Training
The training run list and run detail (progress steps, metrics, logs), and the pretrained weights you uploaded
Required permission: View
This screen collects the training runs executed on the platform. Training runs as a Kubernetes Job in the tenant namespace, and the resulting metrics are logged as an MLflow run. Every member can see the list and detail; new training and uploading weights need the develop permission. The 11 steps for creating new training are described in the New training wizard.
Training list
| # | Area | Description |
|---|---|---|
| ① | Search by name (이름 검색) | Find by run name. |
| ② | Status filter | Filter by the values in the status table below. |
| ③ | Pretrained weights (사전학습 가중치) | Screen of checkpoints you uploaded (develop). |
| ④ | New training (새 학습) | Opens the wizard (develop). |
| ⑤ | Training run | Name (with a Preset (프리셋) badge if created from a preset), status, current step, duration, created. Click to open the detail. While any run is in progress, the list refreshes every 10 seconds. |
Training run detail
| # | Area | Description |
|---|---|---|
| ① | Actions | Train again with these settings (이 설정으로 다시 학습) (develop, opens the wizard's review step with the same settings), View MLflow run (MLflow run 보기), Register model (모델 등록) (model_register, completed runs), Stop (중지) (develop, queued or running), Delete (삭제) (operate, finished runs). |
| ② | Run info (실행 정보) | Model, dataset, GPU (number of wired slots, or CPU), runtime image, start and end. |
| ③ | Settings (설정) | Task, classes, split, starting weights, evaluation, export, model registration, data cache, and the collapsed hyperparameters (YAML). |
| ④ | Progress steps (진행 단계) | Progress of the six steps. Expand a step to see its details. |
| ⑤ | Training metrics (학습 지표) | Charts of the metrics logged to MLflow. While running, a Live (실시간) badge is shown. Click metric name chips to overlay up to 4. |
| ⑥ | Logs | Trainer log. Filter with Search logs (로그 검색) and download the file with Full log (전체 로그). |
Expand Run info (debug) (실행 정보 (디버그)) at the bottom of the screen to see the Job manifest, environment variables, volumes, pod status and even cluster events. Use it to find the cause of a failure.
If the run is queued, "You are number N in the queue (대기열 N번째입니다)" appears at the top; if it failed, a banner shows at which step it stopped and the error.
Pretrained weights
Upload checkpoints you already have (.pt, .pth) here when you want to continue training from them. Enter through the Pretrained weights (사전학습 가중치) button on the list.
| # | Area | Description |
|---|---|---|
| ① | Upload weights (가중치 올리기) | Opens the upload dialog. |
| ② | List | Name, variant, size, status, uploaded. Delete with Delete (삭제) on each row. |
- Press Upload weights (가중치 올리기), choose the name, task, framework, model variant (모델 변형) and file, then press Upload (올리기). A checkpoint is tied to its architecture, so it can only be used for training with the variant you choose here.
Upload pretrained weights — a checkpoint only works for the model variant you pick - When the status becomes Ready (사용 가능), it appears under Starting weights (시작 가중치) in step ③ Model of the wizard.
Statuses and badges
Training run status:
| Badge | Value | Meaning |
|---|---|---|
| Accepted (접수됨) | PENDING | Submitted, but not in the queue yet. |
| Queued (대기 중) | QUEUED | Waiting for an execution slot or GPU. Slots are shared by all tenants. |
| Running (실행 중) | RUNNING | The Job is running. |
| Stopping (중지 중) | STOPPING | A stop was requested; the trainer is saving a checkpoint (last.pt) and exiting. If it exceeds the grace period, it is forced to end. |
| Stopped (중지됨) | STOPPED | Stopped by a user. |
| Completed (완료) | COMPLETED | Ended normally. |
| Failed (실패) | FAILED | Ended with an error. See the banner and log in the detail for the cause. |
| Interrupted (중단됨) | INTERRUPTED | A run whose tracking was lost when the server restarted. It does not run again automatically — resubmit it with Train again with these settings (이 설정으로 다시 학습). |
Progress steps (④) and the current step (현재 스텝) in the list:
| Step | What it does |
|---|---|
| Waiting to run (실행 대기) | Waits for its turn in the queue |
| Preparing training environment (학습 환경 준비) | Pulls the training runtime image and starts the pod |
| Preparing dataset (데이터셋 준비) | Moves the dataset onto the training volume (skipped if cached) |
| Training (학습) | Runs the trainer |
| Registering in model registry (모델 레지스트리 등록) | If registration is on, registers the result as a new model version ("Register model (모델 등록)" in the list) |
| Wrapping up results (결과 정리) | Collects evaluation and export results |
Step statuses are Not started · In progress · Completed · Failed · Stopped · Skipped · Interrupted (시작 전 · 진행 중 · 완료 · 실패 · 중지됨 · 건너뜀 · 중단됨). Skipped means the step did not run because an earlier step failed or was stopped.
Pretrained weights status: Uploading (올리는 중) (upload %), Verifying (확인 중) (the server is checking the file), Ready (사용 가능) (selectable in the wizard), Failed (실패) (with the error beside it).