3. New training (11-step wizard)
Choose 11 steps, from the task to the runtime, and start training
Required permission: DevelopDEVELOPER · OPERATOR · ADMIN
New training collects the settings in 11 steps, following the step list on the left, and submits them once, at the end. The Next (다음) button stays disabled until you make a choice in the current step, and you can go back to any step you have passed by clicking it in the list on the left.
| # | Step | What you choose |
|---|---|---|
| 1 | Task (태스크) | Object Detection, Instance Segmentation, Classification … |
| 2 | Framework (프레임워크) | A framework that supports the task (for example YOLO, RF-DETR) |
| 3 | Model (모델) | Model variant and Starting weights (시작 가중치) |
| 4 | Datasets (데이터셋) | A dataset with files usable for training |
| 5 | Classes (클래스) | Classes to include in training |
| 6 | Train/Val split (Train/Val 분할) | Split method, train ratio, seed |
| 7 | Hyperparameters (하이퍼파라미터) | Edit the framework's default YAML |
| 8 | Evaluation (평가) | Evaluate on the validation set after training |
| 9 | Export (Export) | Extra ONNX and TensorRT conversion |
| 10 | Runtime (실행 환경) | Training runtime variant, number of GPU slots |
| 11 | Review and start (검토·시작) | Experiment name, model registry registration, submit |
- In the sidebar, open Training (학습) and press New training (새 학습) ①. (You can also come in through Train with this dataset (이 데이터셋으로 학습) on the dataset detail.)
Training list — ① New training - 1. Task (태스크) — you want to find helmets with bounding boxes, so choose Object Detection ① and press Next (다음). A greyed-out task has no framework that can run it yet.
Step 1, Task — ① Object Detection - In 2. Framework (프레임워크) choose YOLO, and in 3. Model (모델) choose a model variant ①. The Params(M) and mAP in the table are public benchmark values, useful for gauging size and performance. Starting weights (시작 가중치) ② defaults to Catalog pretrained weights (카탈로그 사전학습 가중치); if you have uploaded your own checkpoint, you can choose it here.
Step 3, Model — ① model variant ② starting weights - In 4. Dataset (데이터셋), choose
안전모 현장 사진(the helmet field photos dataset) ① that you created on the previous page. Datasets without files usable for training do not appear in the list. If you turn on Use dataset cache (데이터셋 캐시 사용) ②, training again with the same dataset skips the download.Step 4, Dataset — ① dataset to train on ② dataset cache - 5. Classes (클래스) — the classes read from the annotations and their object counts are shown. Use Select all compatible classes (호환 클래스 전체 선택) ① to include both
headandhelmet. Depending on the model variant, classes are required, optional or not applicable.Step 5, Classes — classes and object counts read from the annotations, ① select all compatible classes - 6. Train/Val split (Train/Val 분할) — the default is Random (ratio-based random) at 80%. Stratified keeps the label ratio of the validation set in line with the training set. The same seed reproduces the same split.
Step 6, Train/Val split — method, train ratio and seed - 7. Hyperparameters (하이퍼파라미터) — the framework defaults are filled in as YAML ①. Change only the values you need, such as
epochs,batchandimgsz. The server checks the allowed keys and ranges on submit; if something is wrong, you are sent back to this step with the error. Reset to defaults (기본값으로 재설정) ② restores the defaults.Step 7, Hyperparameters — ① edit as YAML ② reset to defaults - Run model evaluation (모델 평가 실행) in 8. Evaluation (평가) computes mAP and other metrics on the validation set after training and records them in MLflow. In 9. Export, the PyTorch weights are always saved; turn on ONNX export (ONNX 변환) and TensorRT export (TensorRT 변환) when you need them (the conversion results are also uploaded as MLflow run artifacts).
- 10. Runtime (실행 환경) — training runs as a Kubernetes Job in the tenant namespace. If you leave Training runtime variant (학습 런타임 변형) ① empty (Auto (default) (자동 (기본))), the tenant's default image is used. Choose the number of GPU slots in GPU resources (GPU 자원) ②; with 0, it runs on CPU.
Step 10, Runtime — ① training runtime variant ② GPU resources (this tenant shows the warnings for a missing runtime image and GPU) - 11. Review and start (검토·시작) — enter Experiment name (optional) (실험 이름 (선택)) ①. Training runs submitted with the same name are grouped as runs under one experiment in MLflow, which makes them easy to compare (if you leave it empty, a name is generated and each run becomes a new experiment). Turn on Register in model registry (모델 레지스트리 등록) ② and enter Registered model name (등록 모델 이름), and the model is registered automatically as a new version when training finishes. Look over the summary and press Start training (학습 시작) ③.
Step 11, Review and start — ① experiment name ② register in model registry ③ start training
When you submit, a "Training started (학습 시작됨)" notification appears, you are taken to the training run detail screen, and the training run waits in line in the Waiting in queue (실행 대기) step.