fix: Add retries to pipeline execution

tests/integ/test_workflow.py

@@ -18,6 +18,7 @@
 import subprocess
 import time
 import uuid
+import logging
 
 from contextlib import contextmanager
 import pytest
@@ -75,6 +76,7 @@
 from sagemaker.feature_store.feature_group import FeatureGroup, FeatureDefinition, FeatureTypeEnum
 from tests.integ import DATA_DIR
 from tests.integ.kms_utils import get_or_create_kms_key
+from tests.integ.retry import retries
 
 
 def ordered(obj):
@@ -1850,47 +1852,57 @@ def test_training_job_with_debugger_and_profiler(
         sagemaker_session=sagemaker_session,
     )
 
-    try:
-        response = pipeline.create(role)
-        create_arn = response["PipelineArn"]
-
-        execution = pipeline.start()
-        response = execution.describe()
-        assert response["PipelineArn"] == create_arn
-
+    for _ in retries(
+        max_retry_count=5,
+        exception_message_prefix="Waiting for a successful execution of pipeline",
+        seconds_to_sleep=10,
+    ):
         try:
-            execution.wait(delay=10, max_attempts=60)
-        except WaiterError:
-            pass
-        execution_steps = execution.list_steps()
+            response = pipeline.create(role)
+            create_arn = response["PipelineArn"]
 
-        assert len(execution_steps) == 1
-        assert execution_steps[0].get("FailureReason", "") == ""
-        assert execution_steps[0]["StepName"] == "pytorch-train"
-        assert execution_steps[0]["StepStatus"] == "Succeeded"
+            execution = pipeline.start()
+            response = execution.describe()
+            assert response["PipelineArn"] == create_arn
 
-        training_job_arn = execution_steps[0]["Metadata"]["TrainingJob"]["Arn"]
-        job_description = sagemaker_session.sagemaker_client.describe_training_job(
-            TrainingJobName=training_job_arn.split("/")[1]
-        )
+            try:
+                execution.wait(delay=10, max_attempts=60)
+            except WaiterError:
+                pass
+            execution_steps = execution.list_steps()
 
-        for index, rule in enumerate(rules):
-            config = job_description["DebugRuleConfigurations"][index]
-            assert config["RuleConfigurationName"] == rule.name
-            assert config["RuleEvaluatorImage"] == rule.image_uri
-            assert config["VolumeSizeInGB"] == 0
-            assert (
-                config["RuleParameters"]["rule_to_invoke"] == rule.rule_parameters["rule_to_invoke"]
+            assert len(execution_steps) == 1
+            failure_reason = execution_steps[0].get("FailureReason", "")
+            if failure_reason != "":
+                logging.error(f"Pipeline execution failed with error: {failure_reason}.Retrying..")
+                continue
+            assert execution_steps[0]["StepName"] == "pytorch-train"
+            assert execution_steps[0]["StepStatus"] == "Succeeded"
+
+            training_job_arn = execution_steps[0]["Metadata"]["TrainingJob"]["Arn"]
+            job_description = sagemaker_session.sagemaker_client.describe_training_job(
+                TrainingJobName=training_job_arn.split("/")[1]
             )
-        assert job_description["DebugHookConfig"] == debugger_hook_config._to_request_dict()
 
-        assert job_description["ProfilingStatus"] == "Enabled"
-        assert job_description["ProfilerConfig"]["ProfilingIntervalInMilliseconds"] == 500
-    finally:
-        try:
-            pipeline.delete()
-        except Exception:
-            pass
+            for index, rule in enumerate(rules):
+                config = job_description["DebugRuleConfigurations"][index]
+                assert config["RuleConfigurationName"] == rule.name
+                assert config["RuleEvaluatorImage"] == rule.image_uri
+                assert config["VolumeSizeInGB"] == 0
+                assert (
+                    config["RuleParameters"]["rule_to_invoke"]
+                    == rule.rule_parameters["rule_to_invoke"]
+                )
+            assert job_description["DebugHookConfig"] == debugger_hook_config._to_request_dict()
+
+            assert job_description["ProfilingStatus"] == "Enabled"
+            assert job_description["ProfilerConfig"]["ProfilingIntervalInMilliseconds"] == 500
+            break
+        finally:
+            try:
+                pipeline.delete()
+            except Exception:
+                pass
 
 
 def test_two_processing_job_depends_on(