include-metrics-from-training (#6)

Reviewed-on: #6

README.md

@@ -105,7 +105,11 @@ mlflow:
   tracking_server_name: your-tracking-server-name
 ```
 
-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 experiment model versions using the `experiment-latest` MLflow alias. An experiment version is an immutable trained-source artifact; it records that training produced a model, not that the model is better than earlier versions or ready for release.
+When MLflow is enabled, `train start` creates an MLflow run for the SageMaker job. Metric upload through
+`train start --upload-metrics` or `mlflow upload-metrics` finalizes that run and registers completed model artifacts
+as experiment model versions using the `experiment-latest` MLflow alias. `train status` reads SageMaker status only.
+An experiment version is an immutable trained-source artifact; it records that training produced a model, not that
+the model is better than earlier versions or ready for release.
 
 To open the managed SageMaker MLflow UI, request a fresh presigned URL:
 
@@ -128,9 +132,14 @@ qc-cli init --force          Overwrite an existing config file
 ### `mlflow`
 
 ```
-qc-cli mlflow open  Open a presigned MLflow UI URL in a browser
+qc-cli mlflow open                       Open a presigned MLflow UI URL
+qc-cli mlflow upload-metrics [job-name] Upload completed training metrics
 ```
 
+`mlflow upload-metrics` defaults to the last submitted training job. It creates or recovers the job's MLflow run,
+imports `training_metrics.json` from the SageMaker model artifact, and records successful upload in `.qc-cli.json`.
+Use `--force` to upload the metrics again.
+
 ### `infra`
 
 ```
@@ -163,6 +172,7 @@ 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 --upload-metrics  Submit, wait, and upload metrics
 qc-cli train status [job-name]  Show job status; defaults to the last submitted job
 qc-cli train list               List recent training jobs
 qc-cli train list --limit 3     Show a custom number of recent jobs
@@ -170,6 +180,8 @@ 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 start --upload-metrics` checks SageMaker every 30 seconds by default, then uploads metrics after completion. 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`.
 
 ### `ai-hub`
@@ -216,13 +228,34 @@ 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` finalizes the MLflow run after the job reaches a terminal state.
+2. `qc-cli train status` reads and displays SageMaker status only; it does not contact MLflow.
-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:
+3. `qc-cli train start --upload-metrics` polls every 30 seconds by default, then uploads per-epoch metrics after completion.
+4. `qc-cli mlflow upload-metrics [job-name]` uploads or retries metrics for an existing completed job.
+5. The metrics upload workflow finalizes the MLflow run and, when `mlflow.register_trained_models` is enabled, registers the SageMaker `model.tar.gz` as a new MLflow model version with:
    - `qc_cli.stage=experiment`
    - `qc_cli.artifact_kind=trained_source`
    - `qc_cli.source=sagemaker`
-4. The MLflow alias `experiment-latest` points at the most recently registered experiment version.
+6. The MLflow alias `experiment-latest` points at the most recently registered experiment version.
-5. AI Hub upload commands create deployable derived artifacts from a trained-source experiment or local ONNX model.
+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. 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
+{
+  "schema_version": 1,
+  "steps": [
+    {"step": 0, "metrics": {"val.precision": 0.72, "val.recall": 0.68}}
+  ],
+  "summary": {"summary.best_epoch": 0}
+}
+```
+
+Metric names must be non-empty strings, values must be finite numbers, and steps must be non-negative, unique, and
+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.
 

examples/meter-detection/README.md

@@ -153,6 +153,20 @@ Or pass the job name explicitly:
 qc-cli train status qc-cli-YYYYMMDD-HHMMSS
 ```
 
+To submit the job, wait for completion, and automatically import metrics and register the model, run:
+
+```bash
+qc-cli train start --upload-metrics
+```
+
+The default polling interval is 30 seconds. It can be changed with `--poll-interval <seconds>`.
+
+The metrics can be also submitted using:
+
+```bash
+qc-cli mlflow upload-metrics
+```
+
 ## SageMaker Outputs
 
 When the job completes, SageMaker packages the files written under `/opt/ml/model` into `model.tar.gz`.
@@ -163,10 +177,15 @@ This example writes:
 best.pt
 model.onnx
 metrics.json
+training_metrics.json
 ```
 
 The archive is stored under the configured `s3.model_prefix`.
 
+The `mlflow upload-metrics` command imports `training_metrics.json`, which provides per-epoch training and validation
+losses, precision, recall, mAP@0.50, mAP@0.50:0.95, and learning rates. For object detection, mAP and precision/recall
+are more meaningful than classification accuracy when assessing model quality.
+
 ## 6. Configure Qualcomm AI Hub
 
 Authenticate with Qualcomm AI Hub:

examples/meter-detection/source/train.py

@@ -12,6 +12,7 @@ from typing import Any
 
 import yaml
 from sanitize_onnx import sanitize_onnx
+from training_metrics import write_training_metrics
 from ultralytics import YOLO  # type: ignore[reportMissingImports]
 
 
@@ -101,6 +102,7 @@ def main() -> None:
     if not trained_weights.exists():
         raise FileNotFoundError(f"Could not find trained weights in {save_dir / 'weights'}")
 
+    write_training_metrics(save_dir / "results.csv", model_dir / "training_metrics.json")
     copy_if_exists(trained_weights, model_dir / "best.pt")
     trained_model = YOLO(str(trained_weights))
     onnx_path = Path(trained_model.export(format="onnx", imgsz=args.imgsz))

examples/meter-detection/source/training_metrics.py

@@ -0,0 +1,82 @@
+import csv
+import json
+import math
+import re
+from pathlib import Path
+from typing import Any
+
+METRIC_NAMES = {
+    "metrics/precision(B)": "val.precision",
+    "metrics/recall(B)": "val.recall",
+    "metrics/mAP50(B)": "val.map50",
+    "metrics/mAP50-95(B)": "val.map50_95",
+    "train/box_loss": "train.box_loss",
+    "train/cls_loss": "train.cls_loss",
+    "train/dfl_loss": "train.dfl_loss",
+    "val/box_loss": "val.box_loss",
+    "val/cls_loss": "val.cls_loss",
+    "val/dfl_loss": "val.dfl_loss",
+    "time": "train.elapsed_seconds",
+}
+
+
+def write_training_metrics(results_csv: Path, destination: Path) -> None:
+    steps = _read_metric_steps(results_csv)
+    summary = _build_summary(steps)
+    payload = {
+        "schema_version": 1,
+        "steps": steps,
+        "summary": summary,
+    }
+    destination.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+    print(f"Saved {destination}")
+
+
+def _read_metric_steps(results_csv: Path) -> list[dict[str, Any]]:
+    if not results_csv.is_file():
+        raise FileNotFoundError(f"Could not find Ultralytics metrics history: {results_csv}")
+
+    steps: list[dict[str, Any]] = []
+    with results_csv.open(newline="", encoding="utf-8") as csv_file:
+        for row_index, raw_row in enumerate(csv.DictReader(csv_file)):
+            row = {str(key).strip(): value for key, value in raw_row.items()}
+            raw_epoch = row.pop("epoch", row_index)
+            step = int(float(raw_epoch))
+            metrics: dict[str, float] = {}
+            for source_name, raw_value in row.items():
+                if raw_value is None or not raw_value.strip():
+                    continue
+                try:
+                    value = float(raw_value)
+                except ValueError:
+                    continue
+                if math.isfinite(value):
+                    metrics[METRIC_NAMES.get(source_name, _normalize_metric_name(source_name))] = value
+            steps.append({"step": step, "metrics": metrics})
+    return steps
+
+
+def _build_summary(steps: list[dict[str, Any]]) -> dict[str, float]:
+    if not steps:
+        return {}
+
+    summary: dict[str, float] = {}
+    final_step = steps[-1]
+    summary["summary.final_epoch"] = float(final_step["step"])
+    for name, value in final_step["metrics"].items():
+        summary[f"summary.final.{name}"] = value
+
+    scored_steps = [step for step in steps if "val.map50_95" in step["metrics"]]
+    if scored_steps:
+        best_step = max(scored_steps, key=lambda step: step["metrics"]["val.map50_95"])
+        summary["summary.best_epoch"] = float(best_step["step"])
+        summary["summary.best_val.map50_95"] = best_step["metrics"]["val.map50_95"]
+        if "val.map50" in best_step["metrics"]:
+            summary["summary.best_val.map50"] = best_step["metrics"]["val.map50"]
+    return summary
+
+
+def _normalize_metric_name(name: str) -> str:
+    normalized = name.replace("/", ".")
+    normalized = re.sub(r"[^A-Za-z0-9_.-]+", "_", normalized)
+    return normalized.strip("._") or "unnamed"

src/aws/mlflow.py

@@ -1,3 +1,6 @@
+import os
+from collections.abc import Generator
+from contextlib import contextmanager
 from typing import Any, cast
 
 import boto3
@@ -34,3 +37,38 @@ def create_presigned_tracking_server_url(region: str, profile: str, name: 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"])
+
+
+@contextmanager
+def tracking_auth_env(profile: str, region: str) -> Generator[None]:
+    credentials = boto3.Session(profile_name=profile, region_name=region).get_credentials()
+    if credentials is None:
+        raise RuntimeError(f"AWS credentials could not be resolved for profile '{profile}'.")
+
+    frozen_credentials = credentials.get_frozen_credentials()
+    if not frozen_credentials.access_key or not frozen_credentials.secret_key:
+        raise RuntimeError(f"AWS credentials are incomplete for profile '{profile}'.")
+
+    env_updates = {
+        "AWS_PROFILE": profile,
+        "AWS_DEFAULT_REGION": region,
+        "AWS_REGION": region,
+        "AWS_ACCESS_KEY_ID": frozen_credentials.access_key,
+        "AWS_SECRET_ACCESS_KEY": frozen_credentials.secret_key,
+    }
+    if frozen_credentials.token:
+        env_updates["AWS_SESSION_TOKEN"] = frozen_credentials.token
+
+    restore_keys = set(env_updates) | {"AWS_SESSION_TOKEN"}
+    previous_env = {key: os.environ.get(key) for key in restore_keys}
+    try:
+        os.environ.update(env_updates)
+        if not frozen_credentials.token:
+            os.environ.pop("AWS_SESSION_TOKEN", None)
+        yield
+    finally:
+        for key, value in previous_env.items():
+            if value is None:
+                os.environ.pop(key, None)
+            else:
+                os.environ[key] = value

src/cloud/__init__.py

@@ -0,0 +1 @@
+"""Cloud provider adapters."""

src/cloud/mlflow.py

@@ -0,0 +1,77 @@
+from contextlib import AbstractContextManager
+from dataclasses import dataclass
+from typing import Any, Protocol
+
+from src.aws import mlflow as aws_mlflow
+from src.config import Config
+
+
+class MlflowTrackingBackend(Protocol):
+    @property
+    def provider_name(self) -> str: ...
+
+    @property
+    def profile(self) -> str: ...
+
+    @property
+    def region(self) -> str: ...
+
+    def get_tracking_uri(self, tracking_server_name: str) -> str: ...
+
+    def auth_env(self) -> AbstractContextManager[None]: ...
+
+    def training_run_params(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> dict[str, Any]: ...
+
+    def training_run_tags(self, training_job: Any) -> dict[str, Any]: ...
+
+    def training_status_params(self, training_job_status: Any) -> dict[str, Any]: ...
+
+    def model_version_tags(self, training_job_status: Any) -> dict[str, Any]: ...
+
+
+@dataclass(frozen=True)
+class AwsMlflowTrackingBackend:
+    profile: str
+    region: str
+    provider_name: str = "aws"
+
+    def get_tracking_uri(self, tracking_server_name: str) -> str:
+        return aws_mlflow.get_tracking_server_arn(self.region, self.profile, tracking_server_name)
+
+    def auth_env(self) -> AbstractContextManager[None]:
+        return aws_mlflow.tracking_auth_env(self.profile, self.region)
+
+    def training_run_params(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> dict[str, Any]:
+        return {
+            "provider.name": self.provider_name,
+            "provider.region": region,
+            "provider.profile": profile,
+            "sagemaker.role_arn": role_arn,
+            "sagemaker.job_name": training_job.job_name,
+            "sagemaker.training_image": training_job.image_uri,
+            "sagemaker.instance_type": training_job.instance_type,
+            "sagemaker.instance_count": training_job.instance_count,
+            "sagemaker.s3_train_uri": training_job.s3_train_uri,
+            "sagemaker.s3_output_path": training_job.s3_output_path,
+            "sagemaker.entry_point": training_job.entry_point,
+            "sagemaker.source_dir": training_job.source_dir,
+        }
+
+    def training_run_tags(self, training_job: Any) -> dict[str, Any]:
+        return {"sagemaker.job_name": training_job.job_name}
+
+    def training_status_params(self, training_job_status: Any) -> dict[str, Any]:
+        return {
+            "sagemaker.training_status": training_job_status.status,
+            "sagemaker.created_at": training_job_status.created,
+            "sagemaker.modified_at": training_job_status.modified,
+            "sagemaker.model_artifacts": training_job_status.model_artifacts,
+            "sagemaker.failure_reason": training_job_status.failure_reason,
+        }
+
+    def model_version_tags(self, training_job_status: Any) -> dict[str, Any]:
+        return {"sagemaker.job_name": training_job_status.name}
+
+
+def mlflow_tracking_backend_from_config(cfg: Config) -> MlflowTrackingBackend:
+    return AwsMlflowTrackingBackend(profile=cfg.aws.profile, region=cfg.aws.region)

src/commands/mlflow.py

@@ -2,8 +2,11 @@ import webbrowser
 
 import typer
 
+from src import state as state_ops
 from src.aws import mlflow as aws_mlflow
 from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
+from src.config import MlflowMode
+from src.tracking.upload import upload_training_metrics
 
 app = typer.Typer(help="Manage MLflow tracking server access")
 
@@ -39,3 +42,54 @@ def open_mlflow(config: str = CONFIG_OPT) -> None:
         CONSOLE.print("[green]✓[/green] Opened MLflow UI in your browser.")
     else:
         CONSOLE.print("[yellow]Could not open a browser automatically. Open the URL above manually.[/yellow]")
+
+
+@app.command(name="upload-metrics")
+def upload_metrics(
+    job_name: str | None = typer.Argument(None, help="Training job name (default: last submitted job)"),
+    force: bool = typer.Option(False, "--force", help="Upload again even if this job is marked as uploaded"),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Upload a completed training job's metric history to MLflow."""
+    cfg = load_cfg(config)
+    if cfg.mlflow.mode is MlflowMode.disabled:
+        CONSOLE.print("[red]MLflow is disabled in config.yaml.[/red]")
+        raise typer.Exit(1)
+
+    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)
+
+    if st.get_training_job(job_name).get("mlflow_metrics_uploaded") and not force:
+        CONSOLE.print(f"[green]✓[/green] Metrics already uploaded for [cyan]{job_name}[/cyan].")
+        return
+
+    try:
+        result = upload_training_metrics(
+            job_name=job_name,
+            config_path=config,
+            cfg=cfg,
+            force=force,
+        )
+    except Exception as e:
+        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(
+            f"MLflow model version: [cyan]{result.registered_model_version}[/cyan] "
+            "([cyan]experiment-latest[/cyan])"
+        )

src/commands/train.py

@@ -1,3 +1,4 @@
+import time
 from datetime import datetime
 from pathlib import Path
 
@@ -11,6 +12,7 @@ from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
 from src.config import Config, MlflowMode
 from src.infra.state import read_infra_state
 from src.tracking.mlflow import MlflowTracker
+from src.tracking.upload import upload_training_metrics
 
 app = typer.Typer(help="Manage SageMaker training jobs")
 
@@ -21,6 +23,8 @@ _STATUS_COLOR = {
     "Stopping": "yellow",
     "Stopped": "dim",
 }
+_TERMINAL_STATUSES = {"Completed", "Failed", "Stopped"}
+DEFAULT_POLL_INTERVAL_SECONDS = 30
 
 
 def _tracker(cfg):
@@ -48,11 +52,100 @@ def _sagemaker_role_arn(config_path: str, cfg: Config) -> str:
     raise RuntimeError("SageMaker role not found in infra state. Run 'qc-cli infra setup' first.")
 
 
+def _print_training_status(status: sm_ops.TrainingJobStatus) -> None:
+    color = _STATUS_COLOR.get(status.status, "white")
+    CONSOLE.print(f"Job:     [cyan]{status.name}[/cyan]")
+    CONSOLE.print(f"Status:  [{color}]{status.status}[/{color}]")
+    if status.created:
+        CONSOLE.print(f"Created: {status.created}")
+    if status.model_artifacts:
+        CONSOLE.print(f"Artifacts: {status.model_artifacts}")
+    if status.failure_reason:
+        CONSOLE.print(f"[red]Failure: {status.failure_reason}[/red]")
+
+
+def _wait_and_upload_metrics(
+    *,
+    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)
+                if training_status.status != "Completed":
+                    raise typer.Exit(1)
+                try:
+                    result = upload_training_metrics(
+                        job_name=job_name,
+                        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] "
+                            "([cyan]experiment-latest[/cyan])"
+                        )
+                except Exception as e:
+                    CONSOLE.print(f"[red]MLflow metric upload failed: {e}[/red]")
+                    CONSOLE.print(
+                        f"Retry with [cyan]qc-cli mlflow upload-metrics {job_name}[/cyan]."
+                    )
+                    raise typer.Exit(1)
+                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(
+    upload_metrics: bool = typer.Option(
+        False,
+        "--upload-metrics",
+        help="Wait for completion, then upload training metrics to MLflow",
+    ),
+    poll_interval: int = typer.Option(
+        DEFAULT_POLL_INTERVAL_SECONDS,
+        "--poll-interval",
+        min=1,
+        help="Seconds between status checks when --upload-metrics is used",
+    ),
+    config: str = CONFIG_OPT,
+) -> None:
     """Submit a SageMaker training job."""
     cfg = load_cfg(config)
 
+    if upload_metrics and cfg.mlflow.mode is MlflowMode.disabled:
+        CONSOLE.print("[red]--upload-metrics requires MLflow to be enabled in config.yaml.[/red]")
+        raise typer.Exit(1)
+
     if not cfg.sagemaker.training.image_uri:
         CONSOLE.print("[red]sagemaker.training.image_uri is required in config.yaml.[/red]")
         CONSOLE.print(
@@ -89,12 +182,20 @@ def start(config: str = CONFIG_OPT) -> None:
 
     st = state_ops.store(config)
     st.set_last_training_job(job_name)
+    try:
         run_id = tracker.start_training_run(
             training_job,
             region=cfg.aws.region,
             profile=cfg.aws.profile,
             role_arn=role_arn,
         )
+    except Exception as e:
+        run_id = None
+        CONSOLE.print(f"[yellow]MLflow run creation failed: {e}[/yellow]")
+        CONSOLE.print(
+            "The SageMaker job is still running. Upload metrics after completion with "
+            f"[cyan]qc-cli mlflow upload-metrics {job_name}[/cyan]."
+        )
     if run_id:
         st.update_training_job(job_name, mlflow_run_id=run_id)
 
@@ -102,6 +203,14 @@ 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]")
+    if upload_metrics:
+        _wait_and_upload_metrics(
+            job_name=job_name,
+            poll_interval=poll_interval,
+            config_path=config,
+            cfg=cfg,
+        )
+    else:
         CONSOLE.print("Track progress: [cyan]qc-cli train status[/cyan]")
 
 
@@ -123,35 +232,7 @@ def status(
             raise typer.Exit(1)
 
     status = sm_ops.get_training_job_status(cfg.aws.boto3_session, job_name)
-    color = _STATUS_COLOR.get(status.status, "white")
+    _print_training_status(status)
-
-    CONSOLE.print(f"Job:     [cyan]{status.name}[/cyan]")
-    CONSOLE.print(f"Status:  [{color}]{status.status}[/{color}]")
-    if status.created:
-        CONSOLE.print(f"Created: {status.created}")
-    if status.model_artifacts:
-        CONSOLE.print(f"Artifacts: {status.model_artifacts}")
-    if status.failure_reason:
-        CONSOLE.print(f"[red]Failure: {status.failure_reason}[/red]")
-
-    job_state = st.get_training_job(job_name)
-    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"}:
-        tracker = _tracker(cfg)
-        version = tracker.finalize_training_run(
-            run_id=str(run_id),
-            training_job_status=status,
-        )
-        updates = {"mlflow_finalized_status": status.status}
-        if version:
-            updates["registered_model_version"] = version
-        st.update_training_job(job_name, **updates)
-        if version:
-            st.set_latest_experiment_model_version(version)
-            CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]experiment-latest[/cyan])")
-    if run_id and cfg.mlflow.mode is not MlflowMode.disabled:
-        CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
 
 
 @app.command(name="list")

src/tracking/__init__.py

@@ -1,3 +1,3 @@
-from src.tracking.mlflow import MlflowTracker, NoopTracker, Tracker
+from src.tracking.mlflow import FinalizeResult, MlflowTracker, NoopTracker, Tracker
 
-__all__ = ["MlflowTracker", "NoopTracker", "Tracker"]
+__all__ = ["FinalizeResult", "MlflowTracker", "NoopTracker", "Tracker"]

src/tracking/metrics.py

@@ -0,0 +1,93 @@
+import json
+import math
+import tarfile
+from dataclasses import dataclass
+from pathlib import PurePosixPath
+from typing import Any
+
+METRICS_ARTIFACT_NAME = "training_metrics.json"
+METRICS_SCHEMA_VERSION = 1
+MAX_METRICS_ARTIFACT_BYTES = 10 * 1024 * 1024
+
+
+@dataclass(frozen=True)
+class MetricStep:
+    step: int
+    metrics: dict[str, float]
+
+
+@dataclass(frozen=True)
+class TrainingMetrics:
+    steps: list[MetricStep]
+    summary: dict[str, float]
+    raw: dict[str, Any]
+
+
+def parse_training_metrics(data: bytes) -> TrainingMetrics:
+    try:
+        value = json.loads(data)
+    except (UnicodeDecodeError, json.JSONDecodeError) as exc:
+        raise ValueError(f"{METRICS_ARTIFACT_NAME} is not valid JSON") from exc
+    if not isinstance(value, dict):
+        raise ValueError(f"{METRICS_ARTIFACT_NAME} must contain a JSON object")
+    if value.get("schema_version") != METRICS_SCHEMA_VERSION:
+        raise ValueError(f"Unsupported training metrics schema version: {value.get('schema_version')!r}")
+
+    raw_steps = value.get("steps")
+    if not isinstance(raw_steps, list):
+        raise ValueError("training metrics 'steps' must be a list")
+
+    steps: list[MetricStep] = []
+    previous_step: int | None = None
+    for index, raw_step in enumerate(raw_steps):
+        if not isinstance(raw_step, dict):
+            raise ValueError(f"training metrics step {index} must be an object")
+        step = raw_step.get("step")
+        if isinstance(step, bool) or not isinstance(step, int) or step < 0:
+            raise ValueError(f"training metrics step {index} has an invalid 'step'")
+        if previous_step is not None and step <= previous_step:
+            raise ValueError("training metrics steps must be unique and strictly increasing")
+        metrics = _numeric_metrics(raw_step.get("metrics"), f"training metrics step {step}")
+        steps.append(MetricStep(step=step, metrics=metrics))
+        previous_step = step
+
+    summary = _numeric_metrics(value.get("summary", {}), "training metrics summary")
+    return TrainingMetrics(steps=steps, summary=summary, raw=value)
+
+
+def read_training_metrics_from_tar(archive_path: str) -> bytes | None:
+    with tarfile.open(archive_path, mode="r:*") as archive:
+        matches = [
+            member
+            for member in archive.getmembers()
+            if member.isfile() and PurePosixPath(member.name).name == METRICS_ARTIFACT_NAME
+        ]
+        if not matches:
+            return None
+        if len(matches) > 1:
+            raise ValueError(f"Model archive contains multiple {METRICS_ARTIFACT_NAME} files")
+        if matches[0].size > MAX_METRICS_ARTIFACT_BYTES:
+            raise ValueError(
+                f"{METRICS_ARTIFACT_NAME} exceeds the {MAX_METRICS_ARTIFACT_BYTES}-byte size limit"
+            )
+        extracted = archive.extractfile(matches[0])
+        if extracted is None:
+            raise ValueError(f"Could not read {METRICS_ARTIFACT_NAME} from model archive")
+        return extracted.read()
+
+
+def _numeric_metrics(value: Any, context: str) -> dict[str, float]:
+    if not isinstance(value, dict):
+        raise ValueError(f"{context} 'metrics' must be an object")
+
+    metrics: dict[str, float] = {}
+    for raw_name, raw_value in value.items():
+        if not isinstance(raw_name, str) or not raw_name:
+            raise ValueError(f"{context} contains an invalid metric name")
+        if isinstance(raw_value, bool) or not isinstance(raw_value, int | float):
+            raise ValueError(f"{context} metric '{raw_name}' must be numeric")
+        metric_value = float(raw_value)
+        if not math.isfinite(metric_value):
+            raise ValueError(f"{context} metric '{raw_name}' must be finite")
+        metrics[raw_name] = metric_value
+    return metrics

src/tracking/mlflow.py

@@ -1,18 +1,46 @@
 import os
+import tempfile
 from dataclasses import dataclass
 from typing import Any, Protocol
 
 import mlflow
 from mlflow.tracking import MlflowClient
 
-from src.aws import mlflow as aws_mlflow
+from src.aws import s3
+from src.cloud.mlflow import MlflowTrackingBackend, mlflow_tracking_backend_from_config
 from src.config import Config, MlflowMode
+from src.tracking.metrics import METRICS_ARTIFACT_NAME, parse_training_metrics, read_training_metrics_from_tar
+
+
+@dataclass(frozen=True)
+class FinalizeResult:
+    registered_model_version: str | None = None
+    warnings: tuple[str, ...] = ()
 
 
 class Tracker(Protocol):
     def start_training_run(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> str | None: ...
 
-    def finalize_training_run(self, *, run_id: str | None, training_job_status: Any) -> str | None: ...
+    def finalize_training_run(
+        self,
+        *,
+        run_id: str | None,
+        training_job_status: Any,
+        region: str,
+        profile: str,
+        command: str,
+    ) -> FinalizeResult: ...
+
+    def ensure_training_run(self, job_name: str) -> str: ...
+
+    def upload_training_metrics(
+        self,
+        *,
+        run_id: str,
+        training_job_status: Any,
+        region: str,
+        profile: str,
+    ) -> bool: ...
 
 
 @dataclass(frozen=True)
@@ -20,8 +48,29 @@ class NoopTracker:
     def start_training_run(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> str | None:
         return None
 
-    def finalize_training_run(self, *, run_id: str | None, training_job_status: Any) -> str | None:
+    def finalize_training_run(
-        return None
+        self,
+        *,
+        run_id: str | None,
+        training_job_status: Any,
+        region: str,
+        profile: str,
+        command: str,
+    ) -> FinalizeResult:
+        return FinalizeResult()
+
+    def ensure_training_run(self, job_name: str) -> str:
+        raise RuntimeError("MLflow is disabled.")
+
+    def upload_training_metrics(
+        self,
+        *,
+        run_id: str,
+        training_job_status: Any,
+        region: str,
+        profile: str,
+    ) -> bool:
+        raise RuntimeError("MLflow is disabled.")
 
 
 @dataclass(frozen=True)
@@ -30,6 +79,7 @@ class MlflowTracker:
     experiment_name: str
     registered_model_name: str
     register_trained_models: bool
+    tracking_backend: MlflowTrackingBackend
 
     @classmethod
     def from_config(cls, cfg: Config) -> Tracker:
@@ -42,11 +92,10 @@ class MlflowTracker:
         if not tracking_server_name:
             raise RuntimeError("MLflow tracking server name could not be resolved.")
 
-        tracking_uri = aws_mlflow.get_tracking_server_arn(
+        tracking_backend = mlflow_tracking_backend_from_config(cfg)
-            cfg.aws.region,
+
-            cfg.aws.profile,
+        tracking_uri = tracking_backend.get_tracking_uri(tracking_server_name)
-            tracking_server_name,
+        with tracking_backend.auth_env():
-        )
             mlflow.set_tracking_uri(tracking_uri)
             mlflow.set_experiment(cfg.mlflow.experiment_name)
 
@@ -55,62 +104,57 @@ class MlflowTracker:
             experiment_name=cfg.mlflow.experiment_name,
             registered_model_name=cfg.mlflow.registered_model_name,
             register_trained_models=cfg.mlflow.register_trained_models,
+            tracking_backend=tracking_backend,
         )
 
     def start_training_run(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> str | None:
-        run = mlflow.start_run(run_name=training_job.job_name)
+        with self.tracking_backend.auth_env():
+            with mlflow.start_run(run_name=training_job.job_name) as run:
                 run_id = str(run.info.run_id)
-
+                self._log_params(
-        params = {
+                    self.tracking_backend.training_run_params(
-            "aws.region": region,
+                        training_job,
-            "aws.profile": profile,
+                        region=region,
-            "sagemaker.role_arn": role_arn,
+                        profile=profile,
-            "sagemaker.job_name": training_job.job_name,
+                        role_arn=role_arn,
-            "sagemaker.training_image": training_job.image_uri,
+                    )
-            "sagemaker.instance_type": training_job.instance_type,
+                )
-            "sagemaker.instance_count": training_job.instance_count,
-            "sagemaker.s3_train_uri": training_job.s3_train_uri,
-            "sagemaker.s3_output_path": training_job.s3_output_path,
-            "sagemaker.entry_point": training_job.entry_point,
-            "sagemaker.source_dir": training_job.source_dir,
-        }
-        self._log_params(params)
                 self._log_params({f"hyperparameters.{key}": value for key, value in training_job.hyperparameters.items()})
                 mlflow.set_tags(
                     {
                         "qc_cli.stage": "experiment",
                         "qc_cli.artifact_kind": "trained_source",
-                "qc_cli.source": "sagemaker",
+                        "qc_cli.source": self.tracking_backend.provider_name,
                         "qc_cli.command": "train start",
-                "sagemaker.job_name": training_job.job_name,
+                        **self.tracking_backend.training_run_tags(training_job),
                     }
                 )
-        mlflow.end_run()
                 return run_id
 
-    def finalize_training_run(self, *, run_id: str | None, training_job_status: Any) -> str | None:
+    def finalize_training_run(
+        self,
+        *,
+        run_id: str | None,
+        training_job_status: Any,
+        region: str,
+        profile: str,
+        command: str,
+    ) -> FinalizeResult:
         if not run_id:
-            return None
+            return FinalizeResult()
 
+        with self.tracking_backend.auth_env():
             with mlflow.start_run(run_id=run_id):
-            self._log_params(
+                self._log_params(self.tracking_backend.training_status_params(training_job_status))
-                {
-                    "sagemaker.training_status": training_job_status.status,
-                    "sagemaker.created_at": training_job_status.created,
-                    "sagemaker.modified_at": training_job_status.modified,
-                    "sagemaker.model_artifacts": training_job_status.model_artifacts,
-                    "sagemaker.failure_reason": training_job_status.failure_reason,
-                }
-            )
                 self._log_final_metrics(training_job_status.raw)
-            mlflow.set_tag("qc_cli.command", "train status")
+                mlflow.set_tag("qc_cli.command", command)
 
                 if training_job_status.status != "Completed" or not training_job_status.model_artifacts:
                     mlflow.set_tag("qc_cli.training_terminal_status", training_job_status.status)
-                return None
+                    return FinalizeResult()
 
                 if not self.register_trained_models:
-                return None
+                    return FinalizeResult()
 
                 client = MlflowClient()
                 self._ensure_registered_model(client, self.registered_model_name)
@@ -121,15 +165,65 @@ class MlflowTracker:
                     tags={
                         "qc_cli.stage": "experiment",
                         "qc_cli.artifact_kind": "trained_source",
-                    "qc_cli.source": "sagemaker",
+                        "qc_cli.source": self.tracking_backend.provider_name,
-                    "sagemaker.job_name": training_job_status.name,
+                        **self.tracking_backend.model_version_tags(training_job_status),
                     },
                 )
                 version_number = str(version.version)
                 client.set_registered_model_alias(self.registered_model_name, "experiment-latest", version_number)
                 mlflow.set_tag("qc_cli.registered_model_name", self.registered_model_name)
                 mlflow.set_tag("qc_cli.registered_model_version", version_number)
-            return version_number
+                return FinalizeResult(registered_model_version=version_number)
+
+    def ensure_training_run(self, job_name: str) -> str:
+        with self.tracking_backend.auth_env():
+            client = MlflowClient()
+            experiment = client.get_experiment_by_name(self.experiment_name)
+            if experiment is None:
+                experiment_id = mlflow.create_experiment(self.experiment_name)
+            else:
+                experiment_id = experiment.experiment_id
+
+            for run in client.search_runs([experiment_id], max_results=1000):
+                if run.data.tags.get("sagemaker.job_name") == job_name:
+                    return str(run.info.run_id)
+
+            run = client.create_run(
+                experiment_id,
+                run_name=job_name,
+                tags={
+                    "qc_cli.stage": "experiment",
+                    "qc_cli.artifact_kind": "trained_source",
+                    "qc_cli.source": self.tracking_backend.provider_name,
+                    "qc_cli.command": "mlflow upload-metrics",
+                    "sagemaker.job_name": job_name,
+                },
+            )
+            return str(run.info.run_id)
+
+    def upload_training_metrics(
+        self,
+        *,
+        run_id: str,
+        training_job_status: Any,
+        region: str,
+        profile: str,
+    ) -> bool:
+        if not training_job_status.model_artifacts:
+            raise ValueError(f"Training job '{training_job_status.name}' has no model artifacts.")
+
+        with self.tracking_backend.auth_env():
+            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)
+                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}
@@ -146,6 +240,26 @@ class MlflowTracker:
         if metrics:
             mlflow.log_metrics(metrics)
 
+    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,
+                profile,
+                model_artifacts,
+                os.path.join(temp_dir, "model.tar.gz"),
+            )
+            metrics_data = read_training_metrics_from_tar(archive_path)
+            if metrics_data is None:
+                return False
+            metrics = parse_training_metrics(metrics_data)
+            for metric_step in metrics.steps:
+                if metric_step.metrics:
+                    mlflow.log_metrics(metric_step.metrics, step=metric_step.step)
+            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:
             client.get_registered_model(name)

src/tracking/upload.py

@@ -0,0 +1,75 @@
+from dataclasses import dataclass
+
+from src import state as state_ops
+from src.aws import sagemaker as sm_ops
+from src.config import Config, MlflowMode
+from src.tracking.mlflow import MlflowTracker
+
+
+@dataclass(frozen=True)
+class MetricsUploadResult:
+    run_id: str
+    registered_model_version: str | None = None
+    metrics_history_uploaded: bool = True
+
+
+def upload_training_metrics(
+    *,
+    job_name: str,
+    config_path: str,
+    cfg: Config,
+    force: bool = False,
+) -> MetricsUploadResult:
+    if cfg.mlflow.mode is MlflowMode.disabled:
+        raise RuntimeError("MLflow is disabled in config.yaml.")
+
+    st = state_ops.store(config_path)
+    job_state = st.get_training_job(job_name)
+    if job_state.get("mlflow_metrics_uploaded") and not force:
+        return MetricsUploadResult(
+            run_id=str(job_state.get("mlflow_run_id") or ""),
+            registered_model_version=(
+                str(job_state["registered_model_version"])
+                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)
+    if status.status != "Completed":
+        raise RuntimeError(
+            f"Training job {job_name} is {status.status}; metrics can be uploaded only after completion."
+        )
+
+    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)
+    metrics_history_uploaded = tracker.upload_training_metrics(
+        run_id=run_id,
+        training_job_status=status,
+        region=cfg.aws.region,
+        profile=cfg.aws.profile,
+    )
+    finalized = tracker.finalize_training_run(
+        run_id=run_id,
+        training_job_status=status,
+        region=cfg.aws.region,
+        profile=cfg.aws.profile,
+        command="mlflow upload-metrics",
+    )
+    updates = {
+        "mlflow_metrics_uploaded": True,
+        "mlflow_metrics_history_uploaded": metrics_history_uploaded,
+        "mlflow_finalized_status": status.status,
+    }
+    if finalized.registered_model_version:
+        updates["registered_model_version"] = finalized.registered_model_version
+    st.update_training_job(job_name, **updates)
+    if finalized.registered_model_version:
+        st.set_latest_experiment_model_version(finalized.registered_model_version)
+    return MetricsUploadResult(
+        run_id=run_id,
+        registered_model_version=finalized.registered_model_version,
+        metrics_history_uploaded=metrics_history_uploaded,
+    )