Introduction to Jupyter Notebooks Part 2

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Ways to run Jupyter

• Local setup
  • If Python is installed, use pip:
    pip install notebook          # classic Jupyter Notebook server
    pip install jupyterlab        # modern JupyterLab interface
    
  • Or install the Anaconda distribution, which bundles Python, Jupyter, common data science libraries, and the conda package manager: https://www.anaconda.com/products/distribution
• Containerized
  • Use Docker or another container runtime to pull ready-made Jupyter images. Containers isolate the notebook server and dependencies from the host OS while providing the same browser-based UI.
• Inside an IDE
  • Some IDEs (for example Visual Studio Code) open .ipynb files natively and provide notebook-like experiences directly inside the editor. IDE support varies; for many data-science tasks the full Jupyter UI is preferred.
• Hosted cloud service
  • Managed services run Jupyter servers in the cloud and give you a remote URL to access via your browser. AWS SageMaker is a common option that integrates Jupyter/JupyterLab with AWS services and storage.

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Jupyter in AWS SageMaker

• Classic Notebook Instances — managed EC2 instances preconfigured to run a Jupyter server.
• JupyterLab support — a multi-tabbed interface with extension support.
• SageMaker Studio — a full ML-focused IDE built on JupyterLab that integrates many additional tools and workflows for data scientists and MLOps engineers.

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Hosting resources and instance sizing

# Example 1: Simple arithmetic in a notebook cell
a = 10
b = 5
print(a + b)
# Output:
# 15

# Example 2: Iterate over a list of prices and print each
prices = [50000, 60000, 65000, 44000, 127000]
for price in prices:
    print(f'Current price is {price}')
# Output:
# Current price is 50000
# Current price is 60000
# Current price is 65000
# Current price is 44000
# Current price is 127000

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Visualizations and documentation

• Inline visualizations and instant feedback from code execution.
• Integration with many cloud environments (e.g., Google Colab, AWS SageMaker, Databricks).
• Self-documenting workflows: combine code cells with markdown for clear explanations, hypotheses, and reproducible steps.

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Summary

• Ways to run Jupyter: locally (pip or Anaconda), in containers, inside IDEs, or as hosted cloud services (e.g., AWS SageMaker).
• The browser is always the user interface for Jupyter; the server executes code and stores outputs.
• Notebook cells can be code or markdown; code cell stdout and visual outputs are captured inline in the .ipynb file.
• JupyterLab is a modern, tabbed interface with extension support; SageMaker Studio builds on JupyterLab to provide a full ML IDE.
• SageMaker supports legacy Notebook Instances and the newer Studio — prefer Studio for new projects.

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Links and references

• JupyterLab: https://jupyter.org/
• Anaconda distribution: https://www.anaconda.com/products/distribution
• Docker: https://www.docker.com/
• AWS SageMaker overview and resources: https://learn.kodekloud.com/user/courses/aws-sagemaker
• Matplotlib: https://matplotlib.org/
• Seaborn: https://seaborn.pydata.org/