command to create presigned URL for MLFlow

README.md

@@ -83,7 +83,7 @@ mlflow:
   register_trained_models: true
 ```
 
-In `create` mode, the CLI manages the tracking server name from `infra.stack_name`.
+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:
 
@@ -101,6 +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`
@@ -118,6 +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}")"
+TRAIN_OUTPUT_FILE="$(mktemp)"
-echo "${TRAIN_OUTPUT}"
+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

@@ -150,6 +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,
@@ -210,6 +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/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: