QuantumGraphBuilder

class lsst.pipe.base.quantum_graph_builder.QuantumGraphBuilder(pipeline_graph: PipelineGraph, butler: Butler, *, input_collections: Sequence[str] | None = None, output_run: str | None = None, skip_existing_in: Sequence[str] = (), clobber: bool = False)

Bases: ABC

An abstract base class for building QuantumGraph objects from a pipeline.

Parameters:

pipeline_graphpipeline_graph.PipelineGraph

Pipeline to build a QuantumGraph from, as a graph. Will be resolved in-place with the given butler (any existing resolution is ignored).

butlerlsst.daf.butler.Butler

Client for the data repository. Should be read-only.

input_collectionsSequence [ str ], optional

Collections to search for overall-input datasets. If not provided, butler.collections is used (and must not be empty).

output_runstr, optional

Output RUN collection. If not provided, butler.run is used (and must not be None).

skip_existing_inSequence [ str ], optional

Collections to search for outputs that already exist for the purpose of skipping quanta that have already been run.

clobberbool, optional

Whether to raise if predicted outputs already exist in output_run (not including those quanta that would be skipped because they’ve already been run). This never actually clobbers outputs; it just informs the graph generation algorithm whether execution will run with clobbering enabled. This is ignored if output_run does not exist.

Notes

Constructing a QuantumGraphBuilder will run queries for existing datasets with empty data IDs (including but not limited to init inputs and outputs), in addition to resolving the given pipeline graph and testing for existence of the output run collection.

The build method splits the pipeline graph into independent subgraphs, then calls the abstract method process_subgraph on each, to allow concrete implementations to populate the rough graph structure (the QuantumGraphSkeleton class), including searching for existing datasets. The build method then:

• assembles lsst.daf.butler.Quantum instances from all data IDs in the skeleton;

• looks for existing outputs found in skip_existing_in to see if any quanta should be skipped;

• calls PipelineTaskConnections.adjustQuantum on all quanta, adjusting downstream quanta appropriately when preliminary predicted outputs are rejected (pruning nodes that will not have the inputs they need to run);

• attaches datastore records and registry dataset types to the graph.

In addition to implementing process_subgraph, derived classes are generally expected to add new construction keyword-only arguments to control the data IDs of the quantum graph, while forwarding all of the arguments defined in the base class to super.

Attributes Summary

universe

Definitions of all data dimensions.

Methods Summary

build([metadata, attach_datastore_records])	Build the quantum graph.
process_subgraph(subgraph)	Build the rough structure for an independent subset of the QuantumGraph and query for relevant existing datasets.

Attributes Documentation

universe

Definitions of all data dimensions.

Methods Documentation

final build(metadata: Mapping[str, Any] | None = None, attach_datastore_records: bool = True) → QuantumGraph

Build the quantum graph.

Parameters:

metadataMapping, optional

Flexible metadata to add to the quantum graph.

attach_datastore_recordsbool, optional

Whether to include datastore records in the graph. Required for lsst.daf.butler.QuantumBackedButler execution.

Returns:

quantum_graphQuantumGraph

DAG describing processing to be performed.

Notes

External code is expected to construct a QuantumGraphBuilder and then call this method exactly once. See class documentation for details on what it does.

abstract process_subgraph(subgraph: PipelineGraph) → QuantumGraphSkeleton

Build the rough structure for an independent subset of the QuantumGraph and query for relevant existing datasets.

Parameters:

subgraphpipeline_graph.PipelineGraph

Subset of the pipeline graph that should be processed by this call. This is always resolved and topologically sorted. It should not be modified.

Returns:

skeletonquantum_graph_skeleton.QuantumGraphSkeleton

Class representing an initial quantum graph. See quantum_graph_skeleton.QuantumGraphSkeleton docs for details. After this is returned, the object may be modified in-place in unspecified ways.

Notes

The quantum_graph_skeleton.QuantumGraphSkeleton should associate DatasetRef objects with nodes for existing datasets. In particular:

• quantum_graph_skeleton.QuantumGraphSkeleton.set_dataset_ref must be used to associate existing datasets with all overall-input dataset nodes in the skeleton by querying input_collections. This includes all standard input nodes and any prerequisite nodes added by the method (prerequisite nodes may also be left out entirely, as the base class can add them later, albeit possibly less efficiently).

• quantum_graph_skeleton.QuantumGraphSkeleton.set_output_for_skip must be used to associate existing datasets with output dataset nodes by querying skip_existing_in.

• quantum_graph_skeleton.QuantumGraphSkeleton.add_output_in_the_way must be used to associated existing outputs with output dataset nodes by querying output_run if output_run_exists is True. Note that the presence of such datasets is not automatically an error, even if clobber is False, as these may be quanta that will be skipped.

DatasetRef objects for existing datasets with empty data IDs in all of the above categories may be found in the empty_dimensions_datasets attribute, as these are queried for prior to this call by the base class, but associating them with graph nodes is still this method’s responsibility.

Dataset types should never be components and should always use the “common” storage class definition in pipeline_graph.DatasetTypeNode (which is the data repository definition when the dataset type is registered).