Visualize
plt.figure(figsize=(10, 6))
plt.plot(data['x'], data['y'])
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Data Visualization')
plt.show()

# Upload data files
dxflow fs upload /local/dataset.csv data/

# Download notebooks
dxflow fs download notebooks/ /local/notebooks/

# Add GPU support for ML/DL
services:
  jupyter:
    image: jupyter/tensorflow-notebook:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

# Install extensions inside container
dxflow compose execute jupyter -- \
  pip install jupyterlab-git jupyterlab-lsp

# Code formatter
pip install jupyterlab_code_formatter black

# Table of contents
pip install jupyterlab-toc