command to create presigned URL for MLFlow

README.md

@@ -78,12 +78,13 @@ To provision an MLflow tracking server, set:
 ```yaml
 mlflow:
   mode: create
-  tracking_server_name: your-tracking-server-name
   experiment_name: qc-cli-training
   registered_model_name: qc-cli-model
   register_trained_models: true
 ```
 
+In `create` mode, the CLI manages the tracking server name from `infra.stack_name`; you do not need to set `tracking_server_name`.
+
 To use an existing MLflow tracking server, set:
 
 ```yaml
@@ -100,6 +101,14 @@ uv sync --extra mlflow
 
 When MLflow is enabled, `train start` creates an MLflow run for the SageMaker job. `train status` finalizes that run once the job reaches a terminal state and registers completed model artifacts as pre-release model versions using the `prerelease-latest` MLflow alias.
 
+To open the managed SageMaker MLflow UI, request a fresh presigned URL:
+
+```bash
+qc-cli infra mlflow-url --config config.yaml
+```
+
+This works for `mode: create` and for `mode: existing` when the existing server is managed by Amazon SageMaker. In `create` mode, the command uses the CLI-managed tracking server name. In `existing` mode, it uses `mlflow.tracking_server_name`. If the existing MLflow server is external to SageMaker, open it with that server's own URL instead.
+
 ## Commands
 
 ### `init`
@@ -117,6 +126,7 @@ qc-cli infra setup                         Deploy the CDK stack
 qc-cli infra setup --no-bootstrap          Deploy without running CDK bootstrap
 qc-cli infra setup --cloudformation-execution-policy <arn> Set CDK bootstrap execution policy ARN
 qc-cli infra status                        Show CDK stack/resource status
+qc-cli infra mlflow-url                    Print a presigned MLflow UI URL
 qc-cli infra destroy                       Destroy stack, retaining S3 data
 qc-cli infra destroy --yes                 Destroy stack without confirmation
 qc-cli infra destroy --delete-bucket-data  Destroy stack and delete S3 data

examples/training/run_training.sh

@@ -72,10 +72,11 @@ if [[ "${SKIP_UPLOAD}" == false ]]; then
   run uv run qc-cli upload "${DATASET_DIR}" --config "${CONFIG_PATH}"
 fi
 
-TRAIN_OUTPUT="$(uv run qc-cli train start --config "${CONFIG_PATH}")"
-echo "${TRAIN_OUTPUT}"
+TRAIN_OUTPUT_FILE="$(mktemp)"
+trap 'rm -f "${TRAIN_OUTPUT_FILE}"' EXIT
+run uv run qc-cli train start --config "${CONFIG_PATH}" | tee "${TRAIN_OUTPUT_FILE}"
 
-JOB_NAME="$(printf '%s\n' "${TRAIN_OUTPUT}" | grep -Eo 'qc-cli-[0-9]{8}-[0-9]{6}' | tail -n 1)"
+JOB_NAME="$(grep -Eo 'qc-cli-[0-9]{8}-[0-9]{6}' "${TRAIN_OUTPUT_FILE}" | tail -n 1)"
 if [[ -z "${JOB_NAME}" ]]; then
   echo "Could not find training job name in qc-cli output." >&2
   exit 1

src/aws/mlflow.py

@@ -28,3 +28,9 @@ def get_tracking_server_arn(region: str, profile: str, name: str) -> str:
     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/commands/infra.py

@@ -77,7 +77,8 @@ def setup(
     if outputs.get("SageMakerRoleArn"):
         CONSOLE.print(f"[green]✓[/green] IAM role:  {outputs['SageMakerRoleArn']}")
     if cfg.mlflow.mode is MlflowMode.create and outputs.get("MlflowTrackingServerArn"):
-        CONSOLE.print(f"[green]✓[/green] MLflow:    {outputs['MlflowTrackingServerArn']}")
+        mlflow_name = outputs.get("MlflowTrackingServerName", cfg.managed_mlflow_tracking_server_name)
+        CONSOLE.print(f"[green]✓[/green] MLflow:    {mlflow_name}")
     elif cfg.mlflow.mode is MlflowMode.existing:
         CONSOLE.print(f"[green]✓[/green] MLflow:    {cfg.mlflow.tracking_server_name}")
     CONSOLE.print("\n[bold green]Infrastructure ready.[/bold green]")
@@ -102,7 +103,7 @@ def status(config: str = CONFIG_OPT) -> None:
         if cfg.mlflow.mode is not MlflowMode.disabled:
             table.add_row(
                 "MLflow",
-                cfg.mlflow.tracking_server_name or "-",
+                cfg.effective_mlflow_tracking_server_name or "-",
                 "[red]unknown[/red]",
                 "-",
             )
@@ -126,7 +127,7 @@ def status(config: str = CONFIG_OPT) -> None:
     if cfg.mlflow.mode is MlflowMode.create:
         table.add_row(
             "MLflow",
-            cfg.mlflow.tracking_server_name or "-",
+            outputs.get("MlflowTrackingServerName", cfg.managed_mlflow_tracking_server_name),
             "[green]managed[/green]",
             outputs.get("MlflowTrackingServerArn", outputs.get("MlflowArtifactUri", "-")),
         )
@@ -149,6 +150,32 @@ def status(config: str = CONFIG_OPT) -> None:
     CONSOLE.print(table)
 
 
+@app.command(name="mlflow-url")
+def mlflow_url(config: str = CONFIG_OPT) -> None:
+    """Print a presigned URL for the configured MLflow tracking server."""
+    cfg = load_cfg(config)
+    tracking_server_name = _mlflow_tracking_server_name(cfg)
+
+    try:
+        url = mlflow.create_presigned_tracking_server_url(
+            cfg.aws.region,
+            cfg.aws.profile,
+            tracking_server_name,
+        )
+    except Exception as e:
+        CONSOLE.print("[yellow]Could not create a SageMaker MLflow UI URL.[/yellow]")
+        CONSOLE.print(f"Tracking server: [cyan]{tracking_server_name}[/cyan]")
+        CONSOLE.print(f"Reason: {e}")
+        CONSOLE.print(
+            "This command can create presigned URLs only for MLflow tracking servers managed by "
+            "Amazon SageMaker. If this is an external MLflow server, open it with that server's own URL."
+        )
+        raise typer.Exit(1)
+
+    CONSOLE.print(f"MLflow tracking server: [cyan]{tracking_server_name}[/cyan]")
+    CONSOLE.print(f"MLflow UI: {url}")
+
+
 @app.command()
 def destroy(
     config: str = CONFIG_OPT,
@@ -209,6 +236,15 @@ def _role_name(configured_name: str, role_arn: str) -> str:
         return role_arn.rsplit("/", 1)[-1]
     return "-"
 
+
+def _mlflow_tracking_server_name(cfg: Config) -> str:
+    name = cfg.effective_mlflow_tracking_server_name
+    if not name:
+        CONSOLE.print("[red]MLflow is disabled in config.yaml.[/red]")
+        raise typer.Exit(1)
+    return name
+
+
 def _destroy_account_id(config_path: str, cfg: Config) -> str:
     config_dir = str(Path(config_path).parent)
     state = read_infra_state(config_dir)

src/commands/train.py

@@ -8,7 +8,7 @@ from src import state as state_ops
 from src.aws import iam
 from src.aws import sagemaker as sm_ops
 from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
-from src.config import Config
+from src.config import Config, MlflowMode
 from src.infra.state import read_infra_state
 from src.tracking.mlflow import MlflowTracker
 
@@ -101,6 +101,7 @@ def start(config: str = CONFIG_OPT) -> None:
     CONSOLE.print(f"[green]✓[/green] Job submitted: [bold]{job_name}[/bold]")
     if run_id:
         CONSOLE.print(f"MLflow run: [cyan]{run_id}[/cyan]")
+        CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/cyan]")
     CONSOLE.print("Track progress: [cyan]qc-cli train status[/cyan]")
 
 
@@ -137,7 +138,8 @@ def status(
     run_id = job_state.get("mlflow_run_id")
     already_registered = job_state.get("registered_model_version")
     if run_id and not already_registered and status.status in {"Completed", "Failed", "Stopped"}:
-        version = _tracker(cfg).finalize_training_run(
+        tracker = _tracker(cfg)
+        version = tracker.finalize_training_run(
             run_id=str(run_id),
             training_job_status=status,
         )
@@ -148,6 +150,8 @@ def status(
         if version:
             st.set_latest_prerelease_model_version(version)
             CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]prerelease-latest[/cyan])")
+    if run_id and cfg.mlflow.mode is not MlflowMode.disabled:
+        CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/cyan]")
 
 
 @app.command(name="list")

src/config.py

@@ -1,5 +1,5 @@
 import re
-from enum import Enum
+from enum import StrEnum
 from typing import Any, Literal, TypedDict
 
 from mypy_boto3_s3.literals import BucketLocationConstraintType
@@ -7,13 +7,13 @@ from mypy_boto3_sagemaker.literals import TrainingInstanceTypeType
 from pydantic import BaseModel, Field, model_validator
 
 
-class MlflowMode(str, Enum):
+class MlflowMode(StrEnum):
     disabled = "disabled"
     create = "create"
     existing = "existing"
 
 
-class MlflowServerSize(str, Enum):
+class MlflowServerSize(StrEnum):
     small = "Small"
     medium = "Medium"
     large = "Large"
@@ -94,8 +94,8 @@ class MlflowConfig(BaseModel):
 
     @model_validator(mode="after")
     def require_tracking_server_name(self) -> "MlflowConfig":
-        if self.mode in {MlflowMode.create, MlflowMode.existing} and not self.tracking_server_name:
-            raise ValueError("mlflow.tracking_server_name is required when mlflow.mode is create or existing")
+        if self.mode is MlflowMode.existing and not self.tracking_server_name:
+            raise ValueError("mlflow.tracking_server_name is required when mlflow.mode is existing")
         return self
 
 
@@ -105,3 +105,15 @@ class Config(BaseModel):
     s3: S3Config = Field(default_factory=S3Config)
     sagemaker: SageMakerConfig = Field(default_factory=SageMakerConfig)
     mlflow: MlflowConfig = Field(default_factory=MlflowConfig)
+
+    @property
+    def managed_mlflow_tracking_server_name(self) -> str:
+        return f"{self.infra.stack_name}-mlflow"
+
+    @property
+    def effective_mlflow_tracking_server_name(self) -> str | None:
+        if self.mlflow.mode is MlflowMode.disabled:
+            return None
+        if self.mlflow.mode is MlflowMode.existing:
+            return self.mlflow.tracking_server_name
+        return self.managed_mlflow_tracking_server_name

src/infra/stack.py

@@ -74,6 +74,7 @@ class QCStack(Stack):
         CfnOutput(self, "SageMakerRoleArn", value=role.attr_arn)
 
         if config.mlflow.mode is MlflowMode.create:
+            tracking_server_name = config.managed_mlflow_tracking_server_name
             artifact_prefix = config.mlflow.artifact_prefix.strip("/")
             artifact_uri = (
                 f"s3://{data_bucket.bucket_name}/{artifact_prefix}/"
@@ -145,14 +146,14 @@ class QCStack(Stack):
                 "MlflowTrackingServer",
                 artifact_store_uri=artifact_uri,
                 role_arn=mlflow_role.attr_arn,
-                tracking_server_name=config.mlflow.tracking_server_name or "",
+                tracking_server_name=tracking_server_name,
                 automatic_model_registration=config.mlflow.automatic_model_registration,
                 mlflow_version=config.mlflow.mlflow_version,
                 tracking_server_size=config.mlflow.tracking_server_size.value,
                 weekly_maintenance_window_start=config.mlflow.weekly_maintenance_window_start,
             )
 
-            CfnOutput(self, "MlflowTrackingServerName", value=config.mlflow.tracking_server_name or "")
+            CfnOutput(self, "MlflowTrackingServerName", value=tracking_server_name)
             CfnOutput(self, "MlflowTrackingServerArn", value=tracking_server.attr_tracking_server_arn)
             CfnOutput(self, "MlflowArtifactUri", value=artifact_uri)
             CfnOutput(self, "MlflowRoleArn", value=mlflow_role.attr_arn)

src/tracking/mlflow.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import os
 from dataclasses import dataclass
 from typing import Any, Protocol
 
@@ -40,6 +41,8 @@ class MlflowTracker:
         if cfg.mlflow.mode is MlflowMode.disabled:
             return NoopTracker()
 
+        os.environ.setdefault("MLFLOW_SUPPRESS_PRINTING_URL_TO_STDOUT", "true")
+
         try:
             import mlflow
         except ImportError as e:
@@ -48,13 +51,14 @@ class MlflowTracker:
                 "Install with: qc-cli[mlflow]"
             ) from e
 
-        if not cfg.mlflow.tracking_server_name:
-            raise RuntimeError("mlflow.tracking_server_name is required when MLflow is enabled.")
+        tracking_server_name = cfg.effective_mlflow_tracking_server_name
+        if not tracking_server_name:
+            raise RuntimeError("MLflow tracking server name could not be resolved.")
 
         tracking_uri = aws_mlflow.get_tracking_server_arn(
             cfg.aws.region,
             cfg.aws.profile,
-            cfg.mlflow.tracking_server_name,
+            tracking_server_name,
         )
         mlflow.set_tracking_uri(tracking_uri)
         mlflow.set_experiment(cfg.mlflow.experiment_name)