Introduced on_demand_benchmark decorator - see SciTools/iris#4621.

benchmarks/benchmarks/__init__.py

@@ -1,6 +1,9 @@
 """Benchmark tests for iris-esmf-regrid"""
 
 
+from os import environ
+
+
 def disable_repeat_between_setup(benchmark_object):
     """
     Decorator for benchmarks where object persistence would be inappropriate.
@@ -30,15 +33,38 @@ def disable_repeat_between_setup(benchmark_object):
 def skip_benchmark(benchmark_object):
     """
     Decorator for benchmarks skipping benchmarks.
+
+    Simply doesn't return the object.
+
+    Warnings
+    --------
+    ASV's architecture means decorated classes cannot be sub-classed. Code for
+    inheritance should be in a mixin class that doesn't include any methods
+    which ASV will recognise as benchmarks
+    (e.g. ``def time_something(self):`` ).
+
+    """
+    pass
+
+
+def on_demand_benchmark(benchmark_object):
     """
+    Decorator. Disables these benchmark(s) unless ON_DEMAND_BENCHARKS env var is set.
 
-    def setup_cache(self):
-        pass
+    For benchmarks that, for whatever reason, should not be run by default.
+    E.g:
+        * Require a local file
+        * Used for scalability analysis instead of commit monitoring.
 
-    def setup(*args):
-        raise NotImplementedError
+    Can be applied to benchmark classes/methods/functions.
 
-    benchmark_object.setup_cache = setup_cache
-    benchmark_object.setup = setup
+    Warnings
+    --------
+    ASV's architecture means decorated classes cannot be sub-classed. Code for
+    inheritance should be in a mixin class that doesn't include any methods
+    which ASV will recognise as benchmarks
+    (e.g. ``def time_something(self):`` ).
 
-    return benchmark_object
+    """
+    if "ON_DEMAND_BENCHMARKS" in environ:
+        return benchmark_object

benchmarks/benchmarks/long/esmf_regridder.py

@@ -15,11 +15,11 @@
 )
 from esmf_regrid.schemes import ESMFAreaWeightedRegridder
 
-from .. import skip_benchmark
+from .. import on_demand_benchmark, skip_benchmark
 from ..generate_data import _grid_cube, _gridlike_mesh_cube
 
 
-class PrepareScalabilityGridToGrid:
+class PrepareScalabilityMixin:
     timeout = 180
     params = [50, 100, 200, 400, 600, 800]
     param_names = ["grid width"]
@@ -42,11 +42,18 @@ def setup(self, n):
         self.src = self.src_cube(n)
         self.tgt = self.tgt_cube(n)
 
-    def time_prepare(self, n):
+    def _time_prepare(self, n):
         _ = self.regridder(self.src, self.tgt)
 
 
-class PrepareScalabilityMeshToGrid(PrepareScalabilityGridToGrid):
+@on_demand_benchmark
+class PrepareScalabilityGridToGrid(PrepareScalabilityMixin):
+    def time_prepare(self, n):
+        super()._time_prepare(n)
+
+
+@on_demand_benchmark
+class PrepareScalabilityMeshToGrid(PrepareScalabilityMixin):
     regridder = MeshToGridESMFRegridder
 
     def src_cube(self, n):
@@ -83,10 +90,11 @@ def time_save(self, _, n):
         save_regridder(self.rg, self.destination_file)
 
     def time_prepare(self, _, n):
-        super().time_prepare(n)
+        super()._time_prepare(n)
 
 
-class PrepareScalabilityGridToMesh(PrepareScalabilityGridToGrid):
+@on_demand_benchmark
+class PrepareScalabilityGridToMesh(PrepareScalabilityMixin):
     regridder = GridToMeshESMFRegridder
 
     def tgt_cube(self, n):
@@ -123,10 +131,10 @@ def time_save(self, _, n):
         save_regridder(self.rg, self.destination_file)
 
     def time_prepare(self, _, n):
-        super().time_prepare(n)
+        super()._time_prepare(n)
 
 
-class PerformScalabilityGridToGrid:
+class PerformScalabilityMixin:
     params = [100, 200, 400, 600, 800, 1000]
     param_names = ["height"]
     grid_size = 400
@@ -185,21 +193,31 @@ def setup(self, cache, height):
         cube = self.add_src_metadata(cube)
         self.result = regridder(cube)
 
-    def time_perform(self, cache, height):
+    def _time_perform(self, cache, height):
         assert not self.src.has_lazy_data()
         rg, _ = cache
         _ = rg(self.src)
 
-    def time_lazy_perform(self, cache, height):
+    def _time_lazy_perform(self, cache, height):
         # Don't touch result.data - permanent realisation plays badly with
         #  ASV's re-run strategy.
         assert self.result.has_lazy_data()
         self.result.core_data().compute()
 
 
-class PerformScalabilityMeshToGrid(PerformScalabilityGridToGrid):
+@on_demand_benchmark
+class PerformScalabilityGridToGrid(PerformScalabilityMixin):
+    def time_perform(self, cache, height):
+        super()._time_perform(cache, height)
+
+    def time_lazy_perform(self, cache, height):
+        super()._time_lazy_perform(cache, height)
+
+
+@on_demand_benchmark
+class PerformScalabilityMeshToGrid(PerformScalabilityMixin):
     regridder = MeshToGridESMFRegridder
-    chunk_size = [PerformScalabilityGridToGrid.grid_size ^ 2, 10]
+    chunk_size = [PerformScalabilityMixin.grid_size ^ 2, 10]
     file_name = "chunked_cube_1d.nc"
 
     def setup_cache(self):
@@ -223,8 +241,15 @@ def add_src_metadata(self, cube):
         cube.add_aux_coord(mesh_coord_y, 0)
         return cube
 
+    def time_perform(self, cache, height):
+        super()._time_perform(cache, height)
+
+    def time_lazy_perform(self, cache, height):
+        super()._time_lazy_perform(cache, height)
 
-class PerformScalabilityGridToMesh(PerformScalabilityGridToGrid):
+
+@on_demand_benchmark
+class PerformScalabilityGridToMesh(PerformScalabilityMixin):
     regridder = GridToMeshESMFRegridder
 
     def setup_cache(self):
@@ -242,11 +267,17 @@ def tgt_cube(self):
         tgt.add_aux_coord(mesh_coord_y, 0)
         return tgt
 
+    def time_perform(self, cache, height):
+        super()._time_perform(cache, height)
+
+    def time_lazy_perform(self, cache, height):
+        super()._time_lazy_perform(cache, height)
+
 
 # These benchmarks unusually long and are resource intensive so are skipped.
 # They can be run by manually removing the skip.
 @skip_benchmark
-class PerformScalability1kGridToGrid(PerformScalabilityGridToGrid):
+class PerformScalability1kGridToGrid(PerformScalabilityMixin):
     timeout = 600
     grid_size = 1100
     chunk_size = [grid_size, grid_size, 10]
@@ -259,11 +290,17 @@ class PerformScalability1kGridToGrid(PerformScalabilityGridToGrid):
     def setup_cache(self):
         return super().setup_cache()
 
+    def time_perform(self, cache, height):
+        super()._time_perform(cache, height)
+
+    def time_lazy_perform(self, cache, height):
+        super()._time_lazy_perform(cache, height)
+
 
 # These benchmarks unusually long and are resource intensive so are skipped.
 # They can be run by manually removing the skip.
 @skip_benchmark
-class PerformScalability2kGridToGrid(PerformScalabilityGridToGrid):
+class PerformScalability2kGridToGrid(PerformScalabilityMixin):
     timeout = 600
     grid_size = 2200
     chunk_size = [grid_size, grid_size, 10]
@@ -275,3 +312,9 @@ class PerformScalability2kGridToGrid(PerformScalabilityGridToGrid):
 
     def setup_cache(self):
         return super().setup_cache()
+
+    def time_perform(self, cache, height):
+        super()._time_perform(cache, height)
+
+    def time_lazy_perform(self, cache, height):
+        super()._time_lazy_perform(cache, height)

noxfile.py

@@ -475,6 +475,8 @@ def benchmarks(
         )
         publish_subdir.mkdir()
 
+        # Activate on demand benchmarks (C/SPerf are deactivated for 'standard' runs).
+        session.env["ON_DEMAND_BENCHMARKS"] = "True"
         commit_range = "upstream/main^!"
 
         asv_command = [