Introduction to Dask

This repository provides a comprehensive guide to understanding and utilizing Dask, a parallel computing library for Python that is designed to scale data analysis and computation workflows from a single machine to a cluster.

Note

This project is under active development.

Contents

Topic Covered

Topics	Duration
Dask	1.5 Hours
Dask Distributed	1.5 Hours
Dask-ML	3 Hours

• Prerequisite
• Learning Outcomes
• Modules
• Python Virtual Environment
• Dask
  • Task Graphs
  • Dask Arrays
    • Relationship to NumPy Arrays
    • When to Use Dask Arrays
    • Comparisson between Numpy and Dask Array
    • Choosing Chunk Sizes
      • How does compute() work?
    • How does visualize() work?
  • Dask DataFrame
    • Dask DataFrame vs. Pandas DataFrame
    • When to Use Dask DataFrame
  • Dask Delayed
    • Key Concepts of Dask Delayed
    • How to Use Dask Delayed?
    • Key Advantages
  • Dask Futures
    • Key Concepts of Dask Futures
    • How to Use Dask Futures
    • Key Methods with Dask Futures
• Distributed Dask
  • Dask Distributed
    • Local cluster
    • PBS cluster
• Dask-ML
  • Dask ML
    • Dimensions of Scale
      • Challenge 1: Scaling Model Size
      • Challenge 2: Scaling Data Size
    • Dask and Scikit-Learn
  • Hyper Parameter Search
    • Implicit Usage in Dask-ML Operations
  • Parallel Prediction
  • Incremental Learning
  • Distributed Learning
• Reference
• Contributers