The "Introduction to Dask" workshop offers a comprehensive guide to utilising Dask, a parallel computing library for Python designed to scale data analysis from single machines to clusters. Participants will explore Dask's core components, including task graphs, arrays, dataframes, delayed, and futures. The workshop also covers distributed computing with Dask and its integration with machine learning through Dask-ML. A Python virtual environment is provided for hands-on exercises. 

If you have any questions regarding this training, please contact training.nci@anu.edu.au.

Parallel Python is also available online on NCI Teachable website.

Prerequisites

  1. Experience with Python.

  2. Experience with bash or similar Unix shells.

  3. Having a valid NCI account and vp91 membership (instructions will be sent out before the event)

  4. The training session is driven on the NCI ARE service. You can find relevant documentations here: ARE User Guide.

Learning Outcomes

  • Describe what Dask is and when to use it for large data or parallel tasks.

  • Work with Dask Arrays and DataFrames to handle datasets that don’t fit in memory.

  • Use dask.delayed to turn regular Python code into parallel tasks.

  • Run Dask on a laptop or a cluster using the Dask Distributed scheduler.

  • Monitor their computations using the Dask Dashboard.

  • Combine Dask with tools they already know, like NumPy and Pandas.

Topics Covered

Foundation Topics

  • Task Graphs

  • Dask Arrays

  • Dask DataFrame

  • Dask Delayed

  • Dask Futures

  • Distributed Dask

Machine Learning Topics

  • Dask ML

  • Hyper Parameter Search

  • Parallel Prediction

  • Incremental Learning

  • Distributed Learning


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