---
title: Training | Scenario Docs
description: API reference for enhanced model training methods.
---

## `client.models.train.trigger(modelID, params)`

Trigger model training. Returns enhanced response with `response.job.wait()`. See [Jobs](/sdk-helpers/jobs/index.md) for the full `.wait()` reference.

**Signature**

```
client.models.train.trigger(
  modelID: string,
  params: TrainTriggerParams,
  options?: RequestOptions,
): APIPromise<WithJob<TrainTriggerResponse>>
```

**Parameters**

| Name                          | Type      | Description                          |
| ----------------------------- | --------- | ------------------------------------ |
| `modelID`                     | `string`  | The model ID to train.               |
| `params.maxSteps?`            | `number`  | Maximum training steps.              |
| `params.dryRun?`              | `unknown` | If set, validates without executing. |
| `params.originalAssets?`      | `unknown` | Original asset configuration.        |
| `params.trainingImagesCount?` | `unknown` | Number of training images to use.    |

**Returns**

`APIPromise<WithJob<TrainTriggerResponse>>` — The response contains:

| Property | Type     | Description                              |
| -------- | -------- | ---------------------------------------- |
| `job`    | `Job`    | The training job, with `.wait()` method. |
| `model`  | `object` | The model data.                          |

**Example**

```
import Scenario from '@scenario-labs/sdk';


const client = new Scenario({ apiKey: '...' });


const response = await client.models.train.trigger('model_my-custom-model', {
  maxSteps: 1000,
});


console.log(response.job.jobId);  // 'job_...'
console.log(response.job.status); // 'in-progress'


const completed = await response.job.wait({
  intervalMs: 15_000,   // poll every 15 seconds
  timeoutMs: 900_000,   // wait up to 15 minutes
});


console.log(completed.status); // 'success'
```

Caution

Training jobs can take several minutes to hours. Set `timeoutMs` accordingly.