Distribution and Computation of dask.delayed object

Question

Do dask.delayed objects get distributed by dask on a cluster?

Also, is the execution of its task graph also distributed on a cluster?

Answer 1

The short answer is yes.

Users interact by connecting a local Python session to the scheduler and submitting work, either by individual calls to the simple interface client.submit(function, *args, **kwargs) or by using the large data collections and parallel algorithms of the parent dask library. The collections in the dask library like dask.array and dask.dataframe provide easy access to sophisticated algorithms and familiar APIs like NumPy and Pandas, while the simple client.submit interface provides users with custom control when they want to break out of canned “big data” abstractions and submit fully custom workloads.

Dask delayed objects are included in the "parallel algorithms of the parent dask library".

See the documentation for more info. http://distributed.dask.org/en/latest/