style: apply ruff format to all files

mlflow_utils/tracker.py

@@ -23,6 +23,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class PipelineMetadata:
     """Metadata about the Kubeflow Pipeline run."""
+
     pipeline_name: str
     run_id: str
     run_name: Optional[str] = None
@@ -30,12 +31,8 @@ class PipelineMetadata:
     namespace: str = "ai-ml"
 
     # KFP-specific metadata (populated from environment if available)
-    kfp_run_id: Optional[str] = field(
-        default_factory=lambda: os.environ.get("KFP_RUN_ID")
-    )
-    kfp_pod_name: Optional[str] = field(
-        default_factory=lambda: os.environ.get("KFP_POD_NAME")
-    )
+    kfp_run_id: Optional[str] = field(default_factory=lambda: os.environ.get("KFP_RUN_ID"))
+    kfp_pod_name: Optional[str] = field(default_factory=lambda: os.environ.get("KFP_POD_NAME"))
 
     def as_tags(self) -> Dict[str, str]:
         """Convert metadata to MLflow tags."""
@@ -142,10 +139,7 @@ class MLflowTracker:
         Yields:
             The MLflow run object
         """
-        self.client = get_mlflow_client(
-            tracking_uri=self.tracking_uri,
-            configure_global=True
-        )
+        self.client = get_mlflow_client(tracking_uri=self.tracking_uri, configure_global=True)
 
         # Ensure experiment exists
         experiment_id = ensure_experiment(self.experiment_name)
@@ -163,8 +157,7 @@ class MLflowTracker:
             self.run_id = self.run.info.run_id
 
             logger.info(
-                f"Started MLflow run '{self.run_name}' "
-                f"(ID: {self.run_id}) in experiment '{self.experiment_name}'"
+                f"Started MLflow run '{self.run_name}' (ID: {self.run_id}) in experiment '{self.experiment_name}'"
             )
 
             yield self.run
@@ -214,11 +207,7 @@ class MLflowTracker:
         """Log a single parameter."""
         self.log_params({key: value})
 
-    def log_metrics(
-        self,
-        metrics: Dict[str, Union[float, int]],
-        step: Optional[int] = None
-    ) -> None:
+    def log_metrics(self, metrics: Dict[str, Union[float, int]], step: Optional[int] = None) -> None:
         """
         Log metrics to the current run.
 
@@ -233,20 +222,11 @@ class MLflowTracker:
         mlflow.log_metrics(metrics, step=step)
         logger.debug(f"Logged {len(metrics)} metrics")
 
-    def log_metric(
-        self,
-        key: str,
-        value: Union[float, int],
-        step: Optional[int] = None
-    ) -> None:
+    def log_metric(self, key: str, value: Union[float, int], step: Optional[int] = None) -> None:
         """Log a single metric."""
         self.log_metrics({key: value}, step=step)
 
-    def log_artifact(
-        self,
-        local_path: str,
-        artifact_path: Optional[str] = None
-    ) -> None:
+    def log_artifact(self, local_path: str, artifact_path: Optional[str] = None) -> None:
         """
         Log an artifact file to the current run.
 
@@ -261,11 +241,7 @@ class MLflowTracker:
         mlflow.log_artifact(local_path, artifact_path)
         logger.info(f"Logged artifact: {local_path}")
 
-    def log_artifacts(
-        self,
-        local_dir: str,
-        artifact_path: Optional[str] = None
-    ) -> None:
+    def log_artifacts(self, local_dir: str, artifact_path: Optional[str] = None) -> None:
         """
         Log all files in a directory as artifacts.
 
@@ -280,12 +256,7 @@ class MLflowTracker:
         mlflow.log_artifacts(local_dir, artifact_path)
         logger.info(f"Logged artifacts from: {local_dir}")
 
-    def log_dict(
-        self,
-        data: Dict[str, Any],
-        filename: str,
-        artifact_path: Optional[str] = None
-    ) -> None:
+    def log_dict(self, data: Dict[str, Any], filename: str, artifact_path: Optional[str] = None) -> None:
         """
         Log a dictionary as a JSON artifact.
 
@@ -311,7 +282,7 @@ class MLflowTracker:
         model_name: str,
         model_path: Optional[str] = None,
         framework: str = "pytorch",
-        extra_info: Optional[Dict[str, Any]] = None
+        extra_info: Optional[Dict[str, Any]] = None,
     ) -> None:
         """
         Log model information as parameters and tags.
@@ -341,11 +312,7 @@ class MLflowTracker:
         mlflow.set_tag("model.name", model_name)
 
     def log_dataset_info(
-        self,
-        name: str,
-        source: str,
-        size: Optional[int] = None,
-        extra_info: Optional[Dict[str, Any]] = None
+        self, name: str, source: str, size: Optional[int] = None, extra_info: Optional[Dict[str, Any]] = None
     ) -> None:
         """
         Log dataset information.