4. Watch training progress
Follow training with the six-step timeline the server observes and the MLflow metrics
The training run detail updates in real time (the server pushes steps, status and logs over SSE). There is no need to refresh.
- At the top ① are the model, dataset, GPU, runtime image and a summary of the settings; on the right ② are the training metrics charts and the logs. ③ View MLflow run (MLflow run 보기) takes you to the MLflow run this training run created.
Training run detail — ① run info and config ② training metrics (MLflow) ③ View MLflow run - Below, Progress steps (진행 단계) shows how far along the run is. Every training run has the same six steps.
Progress steps — the six-step timeline the server observes
The six-step timeline
| Step | Name on screen | What it waits for |
|---|---|---|
queue | Waiting in queue (실행 대기) | Its turn in the training queue. Waits for the limit on concurrent training runs (default 1) and for GPU slots to free up |
runtime_prepare | Preparing the training environment (학습 환경 준비) | Creating the Kubernetes Job, scheduling the pod, pulling the training image |
data_stage | Preparing the dataset (데이터셋 준비) | Moving the dataset into the pod. Finishes at once if it is already in the cache |
train | Training (학습) | Running the training container. Progress is computed from the number of epochs recorded in MLflow |
register | Model registry registration (모델 레지스트리 등록) | If registration was turned on in the wizard, registers the model the training left as a new version |
finalize | Wrapping up (결과 정리) | Recording the status and end time, notifications |
A step's status moves Not started (시작 전) → In progress (진행 중) → Completed (완료), and becomes Failed (실패), Stopped (중지됨) or Skipped (건너뜀) if something goes wrong. The overall status of the training run appears as a badge in the list and next to the title: Accepted (접수됨), Queued (대기 중), Running (실행 중), Completed (완료), Failed (실패) or Stopped (중지됨).
Training metrics come from MLflow
Values in the charts such as loss and mAP are read directly from the metrics the training container logged to MLflow. The platform does not parse standard output to produce metrics. So:
- Right after training starts, the charts may be empty until the first validation finishes (that is when the metric keys appear).
- You can see the same values in the run detail on the Experiments (실험) screen, or with an MLflow client.
The log card collects the training container's standard output in real time. Search it with Search logs (로그 검색) and download it with Full log (전체 로그).
To stop training or run it again with the same settings, see the How-to pages Stop training and resume later and Train again with the same settings.