training_start
Launch a cloud training job for YOLO models on one dataset, validating it upfront to prevent wasted credits. Requires cost confirmation and may overwrite training history.
Instructions
Start a cloud training job from an existing model or official YOLO base checkpoint (state-changing, may cost credits). Trains on exactly one dataset and requires epochs. The dataset is validated immediately, so an unusable dataset is rejected before any compute starts. In checkpoint mode a project model is created first, then the checkpoint's task is checked against the dataset's task server-side when training starts, by which point the model already exists — a mismatch there still leaves that model behind, unrequested, and the error names it so it can be reviewed and deleted (models_delete) if unwanted. Starting is billable immediately: the platform has no cost preview before that, so the projected cost and remaining balance are only reported after the job starts. Training an existing model that already has a recorded run (any status past pending/untrained) replaces that model's status, epoch count, and training result history the instant the new job starts, and that history cannot be recovered afterward; the previously uploaded weights survive. That path requires confirm_history_loss=true in addition to confirm_cost=true. Checkpoint mode never destroys an existing model's history since it always creates a new one, so it never needs confirm_history_loss — its own risk is the possible leftover model described above. An untrained or never-trained model needs no extra confirmation either. Use training_cancel to stop a job that is already running. Requires confirm_cost=true.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Run name for callbacks. | |
| batch | No | Images per batch: -1 targets about 60% GPU memory, a value between 0 and 1 sets a memory fraction, and a positive integer fixes the image count. | |
| imgsz | No | Target input size: square batches normally, or the long-side size with rect=true. | |
| model | Yes | Existing model ref, or official YOLO base checkpoint such as yolo11n.pt or yolo11n-seg.pt. Checkpoint mode auto-creates a project model. | |
| epochs | Yes | Maximum full passes over the training set. | |
| dataset | Yes | Dataset ref by slug, owner/slug, or a ul://owner/datasets/slug URI. | |
| project | Yes | Project ref that owns the training job and resolved model. | |
| gpu_type | Yes | Cloud GPU type to allocate for training. | |
| train_args | No | Additional YOLO training arguments passed through to the platform. epochs, imgsz, batch, and name here are silently overridden by the matching top-level input when both are set; data and model are rejected outright if present here. | |
| confirm_cost | No | Must be true to allow a credit-costing training run. Starting is billable immediately; the platform has no cost preview before that, so the estimated cost and remaining balance are only reported after the job starts. | |
| confirm_history_loss | No | Must be true to restart training on an existing model that already has a recorded run. Doing so replaces that model's status, epoch count, and training result history irrecoverably; the previously uploaded weights survive. Not required for an untrained model or for checkpoint mode, which creates a new model instead. Separate from confirm_cost. |