Merge branch 'feature/add-sagemaker-schedule' of github.com:zenml-io/…

…zenml into feature/add-sagemaker-schedule

examples/e2e/steps/deployment/deployment_deploy.py

@@ -34,12 +34,10 @@
 
 
 @step
-def deployment_deploy() -> (
-    Annotated[
-        Optional[MLFlowDeploymentService],
-        ArtifactConfig(name="mlflow_deployment", is_deployment_artifact=True),
-    ]
-):
+def deployment_deploy() -> Annotated[
+    Optional[MLFlowDeploymentService],
+    ArtifactConfig(name="mlflow_deployment", is_deployment_artifact=True),
+]:
     """Predictions step.
 
     This is an example of a predictions step that takes the data in and returns

examples/e2e/steps/training/model_evaluator.py

@@ -82,22 +82,22 @@ def model_evaluator(
         dataset_trn.drop(columns=[target]),
         dataset_trn[target],
     )
-    logger.info(f"Train accuracy={trn_acc*100:.2f}%")
+    logger.info(f"Train accuracy={trn_acc * 100:.2f}%")
     tst_acc = model.score(
         dataset_tst.drop(columns=[target]),
         dataset_tst[target],
     )
-    logger.info(f"Test accuracy={tst_acc*100:.2f}%")
+    logger.info(f"Test accuracy={tst_acc * 100:.2f}%")
     mlflow.log_metric("testing_accuracy_score", tst_acc)
 
     messages = []
     if trn_acc < min_train_accuracy:
         messages.append(
-            f"Train accuracy {trn_acc*100:.2f}% is below {min_train_accuracy*100:.2f}% !"
+            f"Train accuracy {trn_acc * 100:.2f}% is below {min_train_accuracy * 100:.2f}% !"
         )
     if tst_acc < min_test_accuracy:
         messages.append(
-            f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}% !"
+            f"Test accuracy {tst_acc * 100:.2f}% is below {min_test_accuracy * 100:.2f}% !"
         )
     if fail_on_accuracy_quality_gates and messages:
         raise RuntimeError(

examples/e2e_nlp/steps/deploying/huggingface_deployment.py

@@ -39,7 +39,9 @@ def deploy_to_huggingface(
     """
     ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###
     secret = Client().get_secret("huggingface_creds")
-    assert secret, "No secret found with name 'huggingface_creds'. Please create one that includes your `username` and `token`."
+    assert secret, (
+        "No secret found with name 'huggingface_creds'. Please create one that includes your `username` and `token`."
+    )
     token = secret.secret_values["token"]
     api = HfApi(token=token)
     hf_repo = api.create_repo(

examples/e2e_nlp/steps/promotion/promote_get_metrics.py

@@ -30,12 +30,10 @@
 
 
 @step
-def promote_get_metrics() -> (
-    Tuple[
-        Annotated[Dict[str, Any], "latest_metrics"],
-        Annotated[Dict[str, Any], "current_metrics`"],
-    ]
-):
+def promote_get_metrics() -> Tuple[
+    Annotated[Dict[str, Any], "latest_metrics"],
+    Annotated[Dict[str, Any], "current_metrics`"],
+]:
     """Get metrics for comparison for promoting a model.
 
     This is an example of a metric retrieval step. It is used to retrieve

examples/llm_finetuning/steps/promote.py

@@ -54,11 +54,11 @@ def promote(
 
     msg = (
         f"`{metric}` values to compare:\n"
-        f"base={base_metrics[metric]*100:.2f}%\n"
-        f"finetuned={ft_metrics[metric]*100:.2f}%"
+        f"base={base_metrics[metric] * 100:.2f}%\n"
+        f"finetuned={ft_metrics[metric] * 100:.2f}%"
     )
     if staging_metrics:
-        msg += f"\nstaging={staging_metrics[metric]*100:.2f}%"
+        msg += f"\nstaging={staging_metrics[metric] * 100:.2f}%"
     logger.info(msg)
 
     if base_metrics[metric] <= ft_metrics[metric]:

examples/mlops_starter/steps/model_promoter.py

@@ -44,7 +44,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool:
 
     if accuracy < 0.8:
         logger.info(
-            f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model."
+            f"Model accuracy {accuracy * 100:.2f}% is below 80% ! Not promoting model."
         )
     else:
         logger.info(f"Model promoted to {stage}!")