Parallelizing Computation with Dask Delayed and Futures

PT26964
Summary
This class module focuses on using the Dask scheduler to empower custom parallel computation. Dask Delayed and Futures represent lightweight mechanisms for building and running custom task graphs, while staying within traditional Python coding patterns. This combination — regular Python code with a powerful distributed scheduler — enables all kinds of industry or discipline-specific workloads to be parallelized for fast, large-scale computation.
Prerequisites
Students should have experience in Python at a basic to intermediate level.
Duration
1 Day/Lecture & Lab
Audience
This course is intended for engineers or data scientists who typically work with large volumes of workloads for computation.
Topics
  • Introduction
  • Building and Running Graphs with Delayed
  • Running and Managing Work with Futures
  • Review and Q & A

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