Merge branch 'master' into master

Author: HappyAmazonian

src/sagemaker/estimator.py

@@ -865,10 +865,6 @@ def _prepare_debugger_for_training(self):
         self.debugger_rule_configs = self._prepare_debugger_rules()
         self._prepare_collection_configs()
         self._validate_and_set_debugger_configs()
-        if not self.debugger_hook_config:
-            if self.environment is None:
-                self.environment = {}
-            self.environment[DEBUGGER_FLAG] = "0"
 
     def _validate_and_set_debugger_configs(self):
         """Set defaults for debugging."""

tests/integ/test_debugger.py

@@ -18,7 +18,6 @@
 import pytest
 
 from sagemaker.debugger.debugger import (
-    DEBUGGER_FLAG,
     DebuggerHookConfig,
     Rule,
     rule_configs,
@@ -749,7 +748,6 @@ def test_mxnet_with_debugger_hook_config_disabled(
         job_description = mx.latest_training_job.describe()
 
         assert job_description.get("DebugHookConfig") is None
-        assert job_description.get("Environment", {}).get(DEBUGGER_FLAG) == "0"
 
 
 def _get_rule_evaluation_statuses(job_description):

tests/integ/test_tuner.py

@@ -601,7 +601,7 @@ def test_tuning_tf(
 
     hyperparameter_ranges = {"epochs": IntegerParameter(1, 2)}
     objective_metric_name = "accuracy"
-    metric_definitions = [{"Name": objective_metric_name, "Regex": "accuracy = ([0-9\\.]+)"}]
+    metric_definitions = [{"Name": objective_metric_name, "Regex": "Accuracy: ([0-9\\.]+)"}]
 
     tuner = HyperparameterTuner(
         estimator,
@@ -656,7 +656,7 @@ def test_tuning_tf_vpc_multi(
 
     hyperparameter_ranges = {"epochs": IntegerParameter(1, 2)}
     objective_metric_name = "accuracy"
-    metric_definitions = [{"Name": objective_metric_name, "Regex": "accuracy = ([0-9\\.]+)"}]
+    metric_definitions = [{"Name": objective_metric_name, "Regex": "Accuracy: ([0-9\\.]+)"}]
 
     tuner = HyperparameterTuner(
         estimator,

tests/unit/sagemaker/tensorflow/test_estimator.py

@@ -483,7 +483,6 @@ def test_fit_ps(time, strftime, sagemaker_session):
     expected_train_args = _create_train_job("1.11", ps=True, py_version="py2")
     expected_train_args["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] = inputs
     expected_train_args["hyperparameters"][TensorFlow.LAUNCH_PS_ENV_NAME] = json.dumps(True)
-    expected_train_args["environment"] = {"USE_SMDEBUG": "0"}
 
     actual_train_args = sagemaker_session.method_calls[0][2]
     assert actual_train_args == expected_train_args

tests/unit/sagemaker/workflow/test_steps.py

@@ -26,7 +26,7 @@
     patch,
 )
 
-from sagemaker.debugger import DEBUGGER_FLAG, ProfilerConfig
+from sagemaker.debugger import ProfilerConfig
 from sagemaker.estimator import Estimator
 from sagemaker.tensorflow import TensorFlow
 from sagemaker.inputs import TrainingInput, TransformInput, CreateModelInput
@@ -485,7 +485,6 @@ def test_training_step_tensorflow(sagemaker_session):
                 "sagemaker_distributed_dataparallel_custom_mpi_options": '""',
             },
             "ProfilerConfig": {"S3OutputPath": "s3://my-bucket/"},
-            "Environment": {DEBUGGER_FLAG: "0"},
         },
         "CacheConfig": {"Enabled": True, "ExpireAfter": "PT1H"},
     }