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Notebook Tutorials

Learn how to set up and use interactive computing environments using Vantage Compute. These tutorials provide practical guidance for Jupyter notebooks, VS Code integration, and collaborative development workflows.

What You'll Learn​

These notebook tutorials cover:

  • Notebook Setup: Configuring Jupyter and VS Code environments
  • Interactive Computing: Developing and debugging code interactively
  • Data Analysis: Using notebooks for exploratory data analysis
  • Collaborative Workflows: Sharing and collaborating on notebook projects
  • Resource Management: Optimizing notebook performance and resource usage
  • Integration: Connecting notebooks with storage, data sources, and compute clusters

Prerequisites​

Before starting these tutorials, you should have:

  • Access to a Vantage Compute environment with notebook support
  • Basic familiarity with Python, R, or other supported languages
  • Understanding of data analysis concepts (helpful but not required)
  • Sample datasets for analysis and experimentation

Tutorial Series​

Getting Started with Notebooks​

Difficulty: Beginner
Duration: 30-45 minutes

Learn the basics of launching and using Jupyter notebooks, including environment setup and basic interactive computing workflows.

Advanced Notebook Workflows​

Difficulty: Intermediate
Duration: 1-2 hours

Explore advanced notebook features, custom environments, and integration with external data sources and compute resources.

Production Notebook Deployments​

Difficulty: Advanced
Duration: 2-3 hours

Implement enterprise-grade notebook solutions with shared environments, version control, and automated deployment pipelines.

Notebook Types Covered​

These tutorials address various notebook scenarios:

  • Jupyter Notebooks: Classic web-based interactive computing
  • JupyterLab: Next-generation notebook interface with advanced features
  • VS Code Notebooks: Integrated development environment with notebook support
  • Collaborative Notebooks: Multi-user environments for team projects
  • Specialized Environments: GPU-enabled notebooks for AI/ML workloads

Use Cases Explored​

Learn to use notebooks for:

  • Data Exploration: Interactive data analysis and visualization
  • Machine Learning: Model development, training, and evaluation
  • Scientific Computing: Research and computational experiments
  • Education: Teaching and learning computational concepts
  • Prototyping: Rapid development and testing of algorithms

Best Practices Covered​

Throughout these tutorials, you'll learn:

  • Environment Management: Creating reproducible computational environments
  • Version Control: Tracking changes and collaborating effectively
  • Performance Optimization: Efficient resource usage and memory management
  • Security: Implementing proper access controls and data protection
  • Documentation: Creating clear, reproducible analysis workflows

Integration Features​

Learn to integrate notebooks with:

  • Storage Systems: Accessing data from various storage backends
  • Compute Clusters: Running intensive computations on HPC resources
  • Databases: Connecting to data sources and warehouses
  • Visualization Tools: Creating interactive charts and dashboards
  • External APIs: Integrating with web services and cloud platforms

Collaborative Workflows​

Explore techniques for:

  • Team Collaboration: Sharing notebooks and environments
  • Code Sharing: Reusable notebook components and libraries
  • Result Sharing: Publishing and presenting analysis results
  • Review Processes: Collaborative review and validation workflows
  • Knowledge Management: Organizing and cataloging notebook projects

Getting Help​

If you encounter issues during these tutorials:

  • Check the troubleshooting sections within each tutorial
  • Refer to the Notebook Documentation for detailed information
  • Contact support for additional assistance

Next Steps​

After completing these tutorials, consider exploring: