python3.10 -m black -l 100

Author: Lokiiiiii

src/sagemaker/estimator.py

@@ -741,7 +741,6 @@ def _prepare_for_training(self, job_name=None):
             self.dependencies = updated_paths["dependencies"]
 
         if self.source_dir or self.entry_point or self.dependencies:
-
             # validate source dir will raise a ValueError if there is something wrong with
             # the source directory. We are intentionally not handling it because this is a
             # critical error.

tests/conftest.py

@@ -277,7 +277,9 @@ def huggingface_pytorch_training_py_version(huggingface_pytorch_training_version
 
 
 @pytest.fixture(scope="module")
-def huggingface_training_compiler_pytorch_version(huggingface_training_compiler_version,):
+def huggingface_training_compiler_pytorch_version(
+    huggingface_training_compiler_version,
+):
     versions = _huggingface_base_fm_version(
         huggingface_training_compiler_version, "pytorch", "huggingface_training_compiler"
     )
@@ -290,7 +292,9 @@ def huggingface_training_compiler_pytorch_version(huggingface_training_compiler_
 
 
 @pytest.fixture(scope="module")
-def huggingface_training_compiler_tensorflow_version(huggingface_training_compiler_version,):
+def huggingface_training_compiler_tensorflow_version(
+    huggingface_training_compiler_version,
+):
     versions = _huggingface_base_fm_version(
         huggingface_training_compiler_version, "tensorflow", "huggingface_training_compiler"
     )
@@ -321,14 +325,18 @@ def huggingface_training_compiler_pytorch_py_version(
 
 
 @pytest.fixture(scope="module")
-def huggingface_pytorch_latest_training_py_version(huggingface_training_pytorch_latest_version,):
+def huggingface_pytorch_latest_training_py_version(
+    huggingface_training_pytorch_latest_version,
+):
     return (
         "py38" if Version(huggingface_training_pytorch_latest_version) >= Version("1.9") else "py36"
     )
 
 
 @pytest.fixture(scope="module")
-def huggingface_pytorch_latest_inference_py_version(huggingface_inference_pytorch_latest_version,):
+def huggingface_pytorch_latest_inference_py_version(
+    huggingface_inference_pytorch_latest_version,
+):
     return (
         "py38"
         if Version(huggingface_inference_pytorch_latest_version) >= Version("1.9")

tests/integ/test_tf.py

@@ -118,11 +118,11 @@ def test_mnist_with_checkpoint_config(
     actual_training_checkpoint_config = sagemaker_session.sagemaker_client.describe_training_job(
         TrainingJobName=training_job_name
     )["CheckpointConfig"]
-    actual_training_environment_variable_config = sagemaker_session.sagemaker_client.describe_training_job(
-        TrainingJobName=training_job_name
-    )[
-        "Environment"
-    ]
+    actual_training_environment_variable_config = (
+        sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=training_job_name)[
+            "Environment"
+        ]
+    )
 
     expected_retry_strategy = {"MaximumRetryAttempts": 2}
     actual_retry_strategy = sagemaker_session.sagemaker_client.describe_training_job(

tests/integ/test_training_compiler.py

@@ -149,7 +149,6 @@ def test_pytorch(
     Test the PyTorch estimator
     """
     with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
-
         hf = PyTorch(
             py_version="py38",
             source_dir=os.path.join(DATA_DIR, "huggingface_byoc"),

tests/unit/test_estimator.py

@@ -1163,7 +1163,9 @@ def test_framework_with_disable_framework_metrics(sagemaker_session):
     assert "profiler_rule_configs" not in args
 
 
-def test_framework_with_disable_framework_metrics_and_update_system_metrics(sagemaker_session,):
+def test_framework_with_disable_framework_metrics_and_update_system_metrics(
+    sagemaker_session,
+):
     f = DummyFramework(
         entry_point=SCRIPT_PATH,
         role=ROLE,
@@ -1183,7 +1185,9 @@ def test_framework_with_disable_framework_metrics_and_update_system_metrics(sage
     assert "profiler_rule_configs" not in args
 
 
-def test_framework_with_disable_framework_metrics_and_update_framework_params(sagemaker_session,):
+def test_framework_with_disable_framework_metrics_and_update_framework_params(
+    sagemaker_session,
+):
     with pytest.raises(ValueError) as error:
         f = DummyFramework(
             entry_point=SCRIPT_PATH,
@@ -3753,7 +3757,6 @@ def test_prepare_init_params_from_job_description_with_training_image_config():
 
 
 def test_prepare_init_params_from_job_description_with_invalid_training_job():
-
     invalid_job_description = RETURNED_JOB_DESCRIPTION.copy()
     invalid_job_description["AlgorithmSpecification"] = {"TrainingInputMode": "File"}
 
@@ -3805,7 +3808,9 @@ def test_prepare_for_training_with_name_based_on_algorithm(sagemaker_session):
 
 
 @patch("sagemaker.workflow.utilities._pipeline_config", MOCKED_PIPELINE_CONFIG)
-def test_prepare_for_training_with_pipeline_name_in_s3_path_no_source_dir(pipeline_session,):
+def test_prepare_for_training_with_pipeline_name_in_s3_path_no_source_dir(
+    pipeline_session,
+):
     # script_uri is NOT provided -> use new cache key behavior that builds path using pipeline name + code_hash
     image_uri = "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.9.0-gpu-py38"
     model_uri = "s3://someprefix2/models/model.tar.gz"
@@ -3993,7 +3998,6 @@ def test_script_mode_estimator(patched_stage_user_code, sagemaker_session):
 def test_script_mode_estimator_same_calls_as_framework(
     patched_tar_and_upload_dir, sagemaker_session
 ):
-
     patched_tar_and_upload_dir.return_value = UploadedCode(
         s3_prefix="s3://%s/%s" % ("bucket", "key"), script_name="script_name"
     )
@@ -4253,7 +4257,6 @@ def test_script_mode_estimator_tags_jumpstart_models_with_no_estimator_js_tags(
 def test_all_framework_estimators_add_jumpstart_tags(
     patched_repack_model, patched_upload_code, patched_tar_and_upload_dir, sagemaker_session
 ):
-
     sagemaker_session.boto_region_name = REGION
     sagemaker_session.sagemaker_client.describe_training_job.return_value = {
         "ModelArtifacts": {"S3ModelArtifacts": "some-uri"}
@@ -4286,7 +4289,7 @@ def test_all_framework_estimators_add_jumpstart_tags(
     }
     jumpstart_model_uri = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[0]}/model_dirs/model.tar.gz"
     jumpstart_model_uri_2 = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[1]}/model_dirs/model.tar.gz"
-    for (framework_estimator_class, kwargs) in framework_estimator_classes_to_kwargs.items():
+    for framework_estimator_class, kwargs in framework_estimator_classes_to_kwargs.items():
         estimator = framework_estimator_class(
             entry_point=ENTRY_POINT,
             role=ROLE,
@@ -4392,7 +4395,6 @@ def test_script_mode_estimator_uses_jumpstart_base_name_with_js_models(
 def test_all_framework_estimators_add_jumpstart_base_name(
     patched_repack_model, patched_upload_code, patched_tar_and_upload_dir, sagemaker_session
 ):
-
     sagemaker_session.boto_region_name = REGION
     sagemaker_session.sagemaker_client.describe_training_job.return_value = {
         "ModelArtifacts": {"S3ModelArtifacts": "some-uri"}
@@ -4425,7 +4427,7 @@ def test_all_framework_estimators_add_jumpstart_base_name(
     }
     jumpstart_model_uri = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[0]}/model_dirs/model.tar.gz"
     jumpstart_model_uri_2 = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[1]}/model_dirs/model.tar.gz"
-    for (framework_estimator_class, kwargs) in framework_estimator_classes_to_kwargs.items():
+    for framework_estimator_class, kwargs in framework_estimator_classes_to_kwargs.items():
         estimator = framework_estimator_class(
             entry_point=ENTRY_POINT,
             role=ROLE,