Coverage for python/lsst/ctrl/mpexec/cmdLineFwk.py: 14%
361 statements
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1# This file is part of ctrl_mpexec.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
22"""Module defining CmdLineFwk class and related methods.
23"""
25from __future__ import annotations
27__all__ = ["CmdLineFwk"]
29import atexit
30import copy
31import datetime
32import getpass
33import logging
34import shutil
35from collections.abc import Iterable, Mapping, Sequence
36from types import SimpleNamespace
37from typing import TYPE_CHECKING
39from astropy.table import Table
40from lsst.daf.butler import (
41 Butler,
42 CollectionType,
43 DatasetId,
44 DatasetRef,
45 DatastoreCacheManager,
46 QuantumBackedButler,
47)
48from lsst.daf.butler.registry import MissingCollectionError, RegistryDefaults
49from lsst.daf.butler.registry.wildcards import CollectionWildcard
50from lsst.pipe.base import (
51 GraphBuilder,
52 Instrument,
53 Pipeline,
54 PipelineDatasetTypes,
55 QuantumGraph,
56 buildExecutionButler,
57)
58from lsst.utils import doImportType
59from lsst.utils.threads import disable_implicit_threading
61from . import util
62from .dotTools import graph2dot, pipeline2dot
63from .executionGraphFixup import ExecutionGraphFixup
64from .mpGraphExecutor import MPGraphExecutor
65from .preExecInit import PreExecInit, PreExecInitLimited
66from .singleQuantumExecutor import SingleQuantumExecutor
68if TYPE_CHECKING:
69 from lsst.daf.butler import (
70 Config,
71 DatasetType,
72 DatastoreRecordData,
73 DimensionUniverse,
74 LimitedButler,
75 Quantum,
76 Registry,
77 )
78 from lsst.pipe.base import TaskDef, TaskFactory
81# ----------------------------------
82# Local non-exported definitions --
83# ----------------------------------
85_LOG = logging.getLogger(__name__)
88class _OutputChainedCollectionInfo:
89 """A helper class for handling command-line arguments related to an output
90 `~lsst.daf.butler.CollectionType.CHAINED` collection.
92 Parameters
93 ----------
94 registry : `lsst.daf.butler.Registry`
95 Butler registry that collections will be added to and/or queried from.
96 name : `str`
97 Name of the collection given on the command line.
98 """
100 def __init__(self, registry: Registry, name: str):
101 self.name = name
102 try:
103 self.chain = tuple(registry.getCollectionChain(name))
104 self.exists = True
105 except MissingCollectionError:
106 self.chain = ()
107 self.exists = False
109 def __str__(self) -> str:
110 return self.name
112 name: str
113 """Name of the collection provided on the command line (`str`).
114 """
116 exists: bool
117 """Whether this collection already exists in the registry (`bool`).
118 """
120 chain: tuple[str, ...]
121 """The definition of the collection, if it already exists (`tuple`[`str`]).
123 Empty if the collection does not already exist.
124 """
127class _OutputRunCollectionInfo:
128 """A helper class for handling command-line arguments related to an output
129 `~lsst.daf.butler.CollectionType.RUN` collection.
131 Parameters
132 ----------
133 registry : `lsst.daf.butler.Registry`
134 Butler registry that collections will be added to and/or queried from.
135 name : `str`
136 Name of the collection given on the command line.
137 """
139 def __init__(self, registry: Registry, name: str):
140 self.name = name
141 try:
142 actualType = registry.getCollectionType(name)
143 if actualType is not CollectionType.RUN:
144 raise TypeError(f"Collection '{name}' exists but has type {actualType.name}, not RUN.")
145 self.exists = True
146 except MissingCollectionError:
147 self.exists = False
149 name: str
150 """Name of the collection provided on the command line (`str`).
151 """
153 exists: bool
154 """Whether this collection already exists in the registry (`bool`).
155 """
158class _ButlerFactory:
159 """A helper class for processing command-line arguments related to input
160 and output collections.
162 Parameters
163 ----------
164 registry : `lsst.daf.butler.Registry`
165 Butler registry that collections will be added to and/or queried from.
167 args : `types.SimpleNamespace`
168 Parsed command-line arguments. The following attributes are used,
169 either at construction or in later methods.
171 ``output``
172 The name of a `~lsst.daf.butler.CollectionType.CHAINED`
173 input/output collection.
175 ``output_run``
176 The name of a `~lsst.daf.butler.CollectionType.RUN` input/output
177 collection.
179 ``extend_run``
180 A boolean indicating whether ``output_run`` should already exist
181 and be extended.
183 ``replace_run``
184 A boolean indicating that (if `True`) ``output_run`` should already
185 exist but will be removed from the output chained collection and
186 replaced with a new one.
188 ``prune_replaced``
189 A boolean indicating whether to prune the replaced run (requires
190 ``replace_run``).
192 ``inputs``
193 Input collections of any type; see
194 :ref:`daf_butler_ordered_collection_searches` for details.
196 ``butler_config``
197 Path to a data repository root or configuration file.
199 writeable : `bool`
200 If `True`, a `~lsst.daf.butler.Butler` is being initialized in a
201 context where actual writes should happens, and hence no output run
202 is necessary.
204 Raises
205 ------
206 ValueError
207 Raised if ``writeable is True`` but there are no output collections.
208 """
210 def __init__(self, registry: Registry, args: SimpleNamespace, writeable: bool):
211 if args.output is not None:
212 self.output = _OutputChainedCollectionInfo(registry, args.output)
213 else:
214 self.output = None
215 if args.output_run is not None:
216 self.outputRun = _OutputRunCollectionInfo(registry, args.output_run)
217 elif self.output is not None:
218 if args.extend_run:
219 if not self.output.chain:
220 raise ValueError("Cannot use --extend-run option with non-existing or empty output chain")
221 runName = self.output.chain[0]
222 else:
223 runName = "{}/{}".format(self.output, Instrument.makeCollectionTimestamp())
224 self.outputRun = _OutputRunCollectionInfo(registry, runName)
225 elif not writeable:
226 # If we're not writing yet, ok to have no output run.
227 self.outputRun = None
228 else:
229 raise ValueError("Cannot write without at least one of (--output, --output-run).")
230 # Recursively flatten any input CHAINED collections. We do this up
231 # front so we can tell if the user passes the same inputs on subsequent
232 # calls, even though we also flatten when we define the output CHAINED
233 # collection.
234 self.inputs = tuple(registry.queryCollections(args.input, flattenChains=True)) if args.input else ()
236 def check(self, args: SimpleNamespace) -> None:
237 """Check command-line options for consistency with each other and the
238 data repository.
240 Parameters
241 ----------
242 args : `types.SimpleNamespace`
243 Parsed command-line arguments. See class documentation for the
244 construction parameter of the same name.
245 """
246 assert not (args.extend_run and args.replace_run), "In mutually-exclusive group in ArgumentParser."
247 if self.inputs and self.output is not None and self.output.exists:
248 # Passing the same inputs that were used to initialize the output
249 # collection is allowed; this means they must _end_ with the same
250 # collections, because we push new runs to the front of the chain.
251 for c1, c2 in zip(self.inputs[::-1], self.output.chain[::-1]):
252 if c1 != c2:
253 raise ValueError(
254 f"Output CHAINED collection {self.output.name!r} exists, but it ends with "
255 "a different sequence of input collections than those given: "
256 f"{c1!r} != {c2!r} in inputs={self.inputs} vs "
257 f"{self.output.name}={self.output.chain}."
258 )
259 if len(self.inputs) > len(self.output.chain):
260 nNew = len(self.inputs) - len(self.output.chain)
261 raise ValueError(
262 f"Cannot add new input collections {self.inputs[:nNew]} after "
263 "output collection is first created."
264 )
265 if args.extend_run:
266 if self.outputRun is None:
267 raise ValueError("Cannot --extend-run when no output collection is given.")
268 elif not self.outputRun.exists:
269 raise ValueError(
270 f"Cannot --extend-run; output collection '{self.outputRun.name}' does not exist."
271 )
272 if not args.extend_run and self.outputRun is not None and self.outputRun.exists:
273 raise ValueError(
274 f"Output run '{self.outputRun.name}' already exists, but --extend-run was not given."
275 )
276 if args.prune_replaced and not args.replace_run:
277 raise ValueError("--prune-replaced requires --replace-run.")
278 if args.replace_run and (self.output is None or not self.output.exists):
279 raise ValueError("--output must point to an existing CHAINED collection for --replace-run.")
281 @classmethod
282 def _makeReadParts(cls, args: SimpleNamespace) -> tuple[Butler, Sequence[str], _ButlerFactory]:
283 """Parse arguments to support implementations of `makeReadButler` and
284 `makeButlerAndCollections`.
286 Parameters
287 ----------
288 args : `types.SimpleNamespace`
289 Parsed command-line arguments. See class documentation for the
290 construction parameter of the same name.
292 Returns
293 -------
294 butler : `lsst.daf.butler.Butler`
295 A read-only butler constructed from the repo at
296 ``args.butler_config``, but with no default collections.
297 inputs : `Sequence` [ `str` ]
298 A collection search path constructed according to ``args``.
299 self : `_ButlerFactory`
300 A new `_ButlerFactory` instance representing the processed version
301 of ``args``.
302 """
303 butler = Butler(args.butler_config, writeable=False)
304 self = cls(butler.registry, args, writeable=False)
305 self.check(args)
306 if self.output and self.output.exists:
307 if args.replace_run:
308 replaced = self.output.chain[0]
309 inputs = list(self.output.chain[1:])
310 _LOG.debug(
311 "Simulating collection search in '%s' after removing '%s'.", self.output.name, replaced
312 )
313 else:
314 inputs = [self.output.name]
315 else:
316 inputs = list(self.inputs)
317 if args.extend_run:
318 assert self.outputRun is not None, "Output collection has to be specified."
319 inputs.insert(0, self.outputRun.name)
320 collSearch = CollectionWildcard.from_expression(inputs).require_ordered()
321 return butler, collSearch, self
323 @classmethod
324 def makeReadButler(cls, args: SimpleNamespace) -> Butler:
325 """Construct a read-only butler according to the given command-line
326 arguments.
328 Parameters
329 ----------
330 args : `types.SimpleNamespace`
331 Parsed command-line arguments. See class documentation for the
332 construction parameter of the same name.
334 Returns
335 -------
336 butler : `lsst.daf.butler.Butler`
337 A read-only butler initialized with the collections specified by
338 ``args``.
339 """
340 cls.defineDatastoreCache() # Ensure that this butler can use a shared cache.
341 butler, inputs, _ = cls._makeReadParts(args)
342 _LOG.debug("Preparing butler to read from %s.", inputs)
343 return Butler(butler=butler, collections=inputs)
345 @classmethod
346 def makeButlerAndCollections(cls, args: SimpleNamespace) -> tuple[Butler, Sequence[str], str | None]:
347 """Return a read-only registry, a collection search path, and the name
348 of the run to be used for future writes.
350 Parameters
351 ----------
352 args : `types.SimpleNamespace`
353 Parsed command-line arguments. See class documentation for the
354 construction parameter of the same name.
356 Returns
357 -------
358 butler : `lsst.daf.butler.Butler`
359 A read-only butler that collections will be added to and/or queried
360 from.
361 inputs : `Sequence` [ `str` ]
362 Collections to search for datasets.
363 run : `str` or `None`
364 Name of the output `~lsst.daf.butler.CollectionType.RUN` collection
365 if it already exists, or `None` if it does not.
366 """
367 butler, inputs, self = cls._makeReadParts(args)
368 run: str | None = None
369 if args.extend_run:
370 assert self.outputRun is not None, "Output collection has to be specified."
371 if self.outputRun is not None:
372 run = self.outputRun.name
373 _LOG.debug("Preparing registry to read from %s and expect future writes to '%s'.", inputs, run)
374 return butler, inputs, run
376 @staticmethod
377 def defineDatastoreCache() -> None:
378 """Define where datastore cache directories should be found.
380 Notes
381 -----
382 All the jobs should share a datastore cache if applicable. This
383 method asks for a shared fallback cache to be defined and then
384 configures an exit handler to clean it up.
385 """
386 defined, cache_dir = DatastoreCacheManager.set_fallback_cache_directory_if_unset()
387 if defined:
388 atexit.register(shutil.rmtree, cache_dir, ignore_errors=True)
389 _LOG.debug("Defining shared datastore cache directory to %s", cache_dir)
391 @classmethod
392 def makeWriteButler(cls, args: SimpleNamespace, taskDefs: Iterable[TaskDef] | None = None) -> Butler:
393 """Return a read-write butler initialized to write to and read from
394 the collections specified by the given command-line arguments.
396 Parameters
397 ----------
398 args : `types.SimpleNamespace`
399 Parsed command-line arguments. See class documentation for the
400 construction parameter of the same name.
401 taskDefs : iterable of `TaskDef`, optional
402 Definitions for tasks in a pipeline. This argument is only needed
403 if ``args.replace_run`` is `True` and ``args.prune_replaced`` is
404 "unstore".
406 Returns
407 -------
408 butler : `lsst.daf.butler.Butler`
409 A read-write butler initialized according to the given arguments.
410 """
411 cls.defineDatastoreCache() # Ensure that this butler can use a shared cache.
412 butler = Butler(args.butler_config, writeable=True)
413 self = cls(butler.registry, args, writeable=True)
414 self.check(args)
415 assert self.outputRun is not None, "Output collection has to be specified." # for mypy
416 if self.output is not None:
417 chainDefinition = list(self.output.chain if self.output.exists else self.inputs)
418 if args.replace_run:
419 replaced = chainDefinition.pop(0)
420 if args.prune_replaced == "unstore":
421 # Remove datasets from datastore
422 with butler.transaction():
423 refs: Iterable[DatasetRef] = butler.registry.queryDatasets(..., collections=replaced)
424 # we want to remove regular outputs but keep
425 # initOutputs, configs, and versions.
426 if taskDefs is not None:
427 initDatasetNames = set(PipelineDatasetTypes.initOutputNames(taskDefs))
428 refs = [ref for ref in refs if ref.datasetType.name not in initDatasetNames]
429 butler.pruneDatasets(refs, unstore=True, disassociate=False)
430 elif args.prune_replaced == "purge":
431 # Erase entire collection and all datasets, need to remove
432 # collection from its chain collection first.
433 with butler.transaction():
434 butler.registry.setCollectionChain(self.output.name, chainDefinition, flatten=True)
435 butler.removeRuns([replaced], unstore=True)
436 elif args.prune_replaced is not None:
437 raise NotImplementedError(f"Unsupported --prune-replaced option '{args.prune_replaced}'.")
438 if not self.output.exists:
439 butler.registry.registerCollection(self.output.name, CollectionType.CHAINED)
440 if not args.extend_run:
441 butler.registry.registerCollection(self.outputRun.name, CollectionType.RUN)
442 chainDefinition.insert(0, self.outputRun.name)
443 butler.registry.setCollectionChain(self.output.name, chainDefinition, flatten=True)
444 _LOG.debug(
445 "Preparing butler to write to '%s' and read from '%s'=%s",
446 self.outputRun.name,
447 self.output.name,
448 chainDefinition,
449 )
450 butler.registry.defaults = RegistryDefaults(run=self.outputRun.name, collections=self.output.name)
451 else:
452 inputs = (self.outputRun.name,) + self.inputs
453 _LOG.debug("Preparing butler to write to '%s' and read from %s.", self.outputRun.name, inputs)
454 butler.registry.defaults = RegistryDefaults(run=self.outputRun.name, collections=inputs)
455 return butler
457 output: _OutputChainedCollectionInfo | None
458 """Information about the output chained collection, if there is or will be
459 one (`_OutputChainedCollectionInfo` or `None`).
460 """
462 outputRun: _OutputRunCollectionInfo | None
463 """Information about the output run collection, if there is or will be
464 one (`_OutputRunCollectionInfo` or `None`).
465 """
467 inputs: tuple[str, ...]
468 """Input collections provided directly by the user (`tuple` [ `str` ]).
469 """
472class _QBBFactory:
473 """Class which is a callable for making QBB instances."""
475 def __init__(
476 self, butler_config: Config, dimensions: DimensionUniverse, dataset_types: Mapping[str, DatasetType]
477 ):
478 self.butler_config = butler_config
479 self.dimensions = dimensions
480 self.dataset_types = dataset_types
482 def __call__(self, quantum: Quantum) -> LimitedButler:
483 """Return freshly initialized `~lsst.daf.butler.QuantumBackedButler`.
485 Factory method to create QuantumBackedButler instances.
486 """
487 return QuantumBackedButler.initialize(
488 config=self.butler_config,
489 quantum=quantum,
490 dimensions=self.dimensions,
491 dataset_types=self.dataset_types,
492 )
495# ------------------------
496# Exported definitions --
497# ------------------------
500class CmdLineFwk:
501 """PipelineTask framework which executes tasks from command line.
503 In addition to executing tasks this activator provides additional methods
504 for task management like dumping configuration or execution chain.
505 """
507 MP_TIMEOUT = 3600 * 24 * 30 # Default timeout (sec) for multiprocessing
509 def __init__(self) -> None:
510 pass
512 def makePipeline(self, args: SimpleNamespace) -> Pipeline:
513 """Build a pipeline from command line arguments.
515 Parameters
516 ----------
517 args : `types.SimpleNamespace`
518 Parsed command line
520 Returns
521 -------
522 pipeline : `~lsst.pipe.base.Pipeline`
523 """
524 if args.pipeline:
525 pipeline = Pipeline.from_uri(args.pipeline)
526 else:
527 pipeline = Pipeline("anonymous")
529 # loop over all pipeline actions and apply them in order
530 for action in args.pipeline_actions:
531 if action.action == "add_instrument":
532 pipeline.addInstrument(action.value)
534 elif action.action == "new_task":
535 pipeline.addTask(action.value, action.label)
537 elif action.action == "delete_task":
538 pipeline.removeTask(action.label)
540 elif action.action == "config":
541 # action value string is "field=value", split it at '='
542 field, _, value = action.value.partition("=")
543 pipeline.addConfigOverride(action.label, field, value)
545 elif action.action == "configfile":
546 pipeline.addConfigFile(action.label, action.value)
548 else:
549 raise ValueError(f"Unexpected pipeline action: {action.action}")
551 if args.save_pipeline:
552 pipeline.write_to_uri(args.save_pipeline)
554 if args.pipeline_dot:
555 pipeline2dot(pipeline, args.pipeline_dot)
557 return pipeline
559 def makeGraph(self, pipeline: Pipeline, args: SimpleNamespace) -> QuantumGraph | None:
560 """Build a graph from command line arguments.
562 Parameters
563 ----------
564 pipeline : `~lsst.pipe.base.Pipeline`
565 Pipeline, can be empty or ``None`` if graph is read from a file.
566 args : `types.SimpleNamespace`
567 Parsed command line
569 Returns
570 -------
571 graph : `~lsst.pipe.base.QuantumGraph` or `None`
572 If resulting graph is empty then `None` is returned.
573 """
574 # make sure that --extend-run always enables --skip-existing
575 if args.extend_run:
576 args.skip_existing = True
578 butler, collections, run = _ButlerFactory.makeButlerAndCollections(args)
580 if args.skip_existing and run:
581 args.skip_existing_in += (run,)
583 if args.qgraph:
584 # click passes empty tuple as default value for qgraph_node_id
585 nodes = args.qgraph_node_id or None
586 qgraph = QuantumGraph.loadUri(args.qgraph, butler.dimensions, nodes=nodes, graphID=args.qgraph_id)
588 # pipeline can not be provided in this case
589 if pipeline:
590 raise ValueError("Pipeline must not be given when quantum graph is read from file.")
591 if args.show_qgraph_header:
592 print(QuantumGraph.readHeader(args.qgraph))
593 else:
594 task_defs = list(pipeline.toExpandedPipeline())
595 if args.mock:
596 from lsst.pipe.base.tests.mocks import mock_task_defs
598 task_defs = mock_task_defs(
599 task_defs,
600 unmocked_dataset_types=args.unmocked_dataset_types,
601 force_failures=args.mock_failure,
602 )
603 # make execution plan (a.k.a. DAG) for pipeline
604 graphBuilder = GraphBuilder(
605 butler.registry,
606 skipExistingIn=args.skip_existing_in,
607 clobberOutputs=args.clobber_outputs,
608 datastore=butler.datastore if args.qgraph_datastore_records else None,
609 )
610 # accumulate metadata
611 metadata = {
612 "input": args.input,
613 "output": args.output,
614 "butler_argument": args.butler_config,
615 "output_run": run,
616 "extend_run": args.extend_run,
617 "skip_existing_in": args.skip_existing_in,
618 "skip_existing": args.skip_existing,
619 "data_query": args.data_query,
620 "user": getpass.getuser(),
621 "time": f"{datetime.datetime.now()}",
622 }
623 assert run is not None, "Butler output run collection must be defined"
624 qgraph = graphBuilder.makeGraph(
625 task_defs,
626 collections,
627 run,
628 args.data_query,
629 metadata=metadata,
630 datasetQueryConstraint=args.dataset_query_constraint,
631 dataId=pipeline.get_data_id(butler.dimensions),
632 )
633 if args.show_qgraph_header:
634 qgraph.buildAndPrintHeader()
636 # Count quanta in graph; give a warning if it's empty and return None.
637 nQuanta = len(qgraph)
638 if nQuanta == 0:
639 return None
640 else:
641 if _LOG.isEnabledFor(logging.INFO):
642 qg_task_table = self._generateTaskTable(qgraph)
643 qg_task_table_formatted = "\n".join(qg_task_table.pformat_all())
644 _LOG.info(
645 "QuantumGraph contains %d quanta for %d tasks, graph ID: %r\n%s",
646 nQuanta,
647 len(qgraph.taskGraph),
648 qgraph.graphID,
649 qg_task_table_formatted,
650 )
652 if args.save_qgraph:
653 qgraph.saveUri(args.save_qgraph)
655 if args.save_single_quanta:
656 for quantumNode in qgraph:
657 sqgraph = qgraph.subset(quantumNode)
658 uri = args.save_single_quanta.format(quantumNode)
659 sqgraph.saveUri(uri)
661 if args.qgraph_dot:
662 graph2dot(qgraph, args.qgraph_dot)
664 if args.execution_butler_location:
665 butler = Butler(args.butler_config)
666 newArgs = copy.deepcopy(args)
668 def builderShim(butler: Butler) -> Butler:
669 newArgs.butler_config = butler._config
670 # Calling makeWriteButler is done for the side effects of
671 # calling that method, maining parsing all the args into
672 # collection names, creating collections, etc.
673 newButler = _ButlerFactory.makeWriteButler(newArgs)
674 return newButler
676 # Include output collection in collections for input
677 # files if it exists in the repo.
678 all_inputs = args.input
679 if args.output is not None:
680 try:
681 all_inputs += (next(iter(butler.registry.queryCollections(args.output))),)
682 except MissingCollectionError:
683 pass
685 _LOG.debug("Calling buildExecutionButler with collections=%s", all_inputs)
686 buildExecutionButler(
687 butler,
688 qgraph,
689 args.execution_butler_location,
690 run,
691 butlerModifier=builderShim,
692 collections=all_inputs,
693 clobber=args.clobber_execution_butler,
694 datastoreRoot=args.target_datastore_root,
695 transfer=args.transfer,
696 )
698 return qgraph
700 def runPipeline(
701 self,
702 graph: QuantumGraph,
703 taskFactory: TaskFactory,
704 args: SimpleNamespace,
705 butler: Butler | None = None,
706 ) -> None:
707 """Execute complete QuantumGraph.
709 Parameters
710 ----------
711 graph : `~lsst.pipe.base.QuantumGraph`
712 Execution graph.
713 taskFactory : `~lsst.pipe.base.TaskFactory`
714 Task factory
715 args : `types.SimpleNamespace`
716 Parsed command line
717 butler : `~lsst.daf.butler.Butler`, optional
718 Data Butler instance, if not defined then new instance is made
719 using command line options.
720 """
721 # Check that output run defined on command line is consistent with
722 # quantum graph.
723 if args.output_run and graph.metadata:
724 graph_output_run = graph.metadata.get("output_run", args.output_run)
725 if graph_output_run != args.output_run:
726 raise ValueError(
727 f"Output run defined on command line ({args.output_run}) has to be "
728 f"identical to graph metadata ({graph_output_run}). "
729 "To update graph metadata run `pipetask update-graph-run` command."
730 )
732 # Make sure that --extend-run always enables --skip-existing,
733 # clobbering should be disabled if --extend-run is not specified.
734 if args.extend_run:
735 args.skip_existing = True
736 else:
737 args.clobber_outputs = False
739 if not args.enable_implicit_threading:
740 disable_implicit_threading()
742 # Make butler instance. QuantumGraph should have an output run defined,
743 # but we ignore it here and let command line decide actual output run.
744 if butler is None:
745 butler = _ButlerFactory.makeWriteButler(args, graph.iterTaskGraph())
747 if args.skip_existing:
748 args.skip_existing_in += (butler.run,)
750 # Enable lsstDebug debugging. Note that this is done once in the
751 # main process before PreExecInit and it is also repeated before
752 # running each task in SingleQuantumExecutor (which may not be
753 # needed if `multipocessing` always uses fork start method).
754 if args.enableLsstDebug:
755 try:
756 _LOG.debug("Will try to import debug.py")
757 import debug # type: ignore # noqa:F401
758 except ImportError:
759 _LOG.warn("No 'debug' module found.")
761 # Save all InitOutputs, configs, etc.
762 preExecInit = PreExecInit(butler, taskFactory, extendRun=args.extend_run)
763 preExecInit.initialize(
764 graph,
765 saveInitOutputs=not args.skip_init_writes,
766 registerDatasetTypes=args.register_dataset_types,
767 saveVersions=not args.no_versions,
768 )
770 if not args.init_only:
771 graphFixup = self._importGraphFixup(args)
772 quantumExecutor = SingleQuantumExecutor(
773 butler,
774 taskFactory,
775 skipExistingIn=args.skip_existing_in,
776 clobberOutputs=args.clobber_outputs,
777 enableLsstDebug=args.enableLsstDebug,
778 exitOnKnownError=args.fail_fast,
779 )
781 timeout = self.MP_TIMEOUT if args.timeout is None else args.timeout
782 executor = MPGraphExecutor(
783 numProc=args.processes,
784 timeout=timeout,
785 startMethod=args.start_method,
786 quantumExecutor=quantumExecutor,
787 failFast=args.fail_fast,
788 pdb=args.pdb,
789 executionGraphFixup=graphFixup,
790 )
791 # Have to reset connection pool to avoid sharing connections with
792 # forked processes.
793 butler.registry.resetConnectionPool()
794 try:
795 with util.profile(args.profile, _LOG):
796 executor.execute(graph)
797 finally:
798 if args.summary:
799 report = executor.getReport()
800 if report:
801 with open(args.summary, "w") as out:
802 # Do not save fields that are not set.
803 out.write(report.json(exclude_none=True, indent=2))
805 def _generateTaskTable(self, qgraph: QuantumGraph) -> Table:
806 """Generate astropy table listing the number of quanta per task for a
807 given quantum graph.
809 Parameters
810 ----------
811 qgraph : `lsst.pipe.base.graph.graph.QuantumGraph`
812 A QuantumGraph object.
814 Returns
815 -------
816 qg_task_table : `astropy.table.table.Table`
817 An astropy table containing columns: Quanta and Tasks.
818 """
819 qg_quanta, qg_tasks = [], []
820 for task_def in qgraph.iterTaskGraph():
821 num_qnodes = qgraph.getNumberOfQuantaForTask(task_def)
822 qg_quanta.append(num_qnodes)
823 qg_tasks.append(task_def.label)
824 qg_task_table = Table(dict(Quanta=qg_quanta, Tasks=qg_tasks))
825 return qg_task_table
827 def _importGraphFixup(self, args: SimpleNamespace) -> ExecutionGraphFixup | None:
828 """Import/instantiate graph fixup object.
830 Parameters
831 ----------
832 args : `types.SimpleNamespace`
833 Parsed command line.
835 Returns
836 -------
837 fixup : `ExecutionGraphFixup` or `None`
839 Raises
840 ------
841 ValueError
842 Raised if import fails, method call raises exception, or returned
843 instance has unexpected type.
844 """
845 if args.graph_fixup:
846 try:
847 factory = doImportType(args.graph_fixup)
848 except Exception as exc:
849 raise ValueError("Failed to import graph fixup class/method") from exc
850 try:
851 fixup = factory()
852 except Exception as exc:
853 raise ValueError("Failed to make instance of graph fixup") from exc
854 if not isinstance(fixup, ExecutionGraphFixup):
855 raise ValueError("Graph fixup is not an instance of ExecutionGraphFixup class")
856 return fixup
857 return None
859 def preExecInitQBB(self, task_factory: TaskFactory, args: SimpleNamespace) -> None:
860 # Load quantum graph. We do not really need individual Quanta here,
861 # but we need datastore records for initInputs, and those are only
862 # available from Quanta, so load the whole thing.
863 qgraph = QuantumGraph.loadUri(args.qgraph, graphID=args.qgraph_id)
864 universe = qgraph.universe
866 # Collect all init input/output dataset IDs.
867 predicted_inputs: set[DatasetId] = set()
868 predicted_outputs: set[DatasetId] = set()
869 for taskDef in qgraph.iterTaskGraph():
870 if (refs := qgraph.initInputRefs(taskDef)) is not None:
871 predicted_inputs.update(ref.id for ref in refs)
872 if (refs := qgraph.initOutputRefs(taskDef)) is not None:
873 predicted_outputs.update(ref.id for ref in refs)
874 predicted_outputs.update(ref.id for ref in qgraph.globalInitOutputRefs())
875 # remove intermediates from inputs
876 predicted_inputs -= predicted_outputs
878 # Very inefficient way to extract datastore records from quantum graph,
879 # we have to scan all quanta and look at their datastore records.
880 datastore_records: dict[str, DatastoreRecordData] = {}
881 for quantum_node in qgraph:
882 for store_name, records in quantum_node.quantum.datastore_records.items():
883 subset = records.subset(predicted_inputs)
884 if subset is not None:
885 datastore_records.setdefault(store_name, DatastoreRecordData()).update(subset)
887 dataset_types = {dstype.name: dstype for dstype in qgraph.registryDatasetTypes()}
889 # Make butler from everything.
890 butler = QuantumBackedButler.from_predicted(
891 config=args.butler_config,
892 predicted_inputs=predicted_inputs,
893 predicted_outputs=predicted_outputs,
894 dimensions=universe,
895 datastore_records=datastore_records,
896 search_paths=args.config_search_path,
897 dataset_types=dataset_types,
898 )
900 # Save all InitOutputs, configs, etc.
901 preExecInit = PreExecInitLimited(butler, task_factory)
902 preExecInit.initialize(qgraph)
904 def runGraphQBB(self, task_factory: TaskFactory, args: SimpleNamespace) -> None:
905 # Load quantum graph.
906 nodes = args.qgraph_node_id or None
907 qgraph = QuantumGraph.loadUri(args.qgraph, nodes=nodes, graphID=args.qgraph_id)
909 if qgraph.metadata is None:
910 raise ValueError("QuantumGraph is missing metadata, cannot ")
912 dataset_types = {dstype.name: dstype for dstype in qgraph.registryDatasetTypes()}
914 _butler_factory = _QBBFactory(
915 butler_config=args.butler_config,
916 dimensions=qgraph.universe,
917 dataset_types=dataset_types,
918 )
920 # make special quantum executor
921 quantumExecutor = SingleQuantumExecutor(
922 butler=None,
923 taskFactory=task_factory,
924 enableLsstDebug=args.enableLsstDebug,
925 exitOnKnownError=args.fail_fast,
926 limited_butler_factory=_butler_factory,
927 )
929 timeout = self.MP_TIMEOUT if args.timeout is None else args.timeout
930 executor = MPGraphExecutor(
931 numProc=args.processes,
932 timeout=timeout,
933 startMethod=args.start_method,
934 quantumExecutor=quantumExecutor,
935 failFast=args.fail_fast,
936 pdb=args.pdb,
937 )
938 try:
939 with util.profile(args.profile, _LOG):
940 executor.execute(qgraph)
941 finally:
942 if args.summary:
943 report = executor.getReport()
944 if report:
945 with open(args.summary, "w") as out:
946 # Do not save fields that are not set.
947 out.write(report.json(exclude_none=True, indent=2))