Generate unique SageMaker training job name based on pipeline and ste…

examples/airflow_orchestration/steps/importers.py

@@ -20,9 +20,11 @@
 
 
 @step
-def importer_mnist() -> Output(
-    train_dataloader=DataLoader,
-    test_dataloader=DataLoader,
+def importer_mnist() -> (
+    Output(
+        train_dataloader=DataLoader,
+        test_dataloader=DataLoader,
+    )
 ):
     """Download the Fashion MNIST dataset."""
     # Download training data from open datasets.

examples/bentoml_deployment/steps/importers.py

@@ -20,9 +20,11 @@
 
 
 @step
-def importer_mnist() -> Output(
-    train_dataloader=DataLoader,
-    test_dataloader=DataLoader,
+def importer_mnist() -> (
+    Output(
+        train_dataloader=DataLoader,
+        test_dataloader=DataLoader,
+    )
 ):
     """Download the Fashion MNIST dataset."""
     # Download training data from open datasets.

examples/custom_code_deployment/kserve_tensorflow/steps/tf_data_loader.py

@@ -19,11 +19,13 @@
 
 
 @step
-def tf_data_loader() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def tf_data_loader() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (x_train, y_train), (

examples/custom_code_deployment/seldon_tensorflow/steps/tf_data_loader.py

@@ -19,11 +19,13 @@
 
 
 @step
-def tf_data_loader() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def tf_data_loader() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (x_train, y_train), (

examples/deepchecks_data_validation/steps/data_loader.py

@@ -19,8 +19,8 @@
 
 
 @step
-def data_loader() -> Output(
-    reference_dataset=pd.DataFrame, comparison_dataset=pd.DataFrame
+def data_loader() -> (
+    Output(reference_dataset=pd.DataFrame, comparison_dataset=pd.DataFrame)
 ):
     """Load the iris dataset."""
     iris_df = iris.load_data(data_format="Dataframe", as_train_test=False)

examples/kserve_deployment/steps/pytorch_steps/mnist_handler.py

@@ -19,7 +19,8 @@ class MNISTDigitClassifier(ImageClassifier):
     """
     MNISTDigitClassifier handler class. This handler extends class ImageClassifier from image_classifier.py, a
     default handler. This handler takes an image and returns the number in that image.
-    Here the post-processing method has been overridden while others are reused from parent class."""
+    Here the post-processing method has been overridden while others are reused from parent class.
+    """
 
     image_processing = transforms.Compose(
         [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]

examples/kserve_deployment/steps/tensorflow_steps/mnist_importer.py

@@ -18,11 +18,13 @@
 
 
 @step
-def importer_mnist() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def importer_mnist() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data and store it as an artifact."""
     (x_train, y_train), (

examples/kubeflow_pipelines_orchestration/steps/importers.py

@@ -20,11 +20,13 @@
 
 
 @step
-def importer() -> Output(
-    X_train=np.ndarray,
-    X_test=np.ndarray,
-    y_train=np.ndarray,
-    y_test=np.ndarray,
+def importer() -> (
+    Output(
+        X_train=np.ndarray,
+        X_test=np.ndarray,
+        y_train=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (X_train, y_train), (

examples/kubernetes_orchestration/steps/importer.py

@@ -20,11 +20,13 @@
 
 
 @step
-def digits_data_loader() -> Output(
-    X_train=np.ndarray,
-    X_test=np.ndarray,
-    y_train=np.ndarray,
-    y_test=np.ndarray,
+def digits_data_loader() -> (
+    Output(
+        X_train=np.ndarray,
+        X_test=np.ndarray,
+        y_train=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Loads the digits dataset as a tuple of flattened numpy arrays."""
     digits = load_digits()

examples/mlflow_tracking/steps/loader/loader_step.py

@@ -18,11 +18,13 @@
 
 
 @step
-def loader_mnist() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def loader_mnist() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (x_train, y_train), (

examples/neptune_tracking/steps/loader/loader_step.py

@@ -19,11 +19,13 @@
 
 
 @step
-def loader_mnist() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def loader_mnist() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (x_train, y_train), (

examples/pytorch/steps/importers.py

@@ -20,9 +20,11 @@
 
 
 @step
-def importer_mnist() -> Output(
-    train_dataloader=DataLoader,
-    test_dataloader=DataLoader,
+def importer_mnist() -> (
+    Output(
+        train_dataloader=DataLoader,
+        test_dataloader=DataLoader,
+    )
 ):
     """Download the Fashion MNIST dataset."""
     # Download training data from open datasets.

examples/quickstart/run.py

@@ -39,7 +39,6 @@
 
 
 def main():
-
     # initialize and run the training pipeline
     training_pipeline_instance = training_pipeline(
         training_data_loader=training_data_loader(),

examples/quickstart/steps/data_loaders.py

@@ -21,11 +21,13 @@
 
 
 @step
-def training_data_loader() -> Output(
-    X_train=pd.DataFrame,
-    X_test=pd.DataFrame,
-    y_train=pd.Series,
-    y_test=pd.Series,
+def training_data_loader() -> (
+    Output(
+        X_train=pd.DataFrame,
+        X_test=pd.DataFrame,
+        y_train=pd.Series,
+        y_test=pd.Series,
+    )
 ):
     """Load the iris dataset as tuple of Pandas DataFrame / Series."""
     iris = load_iris(as_frame=True)

examples/quickstart_py37/run.py

@@ -32,7 +32,6 @@
 
 
 def main():
-
     # initialize and run the training pipeline
     training_pipeline_instance = training_pipeline(
         training_data_loader=training_data_loader(),

examples/quickstart_py37/steps/data_loaders.py

@@ -21,11 +21,13 @@
 
 
 @step
-def training_data_loader() -> Output(
-    X_train=pd.DataFrame,
-    X_test=pd.DataFrame,
-    y_train=pd.Series,
-    y_test=pd.Series,
+def training_data_loader() -> (
+    Output(
+        X_train=pd.DataFrame,
+        X_test=pd.DataFrame,
+        y_train=pd.Series,
+        y_test=pd.Series,
+    )
 ):
     """Load the iris dataset as tuple of Pandas DataFrame / Series."""
     iris = load_iris(as_frame=True)

examples/sagemaker_orchestration/steps/importers.py

@@ -20,9 +20,11 @@
 
 
 @step
-def importer_mnist() -> Output(
-    train_dataloader=DataLoader,
-    test_dataloader=DataLoader,
+def importer_mnist() -> (
+    Output(
+        train_dataloader=DataLoader,
+        test_dataloader=DataLoader,
+    )
 ):
     """Download the Fashion MNIST dataset."""
     # Download training data from open datasets.

examples/scipy/steps/loader/loader_step.py

@@ -28,11 +28,13 @@ def clean_text(text: str):
 
 
 @step
-def importer() -> Output(
-    X_train=np.ndarray,
-    X_test=np.ndarray,
-    y_train=np.ndarray,
-    y_test=np.ndarray,
+def importer() -> (
+    Output(
+        X_train=np.ndarray,
+        X_test=np.ndarray,
+        y_train=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     train = pd.read_csv(TRAIN_PATH)
     test = pd.read_csv(TEST_PATH)

examples/seldon_deployment/pipeline.py

@@ -35,11 +35,13 @@
 
 
 @step
-def importer_mnist() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def importer_mnist() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (x_train, y_train), (

examples/slack_alert/steps/data_loader.py

@@ -20,11 +20,13 @@
 
 
 @step
-def digits_data_loader() -> Output(
-    X_train=np.ndarray,
-    X_test=np.ndarray,
-    y_train=np.ndarray,
-    y_test=np.ndarray,
+def digits_data_loader() -> (
+    Output(
+        X_train=np.ndarray,
+        X_test=np.ndarray,
+        y_train=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Loads the digits dataset as a tuple of flattened numpy arrays."""
     digits = load_digits()

examples/step_operator_remote_training/steps/importer/importer_step.py

@@ -19,11 +19,13 @@
 
 
 @step
-def digits_data_loader() -> Output(
-    X_train=np.ndarray,
-    X_test=np.ndarray,
-    y_train=np.ndarray,
-    y_test=np.ndarray,
+def digits_data_loader() -> (
+    Output(
+        X_train=np.ndarray,
+        X_test=np.ndarray,
+        y_train=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Loads the digits dataset as a tuple of flattened numpy arrays."""
     digits = load_digits()

examples/tekton_pipelines_orchestration/steps/importers.py

@@ -20,11 +20,13 @@
 
 
 @step
-def importer() -> Output(
-    X_train=np.ndarray,
-    X_test=np.ndarray,
-    y_train=np.ndarray,
-    y_test=np.ndarray,
+def importer() -> (
+    Output(
+        X_train=np.ndarray,
+        X_test=np.ndarray,
+        y_train=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (X_train, y_train), (

examples/wandb_tracking/run.py

@@ -21,7 +21,6 @@
 from steps.trainer.trainer_step import TrainerParameters, tf_trainer
 
 if __name__ == "__main__":
-
     # Initialize a pipeline run
     run_1 = wandb_example_pipeline(
         importer=importer_mnist(),

examples/wandb_tracking/steps/importer/importer_step.py

@@ -18,11 +18,13 @@
 
 
 @step
-def importer_mnist() -> Output(
-    x_train=np.ndarray,
-    y_train=np.ndarray,
-    x_test=np.ndarray,
-    y_test=np.ndarray,
+def importer_mnist() -> (
+    Output(
+        x_train=np.ndarray,
+        y_train=np.ndarray,
+        x_test=np.ndarray,
+        y_test=np.ndarray,
+    )
 ):
     """Download the MNIST data store it as an artifact."""
     (x_train, y_train), (

examples/whylogs_data_profiling/run.py

@@ -54,7 +54,6 @@ def visualize_statistics(
 
 
 if __name__ == "__main__":
-
     pipeline_instance = data_profiling_pipeline(
         data_loader=data_loader(),
         data_splitter=data_splitter(),