distributed 2021.10.0

BackRef

This class contains common functionality for Dask Cluster manager classes.

To implement this class, you must provide

  1. A scheduler_comm attribute, which is a connection to the scheduler following the distributed.core.rpc API.

  2. Implement scale , which takes an integer and scales the cluster to that many workers, or else set _supports_scaling to False

For that, you should get the following:

  1. A standard __repr__

  2. A live IPython widget

  3. Adaptive scaling

  4. Integration with dask-labextension

  5. A scheduler_info attribute which contains an up-to-date copy of Scheduler.identity() , which is used for much of the above

  6. Methods to gather logs

Superclass for cluster objects

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

distributed.deploy.adaptive.Adaptive

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


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