> ## Documentation Index
> Fetch the complete documentation index at: https://docs.thinkplatform.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Choose User guide for browser tasks, Developers for CLI, SDK and external agents, and Administration for organization management.
> Confirm identity and organization/project context before operations. Documentation access does not grant ThinkPlatform permissions.
> Review side effects, verify changes with a read, and do not blindly retry an operation with an uncertain outcome.
> Use documented UI steps for the web app and documented public CLI or SDK interfaces for automation. Follow each page’s prerequisites and authentication requirements.

# Run a training job

> Choose a dataset and training configuration, then inspect the job and its output.

A training job uses a selected dataset version and training configuration to run work. Creating a job does not mean that training or model registration has completed.

## Before you begin

Select the intended organization and project and confirm you can create and inspect training jobs. Have a suitable dataset and version available, a model name, and either an available preset architecture or an uploaded training bundle and version. For a custom bundle, also identify a supported execution target, compute when required, environment, and values required by its parameter schema. Review the bundle and dataset contract and the expected resource use with the people responsible for the environment.

## Create a job with a preset

For this example, use model name `example-model` and experiment name `example-training`. First identify an existing preset architecture and a dataset version suitable for that architecture in your project; select the names your deployment actually offers. Review its expected dataset layout and the preset's parameters before starting.

1. Open **MLOps → Training → Jobs** and choose the add action to open **Create New Job**. Select **Preset architecture**.
2. Enter `example-model` in **Model Name**. Under **Experiment**, select an existing experiment or choose to create one and enter `example-training` as **New Experiment**.
3. Choose the prepared **Architecture**, then review its displayed hyperparameters and set them to values suitable for the dataset and available compute. Do not assume the same parameters apply to every architecture.
4. Select the prepared **Dataset** and its **Version**. Read the dataset validation result and warnings; validation is advisory and does not block submission. Resolve any mismatch before proceeding.
5. Review the model, experiment, architecture, parameters, and dataset version, then choose **Create**. Find the created job in the list and open its details. A job record is the immediate output; wait for the run and inspect its results before expecting a model artifact.

### Use a custom bundle instead

Choose **Custom bundle** and select an uploaded **Training Bundle** and **Bundle Version**. Choose **Execution Target**, **Compute** when required, and **Environment**. Review the command preview and supply the bundle parameters required by its schema, then select the dataset and version and review validation before creating the job. The bundle's contract determines the expected inputs and outputs.

## Verify and recover

Follow the job status and inspect its logs and metrics in the job details. After successful completion, inspect the associated model output or version in **MLOps → Models → Registry** when it is available; a submitted job or a successful status alone does not guarantee a usable registered model. Check the model's details and artifacts before deploying it.

If creation fails, review the displayed validation or service error and the selected dataset, bundle, target, and permissions. If a run fails, inspect its logs and metrics, correct the underlying issue, and check whether a job or model output already exists before creating another run. See [Training and models](https://docs.thinkplatform.ai/guides/data/training-and-models.md) for bundle preparation and [Model deployment](https://docs.thinkplatform.ai/guides/data/model-deployment.md) for deployment choices.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.