compute(self, **kwargs)

This turns a lazy Dask collection into its in-memory equivalent. For example a Dask array turns into a NumPy array and a Dask dataframe turns into a Pandas dataframe. The entire dataset must fit into memory before calling this operation.

Parameters

scheduler : string, optional

Which scheduler to use like "threads", "synchronous" or "processes". If not provided, the default is to check the global settings first, and then fall back to the collection defaults.

optimize_graph : bool, optional

If True [default], the graph is optimized before computation. Otherwise the graph is run as is. This can be useful for debugging.

kwargs :

Extra keywords to forward to the scheduler function.

Compute this dask collection

See Also

dask.base.compute

Examples

See :

Back References

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

dask.array.routines.histogram dask.array.reductions.topk dask.array.random.RandomState dask.array.core.map_blocks dask.array.routines.histogram2d dask.array.blockwise.blockwise dask.array.core.from_func dask.array.tiledb_io.from_tiledb dask.array.core.from_delayed dask.array.routines.histogramdd dask.array.core.Array.map_overlap dask.array.reductions.argtopk dask.array.overlap.map_overlap dask.base.optimize

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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