Mlflow implementation (#2)

Reviewed-on: #2

src/aws/mlflow.py

@@ -17,3 +17,20 @@ def describe_tracking_server(region: str, profile: str, name: str) -> dict[str,
         ):
             return None
         raise
+
+
+def get_tracking_server_arn(region: str, profile: str, name: str) -> str:
+    server = describe_tracking_server(region, profile, name)
+    if not server:
+        raise ValueError(f"MLflow tracking server not found: {name}")
+
+    arn = server.get("TrackingServerArn")
+    if not arn:
+        raise ValueError(f"MLflow tracking server has no ARN: {name}")
+    return str(arn)
+
+
+def create_presigned_tracking_server_url(region: str, profile: str, name: str) -> str:
+    client = boto3.Session(profile_name=profile, region_name=region).client("sagemaker")
+    response = client.create_presigned_mlflow_tracking_server_url(TrackingServerName=name)
+    return str(response["AuthorizedUrl"])

src/aws/sagemaker.py

@@ -36,6 +36,7 @@ class TrainingJobStatus:
     modified: datetime | None
     model_artifacts: str | None
     failure_reason: str | None
+    raw: dict[str, Any] = field(default_factory=dict)
 
 
 def _sm(session: Boto3SessionKwargs) -> SageMakerClient:
@@ -116,6 +117,7 @@ def get_training_job_status(session: Boto3SessionKwargs, job_name: str) -> Train
         modified=resp.get("LastModifiedTime"),
         model_artifacts=resp.get("ModelArtifacts", {}).get("S3ModelArtifacts"),
         failure_reason=resp.get("FailureReason"),
+        raw=dict(resp),
     )