SimplePipelineExecutor¶
- class lsst.ctrl.mpexec.SimplePipelineExecutor(quantum_graph: QuantumGraph, butler: Butler)¶
Bases:
object
A simple, high-level executor for pipelines.
- Parameters:
- quantum_graph
QuantumGraph
Graph to be executed.
- butler
Butler
Object that manages all I/O. Must be initialized with
collections
andrun
properties that correspond to the input and output collections, which must be consistent with those used to createquantum_graph
.
- quantum_graph
Notes
Most callers should use one of the
classmethod
factory functions (from_pipeline_filename
,from_task_class
,from_pipeline
) instead of invoking the constructor directly; these guarantee that theButler
andQuantumGraph
are created consistently.This class is intended primarily to support unit testing and small-scale integration testing of
PipelineTask
classes. It deliberately lacks many features present in the command-line-onlypipetask
tool in order to keep the implementation simple. Python callers that need more sophistication should call lower-level tools likeGraphBuilder
,PreExecInit
, andSingleQuantumExecutor
directly.Methods Summary
as_generator
([register_dataset_types])Yield quanta in the
QuantumGraph
in topological order.from_pipeline
(pipeline, *[, where])Create an executor by building a QuantumGraph from an in-memory pipeline.
from_pipeline_filename
(pipeline_filename, *)Create an executor by building a QuantumGraph from an on-disk pipeline YAML file.
from_task_class
(task_class[, config, label, ...])Create an executor by building a QuantumGraph from a pipeline containing a single task.
prep_butler
(root, inputs, output[, output_run])Helper method for creating
Butler
instances with collections appropriate for processing.run
([register_dataset_types])Run all the quanta in the
QuantumGraph
in topological order.Methods Documentation
- as_generator(register_dataset_types: bool = False) Iterator[Quantum] ¶
Yield quanta in the
QuantumGraph
in topological order.These quanta will be run as the returned generator is iterated over. Use this method to run the quanta one at a time. Use
run
to run all quanta in the graph.- Parameters:
- Returns:
- quanta
Iterator
[Quantum
] Executed quanta. At present, these will contain only unresolved
DatasetRef
instances for output datasets, reflecting the state of the quantum just before it was run (but after any adjustments for predicted but now missing inputs). This may change in the future to include resolved outputDatasetRef
objects.
- quanta
Notes
Global initialization steps (see
PreExecInit
) are performed immediately when this method is called, but individual quanta are not actually executed until the returned iterator is iterated over.A topological ordering is not in general unique, but no other guarantees are made about the order in which quanta are processed.
- classmethod from_pipeline(pipeline: Pipeline | Iterable[TaskDef], *, where: str = '', butler: Butler, **kwargs: Any) SimplePipelineExecutor ¶
Create an executor by building a QuantumGraph from an in-memory pipeline.
- Parameters:
- pipeline
Pipeline
orIterable
[TaskDef
] A Python object describing the tasks to run, along with their labels and configuration.
- where
str
, optional Data ID query expression that constraints the quanta generated.
- butler
Butler
Butler that manages all I/O.
prep_butler
can be used to create one.
- pipeline
- Returns:
- executor
SimplePipelineExecutor
An executor instance containing the constructed
QuantumGraph
andButler
, ready forrun
to be called.
- executor
- classmethod from_pipeline_filename(pipeline_filename: str, *, where: str = '', butler: Butler) SimplePipelineExecutor ¶
Create an executor by building a QuantumGraph from an on-disk pipeline YAML file.
- Parameters:
- pipeline_filename
str
Name of the YAML file to load the pipeline definition from.
- where
str
, optional Data ID query expression that constraints the quanta generated.
- butler
Butler
Butler that manages all I/O.
prep_butler
can be used to create one.
- pipeline_filename
- Returns:
- executor
SimplePipelineExecutor
An executor instance containing the constructed
QuantumGraph
andButler
, ready forrun
to be called.
- executor
- classmethod from_task_class(task_class: Type[PipelineTask], config: Config | None = None, label: str | None = None, *, where: str = '', butler: Butler) SimplePipelineExecutor ¶
Create an executor by building a QuantumGraph from a pipeline containing a single task.
- Parameters:
- task_class
type
A concrete
PipelineTask
subclass.- config
Config
, optional Configuration for the task. If not provided, task-level defaults will be used (no per-instrument overrides).
- label
str
, optional Label for the task in its pipeline; defaults to
task_class._DefaultName
.- where
str
, optional Data ID query expression that constraints the quanta generated.
- butler
Butler
Butler that manages all I/O.
prep_butler
can be used to create one.
- task_class
- Returns:
- executor
SimplePipelineExecutor
An executor instance containing the constructed
QuantumGraph
andButler
, ready forrun
to be called.
- executor
- classmethod prep_butler(root: str, inputs: Iterable[str], output: str, output_run: str | None = None) Butler ¶
Helper method for creating
Butler
instances with collections appropriate for processing.- Parameters:
- root
str
Root of the butler data repository; must already exist, with all necessary input data.
- inputs
Iterable
[str
] Collections to search for all input datasets, in search order.
- output
str
Name of a new output
CHAINED
collection to create that will combine both inputs and outputs.- output_run
str
, optional Name of the output
RUN
that will directly hold all output datasets. If not provided, a name will be created fromoutput
and a timestamp.
- root
- Returns:
- butler
Butler
Butler client instance compatible with all
classmethod
factories. Always writeable.
- butler
- run(register_dataset_types: bool = False) List[Quantum] ¶
Run all the quanta in the
QuantumGraph
in topological order.Use this method to run all quanta in the graph. Use
as_generator
to get a generator to run the quanta one at a time.- Parameters:
- Returns:
- quanta
List
[Quantum
] Executed quanta. At present, these will contain only unresolved
DatasetRef
instances for output datasets, reflecting the state of the quantum just before it was run (but after any adjustments for predicted but now missing inputs). This may change in the future to include resolved outputDatasetRef
objects.
- quanta
Notes
A topological ordering is not in general unique, but no other guarantees are made about the order in which quanta are processed.