Merge pull request #2086 from FedML-AI/dimitris/pydantic_conflict_res…

…olution Removing field validator for resolving spacy and pydantic conflict.
FedML-AI · May 7, 2024 · 5e41f65 · 5e41f65
2 parents 69707c4 + c6cb5b0
commit 5e41f65
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Showing 2 changed files with 12 additions and 11 deletions.
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
@@ -1,4 +1,4 @@
-from pydantic import BaseModel, field_validator, NonNegativeInt, NonNegativeFloat
+from pydantic import BaseModel, NonNegativeInt, NonNegativeFloat, validator
 
 
 class AutoscalingPolicy(BaseModel):
@@ -70,9 +70,10 @@ class EWMPolicy(AutoscalingPolicy):
     ub_threshold: NonNegativeFloat  # recommended value: 0.5
     lb_threshold: NonNegativeFloat  # recommended value: 0.5
 
-    @field_validator("metric")
-    def validate_option(cls, v):
-        assert v in ["ewm_latency", "ewm_qps"]
+    @validator("metric")
+    def metric_match(cls, v) -> str:
+        if v not in ["ewm_latency", "ewm_qps"]:
+            raise ValueError("Wrong metric name.")
         return v
 
 

diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/autoscaler_test.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/autoscaler_test.py
@@ -111,21 +111,21 @@ def test_validate_scaling_bounds(self):
 
         # Validate scale up.
         scale_up = autoscaler.validate_scaling_bounds(ScaleOp.UP_OUT_OP, autoscaling_policy)
-        self.assertEquals(scale_up, ScaleOp.UP_OUT_OP)
+        self.assertEqual(scale_up, ScaleOp.UP_OUT_OP)
 
         # Validate scale down.
         scale_down = autoscaler.validate_scaling_bounds(ScaleOp.DOWN_IN_OP, autoscaling_policy)
-        self.assertEquals(scale_down, ScaleOp.DOWN_IN_OP)
+        self.assertEqual(scale_down, ScaleOp.DOWN_IN_OP)
 
         # Validate max out-of-bounds.
         autoscaling_policy.current_replicas = 3
         scale_oob_max = autoscaler.validate_scaling_bounds(ScaleOp.UP_OUT_OP, autoscaling_policy)
-        self.assertEquals(scale_oob_max, ScaleOp.NO_OP)
+        self.assertEqual(scale_oob_max, ScaleOp.NO_OP)
 
         # Validate min out-of-bounds.
         autoscaling_policy.current_replicas = 1
         scale_oob_min = autoscaler.validate_scaling_bounds(ScaleOp.DOWN_IN_OP, autoscaling_policy)
-        self.assertEquals(scale_oob_min, ScaleOp.NO_OP)
+        self.assertEqual(scale_oob_min, ScaleOp.NO_OP)
 
     def test_enforce_scaling_down_delay_interval(self):
         self.populate_redis_with_dummy_metrics()
@@ -140,15 +140,15 @@ def test_enforce_scaling_down_delay_interval(self):
 
         autoscaling_policy.scaledown_delay_secs = 0.0
         scale_down = autoscaler.enforce_scaling_down_delay_interval(ENV_ENDPOINT_ID_1, autoscaling_policy)
-        self.assertEquals(scale_down, ScaleOp.DOWN_IN_OP)
+        self.assertEqual(scale_down, ScaleOp.DOWN_IN_OP)
 
         autoscaling_policy.scaledown_delay_secs = 1
         scale_noop = autoscaler.enforce_scaling_down_delay_interval(ENV_ENDPOINT_ID_1, autoscaling_policy)
-        self.assertEquals(scale_noop, ScaleOp.NO_OP)
+        self.assertEqual(scale_noop, ScaleOp.NO_OP)
 
         time.sleep(2)
         scale_down = autoscaler.enforce_scaling_down_delay_interval(ENV_ENDPOINT_ID_1, autoscaling_policy)
-        self.assertEquals(scale_down, ScaleOp.DOWN_IN_OP)
+        self.assertEqual(scale_down, ScaleOp.DOWN_IN_OP)
         self.clear_redis()