update

README.md

@@ -163,15 +163,15 @@ Uploads use `s3.bucket` and `s3.data_prefix` from `config.yaml`. File uploads de
 
 ```
 qc-cli train start              Submit a SageMaker training job
+qc-cli train start --wait       Submit, wait, and finalize MLflow tracking
 qc-cli train status [job-name]  Show job status; defaults to the last submitted job
-qc-cli train wait [job-name]    Wait for completion and finalize MLflow tracking
 qc-cli train list               List recent training jobs
 qc-cli train list --limit 3     Show a custom number of recent jobs
 ```
 
 `train start` uses `s3://<bucket>/<data_prefix>/` as the training channel and writes outputs under `s3://<bucket>/<model_prefix>/`. If `sagemaker.training.source_dir` is set, the CLI packages that directory, uploads it beside the job output prefix, and passes `sagemaker_program`/`sagemaker_submit_directory` to the SageMaker container.
 
-`train wait` checks SageMaker every 30 seconds by default. Use `--poll-interval <seconds>` to choose another positive interval. Stopping the local command does not stop the SageMaker job.
+`train start --wait` checks SageMaker every 30 seconds by default. Use `--poll-interval <seconds>` to choose another positive interval. Stopping the local command does not stop the SageMaker job.
 
 The expected output artifact is SageMaker’s `model.tar.gz`, normally containing the trained model file your container writes to `/opt/ml/model`.
 
@@ -219,7 +219,7 @@ The CLI uses neutral experiment naming for trained artifacts and reserves releas
 Current behavior:
 
 1. `qc-cli train start` submits a SageMaker training job.
-2. `qc-cli train status` or `qc-cli train wait` finalizes the MLflow run after the job reaches a terminal state. `train wait` blocks and polls every 30 seconds by default.
+2. `qc-cli train status` or `qc-cli train start --wait` finalizes the MLflow run after the job reaches a terminal state. `--wait` polls every 30 seconds by default.
 3. If the job completed and `mlflow.register_trained_models` is enabled, the SageMaker `model.tar.gz` is registered as a new MLflow model version with:
    - `qc_cli.stage=experiment`
    - `qc_cli.artifact_kind=trained_source`

examples/meter-detection/README.md

@@ -153,10 +153,10 @@ Or pass the job name explicitly:
 qc-cli train status qc-cli-YYYYMMDD-HHMMSS
 ```
 
-To wait for completion and automatically import metrics and register the model, run:
+To submit the job, wait for completion, and automatically import metrics and register the model, run:
 
 ```bash
-qc-cli train wait
+qc-cli train start --wait
 ```
 
 The default polling interval is 30 seconds. It can be changed with `--poll-interval <seconds>`.

src/commands/train.py

@@ -102,8 +102,54 @@ def _finalize_terminal_job(
         )
 
 
+def _wait_for_training_job(
+    *,
+    job_name: str,
+    poll_interval: int,
+    config_path: str,
+    cfg: Config,
+) -> None:
+    st = state_ops.store(config_path)
+    previous_status: str | None = None
+    try:
+        while True:
+            training_status = sm_ops.get_training_job_status(cfg.aws.boto3_session, job_name)
+            if training_status.status != previous_status:
+                color = _STATUS_COLOR.get(training_status.status, "white")
+                CONSOLE.print(
+                    f"Job [cyan]{training_status.name}[/cyan]: "
+                    f"[{color}]{training_status.status}[/{color}]"
+                )
+                previous_status = training_status.status
+            if training_status.status in _TERMINAL_STATUSES:
+                _print_training_status(training_status)
+                _finalize_terminal_job(
+                    config_path=config_path,
+                    cfg=cfg,
+                    status=training_status,
+                    command="train start --wait",
+                )
+                job_state = st.get_training_job(job_name)
+                if job_state.get("mlflow_run_id") and cfg.mlflow.mode is not MlflowMode.disabled:
+                    CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
+                return
+            time.sleep(poll_interval)
+    except KeyboardInterrupt:
+        CONSOLE.print("[yellow]Stopped waiting. The SageMaker training job is still running.[/yellow]")
+        raise typer.Exit(130)
+
+
 @app.command()
-def start(config: str = CONFIG_OPT) -> None:
+def start(
+    wait: bool = typer.Option(False, "--wait", help="Wait for completion and finalize MLflow tracking"),
+    poll_interval: int = typer.Option(
+        DEFAULT_POLL_INTERVAL_SECONDS,
+        "--poll-interval",
+        min=1,
+        help="Seconds between status checks when --wait is used",
+    ),
+    config: str = CONFIG_OPT,
+) -> None:
     """Submit a SageMaker training job."""
     cfg = load_cfg(config)
 
@@ -156,7 +202,15 @@ def start(config: str = CONFIG_OPT) -> None:
     if run_id:
         CONSOLE.print(f"MLflow run: [cyan]{run_id}[/cyan]")
         CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
-    CONSOLE.print("Track progress: [cyan]qc-cli train status[/cyan]")
+    if wait:
+        _wait_for_training_job(
+            job_name=job_name,
+            poll_interval=poll_interval,
+            config_path=config,
+            cfg=cfg,
+        )
+    else:
+        CONSOLE.print("Track progress: [cyan]qc-cli train status[/cyan]")
 
 
 @app.command()
@@ -185,57 +239,6 @@ def status(
         CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
 
 
-@app.command()
-def wait(
-    job_name: str | None = typer.Argument(None, help="Training job name (default: last submitted job)"),
-    poll_interval: int = typer.Option(
-        DEFAULT_POLL_INTERVAL_SECONDS,
-        "--poll-interval",
-        min=1,
-        help="Seconds between SageMaker status checks",
-    ),
-    config: str = CONFIG_OPT,
-) -> None:
-    """Wait for a training job and finalize its MLflow run."""
-    cfg = load_cfg(config)
-    st = state_ops.store(config)
-    if not job_name:
-        job_name = st.get_last_training_job()
-        if not job_name:
-            CONSOLE.print(
-                "[red]No training job found in state. Pass a job name or run 'qc-cli train start' first.[/red]"
-            )
-            raise typer.Exit(1)
-
-    previous_status: str | None = None
-    try:
-        while True:
-            training_status = sm_ops.get_training_job_status(cfg.aws.boto3_session, job_name)
-            if training_status.status != previous_status:
-                color = _STATUS_COLOR.get(training_status.status, "white")
-                CONSOLE.print(
-                    f"Job [cyan]{training_status.name}[/cyan]: "
-                    f"[{color}]{training_status.status}[/{color}]"
-                )
-                previous_status = training_status.status
-            if training_status.status in _TERMINAL_STATUSES:
-                _print_training_status(training_status)
-                _finalize_terminal_job(
-                    config_path=config,
-                    cfg=cfg,
-                    status=training_status,
-                    command="train wait",
-                )
-                job_state = st.get_training_job(job_name)
-                if job_state.get("mlflow_run_id") and cfg.mlflow.mode is not MlflowMode.disabled:
-                    CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
-                return
-            time.sleep(poll_interval)
-    except KeyboardInterrupt:
-        CONSOLE.print("[yellow]Stopped waiting. The SageMaker training job is still running.[/yellow]")
-        raise typer.Exit(130)
-
-
 @app.command(name="list")
 def list_jobs(
     limit: int = typer.Option(10, "--limit", "-n", help="Number of jobs to show"),