feat: Add support for actions in debugger rules.

setup.py

@@ -39,7 +39,7 @@ def read_version():
     "numpy>=1.9.0",
     "protobuf>=3.1",
     "protobuf3-to-dict>=0.1.5",
-    "smdebug_rulesconfig==1.0.0",
+    "smdebug_rulesconfig==1.0.1",
     "importlib-metadata>=1.4.0",
     "packaging>=20.0",
 ]

src/sagemaker/debugger/debugger.py

@@ -150,6 +150,7 @@ def __init__(
         volume_size_in_gb,
         rule_parameters,
         collections_to_save,
+        actions=None,
     ):
         """Configure the debugging rules using the following classmethods.
 
@@ -170,6 +171,7 @@ def __init__(
             rule_parameters,
         )
         self.collection_configs = collections_to_save
+        self.actions = actions
 
     @classmethod
     def sagemaker(
@@ -181,6 +183,7 @@ def sagemaker(
         other_trials_s3_input_paths=None,
         rule_parameters=None,
         collections_to_save=None,
+        actions=None,
     ):
         """Initialize a ``Rule`` object for a *built-in* debugging rule.
 
@@ -268,6 +271,9 @@ def sagemaker(
                 """
             )
 
+        if actions is not None and not rule_configs.is_valid_action_object(actions):
+            raise RuntimeError("""`actions` must be of type `Action` or `ActionList`!""")
+
         if other_trials_s3_input_paths is not None:
             for index, s3_input_path in enumerate(other_trials_s3_input_paths):
                 merged_rule_params["other_trial_{}".format(str(index))] = s3_input_path
@@ -298,6 +304,7 @@ def sagemaker(
             volume_size_in_gb=None,
             rule_parameters=merged_rule_params,
             collections_to_save=collections_to_save or base_config_collections,
+            actions=actions,
         )
 
     @classmethod
@@ -314,6 +321,7 @@ def custom(
         other_trials_s3_input_paths=None,
         rule_parameters=None,
         collections_to_save=None,
+        actions=None,
     ):
         """Initialize a ``Rule`` object for a *custom* debugging rule.
 
@@ -352,6 +360,9 @@ def custom(
             :class:`~sagemaker.debugger.Rule`: The instance of the custom rule.
 
         """
+        if actions is not None and not rule_configs.is_valid_action_object(actions):
+            raise RuntimeError("""`actions` must be of type `Action` or `ActionList`!""")
+
         merged_rule_params = cls._set_rule_parameters(
             source, rule_to_invoke, other_trials_s3_input_paths, rule_parameters
         )
@@ -365,8 +376,25 @@ def custom(
             volume_size_in_gb=volume_size_in_gb,
             rule_parameters=merged_rule_params,
             collections_to_save=collections_to_save or [],
+            actions=actions,
         )
 
+    def prepare_actions(self, training_job_name):
+        """Prepare actions for Debugger Rule.
+
+        Args:
+            training_job_name (str): The training job name. To be set as the default training job
+                prefix for the StopTraining action if it is specified.
+        """
+        if self.actions is None:
+            # user cannot manually specify action_json in rule_parameters for actions.
+            self.rule_parameters.pop("action_json", None)
+            return
+
+        self.actions.update_training_job_prefix_if_not_specified(training_job_name)
+        action_params = {"action_json": self.actions.serialize()}
+        self.rule_parameters.update(action_params)
+
     @staticmethod
     def _set_rule_parameters(source, rule_to_invoke, other_trials_s3_input_paths, rule_parameters):
         """Set rule parameters for Debugger Rule.

src/sagemaker/estimator.py

@@ -473,6 +473,7 @@ def _prepare_debugger_rules(self):
             for rule in self.debugger_rules:
                 self._set_default_rule_config(rule)
                 self._set_source_s3_uri(rule)
+                rule.prepare_actions(self._current_job_name)
                 debugger_rule_configs.append(rule.to_debugger_rule_config_dict())
         return debugger_rule_configs
 

tests/integ/test_debugger.py

@@ -60,6 +60,15 @@
 # TODO-reinvent-2019: test get_debugger_artifacts_path and get_tensorboard_artifacts_path
 
 
+@pytest.fixture
+def actions():
+    return rule_configs.ActionList(
+        rule_configs.StopTraining(),
+        rule_configs.Email("[email protected]"),
+        rule_configs.SMS("+01234567890"),
+    )
+
+
 def test_mxnet_with_rules(
     sagemaker_session,
     mxnet_training_latest_version,
@@ -125,6 +134,74 @@ def test_mxnet_with_rules(
         _wait_and_assert_that_no_rule_jobs_errored(training_job=mx.latest_training_job)
 
 
+def test_mxnet_with_rules_and_actions(
+    sagemaker_session,
+    mxnet_training_latest_version,
+    mxnet_training_latest_py_version,
+    cpu_instance_type,
+    actions,
+):
+    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        rules = [
+            Rule.sagemaker(rule_configs.vanishing_gradient(), actions=actions),
+            Rule.sagemaker(
+                base_config=rule_configs.all_zero(),
+                rule_parameters={"tensor_regex": ".*"},
+                actions=actions,
+            ),
+            Rule.sagemaker(rule_configs.loss_not_decreasing(), actions=actions),
+        ]
+
+        script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_gluon.py")
+        data_path = os.path.join(DATA_DIR, "mxnet_mnist")
+
+        mx = MXNet(
+            entry_point=script_path,
+            role="SageMakerRole",
+            framework_version=mxnet_training_latest_version,
+            py_version=mxnet_training_latest_py_version,
+            instance_count=1,
+            instance_type=cpu_instance_type,
+            sagemaker_session=sagemaker_session,
+            rules=rules,
+        )
+
+        train_input = mx.sagemaker_session.upload_data(
+            path=os.path.join(data_path, "train"), key_prefix="integ-test-data/mxnet_mnist/train"
+        )
+        test_input = mx.sagemaker_session.upload_data(
+            path=os.path.join(data_path, "test"), key_prefix="integ-test-data/mxnet_mnist/test"
+        )
+
+        mx.fit({"train": train_input, "test": test_input})
+
+        job_description = mx.latest_training_job.describe()
+
+        for index, rule in enumerate(rules):
+            assert (
+                job_description["DebugRuleConfigurations"][index]["RuleConfigurationName"]
+                == rule.name
+            )
+            assert (
+                job_description["DebugRuleConfigurations"][index]["RuleEvaluatorImage"]
+                == rule.image_uri
+            )
+            assert job_description["DebugRuleConfigurations"][index]["VolumeSizeInGB"] == 0
+            assert (
+                job_description["DebugRuleConfigurations"][index]["RuleParameters"][
+                    "rule_to_invoke"
+                ]
+                == rule.rule_parameters["rule_to_invoke"]
+            )
+
+        assert (
+            _get_rule_evaluation_statuses(job_description)
+            == mx.latest_training_job.rule_job_summary()
+        )
+
+        _wait_and_assert_that_no_rule_jobs_errored(training_job=mx.latest_training_job)
+
+
 def test_mxnet_with_custom_rule(
     sagemaker_session,
     mxnet_training_latest_version,
@@ -178,6 +255,60 @@ def test_mxnet_with_custom_rule(
         _wait_and_assert_that_no_rule_jobs_errored(training_job=mx.latest_training_job)
 
 
+def test_mxnet_with_custom_rule_and_actions(
+    sagemaker_session,
+    mxnet_training_latest_version,
+    mxnet_training_latest_py_version,
+    cpu_instance_type,
+    actions,
+):
+    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        rules = [_get_custom_rule(sagemaker_session, actions)]
+
+        script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_gluon.py")
+        data_path = os.path.join(DATA_DIR, "mxnet_mnist")
+
+        mx = MXNet(
+            entry_point=script_path,
+            role="SageMakerRole",
+            framework_version=mxnet_training_latest_version,
+            py_version=mxnet_training_latest_py_version,
+            instance_count=1,
+            instance_type=cpu_instance_type,
+            sagemaker_session=sagemaker_session,
+            rules=rules,
+        )
+
+        train_input = mx.sagemaker_session.upload_data(
+            path=os.path.join(data_path, "train"), key_prefix="integ-test-data/mxnet_mnist/train"
+        )
+        test_input = mx.sagemaker_session.upload_data(
+            path=os.path.join(data_path, "test"), key_prefix="integ-test-data/mxnet_mnist/test"
+        )
+
+        mx.fit({"train": train_input, "test": test_input})
+
+        job_description = mx.latest_training_job.describe()
+
+        for index, rule in enumerate(rules):
+            assert (
+                job_description["DebugRuleConfigurations"][index]["RuleConfigurationName"]
+                == rule.name
+            )
+            assert (
+                job_description["DebugRuleConfigurations"][index]["RuleEvaluatorImage"]
+                == rule.image_uri
+            )
+            assert job_description["DebugRuleConfigurations"][index]["VolumeSizeInGB"] == 30
+
+        assert (
+            _get_rule_evaluation_statuses(job_description)
+            == mx.latest_training_job.rule_job_summary()
+        )
+
+        _wait_and_assert_that_no_rule_jobs_errored(training_job=mx.latest_training_job)
+
+
 def test_mxnet_with_debugger_hook_config(
     sagemaker_session,
     mxnet_training_latest_version,
@@ -514,7 +645,7 @@ def _get_rule_evaluation_statuses(job_description):
     return debug_rule_eval_statuses + profiler_rule_eval_statuses
 
 
-def _get_custom_rule(session):
+def _get_custom_rule(session, actions=None):
     script_path = os.path.join(DATA_DIR, "mxnet_mnist", "my_custom_rule.py")
 
     return Rule.custom(
@@ -526,6 +657,7 @@ def _get_custom_rule(session):
         image_uri=CUSTOM_RULE_REPO_WITH_PLACEHOLDERS.format(
             CUSTOM_RULE_CONTAINERS_ACCOUNTS_MAP[session.boto_region_name], session.boto_region_name
         ),
+        actions=actions,
     )
 
 

tests/unit/test_estimator.py

@@ -385,6 +385,58 @@ def test_framework_with_only_debugger_rule(sagemaker_session):
     }
 
 
+def test_framework_with_debugger_rule_and_single_action(sagemaker_session):
+    stop_training_action = rule_configs.StopTraining()
+    f = DummyFramework(
+        entry_point=SCRIPT_PATH,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        instance_count=INSTANCE_COUNT,
+        instance_type=INSTANCE_TYPE,
+        rules=[Rule.sagemaker(rule_configs.stalled_training_rule(), actions=stop_training_action)],
+    )
+    f.fit("s3://mydata")
+    sagemaker_session.train.assert_called_once()
+    _, args = sagemaker_session.train.call_args
+    assert args["debugger_rule_configs"][0]["RuleParameters"] == {
+        "rule_to_invoke": "StalledTrainingRule",
+        "action_json": stop_training_action.serialize(),
+    }
+    assert stop_training_action.action_parameters["training_job_prefix"] == f._current_job_name
+    assert args["debugger_hook_config"] == {
+        "S3OutputPath": "s3://{}/".format(BUCKET_NAME),
+        "CollectionConfigurations": [],
+    }
+
+
+def test_framework_with_debugger_rule_and_multiple_actions(sagemaker_session):
+    action_list = rule_configs.ActionList(
+        rule_configs.StopTraining(),
+        rule_configs.Email("[email protected]"),
+        rule_configs.SMS("+1234567890"),
+    )
+    f = DummyFramework(
+        entry_point=SCRIPT_PATH,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        instance_count=INSTANCE_COUNT,
+        instance_type=INSTANCE_TYPE,
+        rules=[Rule.sagemaker(rule_configs.stalled_training_rule(), actions=action_list)],
+    )
+    f.fit("s3://mydata")
+    sagemaker_session.train.assert_called_once()
+    _, args = sagemaker_session.train.call_args
+    assert args["debugger_rule_configs"][0]["RuleParameters"] == {
+        "rule_to_invoke": "StalledTrainingRule",
+        "action_json": action_list.serialize(),
+    }
+    assert action_list.actions[0].action_parameters["training_job_prefix"] == f._current_job_name
+    assert args["debugger_hook_config"] == {
+        "S3OutputPath": "s3://{}/".format(BUCKET_NAME),
+        "CollectionConfigurations": [],
+    }
+
+
 def test_framework_with_only_debugger_hook_config(sagemaker_session):
     hook_config = DebuggerHookConfig(
         s3_output_path="s3://output", collection_configs=[CollectionConfig(name="weights")]