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

# Launch a Training Run

> Start a training job from the dashboard — select your datasets, rubric, and recipe, then start training.

Once you have your [prerequisites](/platform/train/overview#prerequisites) ready, launching a training run takes a few minutes.

## Start a new job

Go to the **Training** tab in the dashboard and click **New Training Job**.

<Frame caption="New Training Job form in the dashboard.">
  <img src="https://mintcdn.com/kuzco/zhciHP1--S5gDjt6/images/training/training-create.png?fit=max&auto=format&n=zhciHP1--S5gDjt6&q=85&s=9738a5488d17e17a3605fb281997ad64" alt="New Training Job form in the dashboard" width="1862" height="1436" data-path="images/training/training-create.png" />
</Frame>

<Steps>
  <Step title="Select a training dataset">
    Choose the dataset the model will learn from. You can [build a dataset from live traffic](/platform/datasets/build-from-traffic), [upload a JSONL file](/platform/datasets/upload-a-dataset), or use [task tags](/platform/gateway/tasks) to filter captured traffic for clean, focused samples.
  </Step>

  <Step title="Select an eval dataset">
    Choose the dataset used for [evaluations throughout training](/platform/train/mid-training-evals). Must have zero overlap with training data.
  </Step>

  <Step title="Select a rubric">
    Choose the [rubric](/platform/eval/write-a-rubric) that defines your quality criteria. Make sure you've validated it against your eval dataset first.
  </Step>

  <Step title="Select a recipe">
    Choose a [recipe](/platform/train/choose-a-recipe) based on task difficulty and capability needs.
  </Step>

  <Step title="Start training">
    Review your selections and click **Start Training** to queue the job.
  </Step>
</Steps>

## Training job lifecycle

Once started, your job moves through these statuses:

| Status                 | What's happening                                                      |
| ---------------------- | --------------------------------------------------------------------- |
| **Exporting datasets** | Your training and eval data is being prepared                         |
| **Queued**             | Job is waiting for available compute                                  |
| **Starting**           | GPUs are being allocated and the training environment is initializing |
| **Running**            | The model is actively training                                        |
| **Completed**          | Training finished successfully                                        |

If something goes wrong, the job will show one of these:

| Status        | What it means                                                                                                 |
| ------------- | ------------------------------------------------------------------------------------------------------------- |
| **Failed**    | Something went wrong — check the [logs](/platform/train/mid-training-evals#logs) on the training details page |
| **Cancelled** | The run was cancelled                                                                                         |
| **Timed out** | The run exceeded the maximum allowed duration                                                                 |

Training can take anywhere from around 10 minutes to 10+ hours depending on dataset size and recipe. Once running, [monitor progress](/platform/train/mid-training-evals) from the training details page.