update

README.md

@@ -238,9 +238,9 @@ Current behavior:
 6. The MLflow alias `experiment-latest` points at the most recently registered experiment version.
 7. AI Hub upload commands create deployable derived artifacts from a trained-source experiment or local ONNX model.
 
-Training scripts can include a `training_metrics.json` file in the SageMaker model directory. The explicit metrics
-upload command logs its ordered metrics to the associated MLflow run using each epoch as the MLflow step and stores
-the JSON as a run artifact:
+Training scripts can include a `training_metrics.json` file in the SageMaker model directory. When present, the
+explicit metrics upload command logs its ordered metrics to the associated MLflow run using each epoch as the MLflow
+step and stores the JSON as a run artifact:
 
 ```json
 {
@@ -253,8 +253,9 @@ the JSON as a run artifact:
 ```
 
 Metric names must be non-empty strings, values must be finite numbers, and steps must be non-negative, unique, and
-strictly increasing. A missing or malformed metrics artifact fails the upload command without affecting the trained
-model or model registration.
+strictly increasing. If the file is missing, the command uploads the final metrics reported by SageMaker and continues
+model registration without per-epoch history. A malformed metrics artifact still fails the upload command without
+affecting the trained model or model registration.
 
 Future release aliases such as `v1` or `production` can point at a selected deployable artifact.
 

src/commands/mlflow.py

@@ -80,7 +80,13 @@ def upload_metrics(
         CONSOLE.print(f"[red]MLflow metric upload failed: {e}[/red]")
         raise typer.Exit(1)
 
+    if result.metrics_history_uploaded:
         CONSOLE.print(f"[green]✓[/green] Uploaded training metrics for [cyan]{job_name}[/cyan].")
+    else:
+        CONSOLE.print(
+            f"[yellow]No training_metrics.json was found in the SageMaker model artifact for "
+            f"[cyan]{job_name}[/cyan]. Uploaded SageMaker final metrics only.[/yellow]"
+        )
     CONSOLE.print(f"MLflow run: [cyan]{result.run_id}[/cyan]")
     if result.registered_model_version:
         CONSOLE.print(

src/commands/train.py

@@ -93,10 +93,16 @@ def _wait_and_upload_metrics(
                         config_path=config_path,
                         cfg=cfg,
                     )
+                    if result.metrics_history_uploaded:
                         CONSOLE.print(
                             f"[green]✓[/green] Uploaded training metrics to MLflow run "
                             f"[cyan]{result.run_id}[/cyan]."
                         )
+                    else:
+                        CONSOLE.print(
+                            "[yellow]No training_metrics.json was found in the SageMaker model artifact. "
+                            "Uploaded SageMaker final metrics only.[/yellow]"
+                        )
                     if result.registered_model_version:
                         CONSOLE.print(
                             f"MLflow model version: [cyan]{result.registered_model_version}[/cyan] "

src/tracking/mlflow.py

@@ -40,7 +40,7 @@ class Tracker(Protocol):
         training_job_status: Any,
         region: str,
         profile: str,
-    ) -> None: ...
+    ) -> bool: ...
 
 
 @dataclass(frozen=True)
@@ -69,7 +69,7 @@ class NoopTracker:
         training_job_status: Any,
         region: str,
         profile: str,
-    ) -> None:
+    ) -> bool:
         raise RuntimeError("MLflow is disabled.")
 
 
@@ -208,7 +208,7 @@ class MlflowTracker:
         training_job_status: Any,
         region: str,
         profile: str,
-    ) -> None:
+    ) -> bool:
         if not training_job_status.model_artifacts:
             raise ValueError(f"Training job '{training_job_status.name}' has no model artifacts.")
 
@@ -216,12 +216,14 @@ class MlflowTracker:
             with mlflow.start_run(run_id=run_id):
                 self._log_params(self.tracking_backend.training_status_params(training_job_status))
                 self._log_final_metrics(training_job_status.raw)
-                self._log_training_metrics(
+                history_uploaded = self._log_training_metrics(
                     training_job_status.model_artifacts,
                     region=region,
                     profile=profile,
                 )
                 mlflow.set_tag("qc_cli.command", "mlflow upload-metrics")
+                mlflow.set_tag("qc_cli.metrics_history_uploaded", str(history_uploaded).lower())
+                return history_uploaded
 
     def _log_params(self, params: dict[str, Any]) -> None:
         cleaned = {key: str(value) for key, value in params.items() if value is not None}
@@ -238,7 +240,7 @@ class MlflowTracker:
         if metrics:
             mlflow.log_metrics(metrics)
 
-    def _log_training_metrics(self, model_artifacts: str, *, region: str, profile: str) -> None:
+    def _log_training_metrics(self, model_artifacts: str, *, region: str, profile: str) -> bool:
         with tempfile.TemporaryDirectory(prefix="qc-cli-metrics-") as temp_dir:
             archive_path = s3.download_file(
                 region,
@@ -248,7 +250,7 @@ class MlflowTracker:
             )
             metrics_data = read_training_metrics_from_tar(archive_path)
             if metrics_data is None:
-                raise ValueError(f"No {METRICS_ARTIFACT_NAME} found in the SageMaker model artifact.")
+                return False
             metrics = parse_training_metrics(metrics_data)
             for metric_step in metrics.steps:
                 if metric_step.metrics:
@@ -256,6 +258,7 @@ class MlflowTracker:
             if metrics.summary:
                 mlflow.log_metrics(metrics.summary)
             mlflow.log_dict(metrics.raw, METRICS_ARTIFACT_NAME)
+            return True
 
     def _ensure_registered_model(self, client: MlflowClient, name: str) -> None:
         try:

src/tracking/upload.py

@@ -10,6 +10,7 @@ from src.tracking.mlflow import MlflowTracker
 class MetricsUploadResult:
     run_id: str
     registered_model_version: str | None = None
+    metrics_history_uploaded: bool = True
 
 
 def upload_training_metrics(
@@ -32,6 +33,7 @@ def upload_training_metrics(
                 if job_state.get("registered_model_version")
                 else None
             ),
+            metrics_history_uploaded=bool(job_state.get("mlflow_metrics_history_uploaded", True)),
         )
 
     status = sm_ops.get_training_job_status(cfg.aws.boto3_session, job_name)
@@ -43,7 +45,7 @@ def upload_training_metrics(
     tracker = MlflowTracker.from_config(cfg)
     run_id = str(job_state.get("mlflow_run_id") or tracker.ensure_training_run(job_name))
     st.update_training_job(job_name, mlflow_run_id=run_id)
-    tracker.upload_training_metrics(
+    metrics_history_uploaded = tracker.upload_training_metrics(
         run_id=run_id,
         training_job_status=status,
         region=cfg.aws.region,
@@ -58,6 +60,7 @@ def upload_training_metrics(
     )
     updates = {
         "mlflow_metrics_uploaded": True,
+        "mlflow_metrics_history_uploaded": metrics_history_uploaded,
         "mlflow_finalized_status": status.status,
     }
     if finalized.registered_model_version:
@@ -68,4 +71,5 @@ def upload_training_metrics(
     return MetricsUploadResult(
         run_id=run_id,
         registered_model_version=finalized.registered_model_version,
+        metrics_history_uploaded=metrics_history_uploaded,
     )