Fundamentals of Dask Transcripts
Chapter: Dask Delayed
Lecture: Introducing Delayed
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Welcome back after working with Dask Arrays, It's now time to jump into, Dask Delayed. You may remember Dask Delayed from the first course to recap The
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Dask Delayed API is a low level API that a lot of other distributed Dask stuff call all the time, for example,
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Dask Array and Dark DataFrame called Dask Delayed. And as we've seen Dask Array and Dask DataFrame can't be used everywhere.
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And the places they can't be 'Dask Delayed' can come to the rescue because it allows
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us to write custom parallel computations using Dask. On top of that.
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You can parallelize existing Python code using Dask Delayed and that's what we're about to do. A few words about what we're going to cover.
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First, we're gonna recap the Delayed API Then we'll parallelize some Python code with the Delayed API.
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Then we'll discuss some best practices for using Dask Delayed and then we'll wrap up with
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some references for those of you who are pretty eager to use Dask Delayed a lot more.