DM-39377: Deprecate PipelineTaskConfig.saveMetadata field

doc/changes/DM-39377.removal.md

@@ -0,0 +1,2 @@
+- `PipelineTaskConfig.saveMetadata` field is now deprecated, will be removed after v26. Its value is ignored and task metadata is always saved.
+- `ResourceConfig` class is removed, it was never used.

pyproject.toml

@@ -16,6 +16,7 @@ classifiers = [
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
     "Topic :: Scientific/Engineering :: Astronomy",
 ]
 keywords=["lsst"]

python/lsst/pipe/base/config.py

@@ -24,7 +24,7 @@
 
 from __future__ import annotations
 
-__all__ = ["ResourceConfig", "PipelineTaskConfig"]
+__all__ = ["PipelineTaskConfig"]
 
 # -------------------------------
 #  Imports of standard modules --
@@ -200,6 +200,7 @@ class to allow configuration of the connections class. This dynamically
         default=True,
         optional=False,
         doc="Flag to enable/disable metadata saving for a task, enabled by default.",
+        deprecated="This field is deprecated and will be removed after v26.",
     )
     saveLogOutput = pexConfig.Field[bool](
         default=True,
@@ -254,26 +255,3 @@ def applyConfigOverrides(
                     for key, value in subConfig.rest.items():
                         overrides.addValueOverride(key, value)
         overrides.applyTo(self)
-
-
-class ResourceConfig(pexConfig.Config):
-    """Configuration for resource requirements.
-
-    This configuration class will be used by some activators to estimate
-    resource use by pipeline. Additionally some tasks could use it to adjust
-    their resource use (e.g. reduce the number of threads).
-
-    For some resources their limit can be estimated by corresponding task,
-    in that case task could set the field value. For many fields defined in
-    this class their associated resource used by a task will depend on the
-    size of the data and is not known in advance. For these resources their
-    value will be configured through overrides based on some external
-    estimates.
-    """
-
-    minMemoryMB = pexConfig.Field[int](
-        default=None,
-        optional=True,
-        doc="Minimal memory needed by task, can be None if estimate is unknown.",
-    )
-    minNumCores = pexConfig.Field[int](default=1, doc="Minimal number of cores needed by task.")

python/lsst/pipe/base/pipeline.py

@@ -175,14 +175,9 @@ def configDatasetName(self) -> str:
         return acc.CONFIG_INIT_OUTPUT_TEMPLATE.format(label=self.label)
 
     @property
-    def metadataDatasetName(self) -> Optional[str]:
-        """Name of a dataset type for metadata of this task, `None` if
-        metadata is not to be saved (`str`)
-        """
-        if self.config.saveMetadata:
-            return self.makeMetadataDatasetName(self.label)
-        else:
-            return None
+    def metadataDatasetName(self) -> str:
+        """Name of a dataset type for metadata of this task (`str`)"""
+        return self.makeMetadataDatasetName(self.label)
 
     @classmethod
     def makeMetadataDatasetName(cls, label: str) -> str:
@@ -1067,22 +1062,22 @@ def makeDatasetTypesSet(
 
         # optionally add output dataset for metadata
         outputs = makeDatasetTypesSet("outputs", is_input=False, freeze=False)
-        if taskDef.metadataDatasetName is not None:
-            # Metadata is supposed to be of the TaskMetadata type, its
-            # dimensions correspond to a task quantum.
-            dimensions = registry.dimensions.extract(taskDef.connections.dimensions)
 
-            # Allow the storage class definition to be read from the existing
-            # dataset type definition if present.
-            try:
-                current = registry.getDatasetType(taskDef.metadataDatasetName)
-            except KeyError:
-                # No previous definition so use the default.
-                storageClass = "TaskMetadata" if _TASK_METADATA_TYPE is TaskMetadata else "PropertySet"
-            else:
-                storageClass = current.storageClass.name
+        # Metadata is supposed to be of the TaskMetadata type, its dimensions
+        # correspond to a task quantum.
+        dimensions = registry.dimensions.extract(taskDef.connections.dimensions)
+
+        # Allow the storage class definition to be read from the existing
+        # dataset type definition if present.
+        try:
+            current = registry.getDatasetType(taskDef.metadataDatasetName)
+        except KeyError:
+            # No previous definition so use the default.
+            storageClass = "TaskMetadata" if _TASK_METADATA_TYPE is TaskMetadata else "PropertySet"
+        else:
+            storageClass = current.storageClass.name
+        outputs.update({DatasetType(taskDef.metadataDatasetName, dimensions, storageClass)})
 
-            outputs.update({DatasetType(taskDef.metadataDatasetName, dimensions, storageClass)})
         if taskDef.logOutputDatasetName is not None:
             # Log output dimensions correspond to a task quantum.
             dimensions = registry.dimensions.extract(taskDef.connections.dimensions)

python/lsst/pipe/base/pipelineTask.py

@@ -37,7 +37,7 @@
     from lsst.utils.logging import LsstLogAdapter
 
     from .butlerQuantumContext import ButlerQuantumContext
-    from .config import PipelineTaskConfig, ResourceConfig
+    from .config import PipelineTaskConfig
     from .struct import Struct
 
 
@@ -179,13 +179,3 @@
         inputs = butlerQC.get(inputRefs)
         outputs = self.run(**inputs)
         butlerQC.put(outputs, outputRefs)
-
-    def getResourceConfig(self) -> Optional[ResourceConfig]:
-        """Return resource configuration for this task.
-
-        Returns
-        -------
-        Object of type `~config.ResourceConfig` or ``None`` if resource
-        configuration is not defined for this task.
-        """
-        return getattr(self.config, "resources", None)