> ## Documentation Index
> Fetch the complete documentation index at: https://arkor-92aeef0e-eng-635.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Recipes

> Recipes that show what TypeScript unlocks for an Arkor training run today.

Arkor is alpha. The framework's surface is intentionally small: one trainer, one manifest slot, a handful of lifecycle callbacks. What makes that small surface worthwhile is that **all of it is your TypeScript code**. Anything you can express in TS, you can wire into a training run, with the same editor, types, and review flow as the rest of your product.

This section collects the patterns that show up first when you try. Each recipe stays inside today's public SDK: no roadmap APIs, no internal imports, nothing that requires forking the runtime.

## Recipes

| Recipe                                                               | What it shows                                                                                                      | Built on                                                                                                     |
| -------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------ |
| [Mid-run evaluation](/cookbook/mid-run-eval)                         | Sanity-check the half-trained model against a fixed prompt at every checkpoint, before the run finishes.           | [`onCheckpoint({ infer })`](/sdk/callbacks)                                                                  |
| [Early stopping on diverging loss](/cookbook/early-stopping)         | Abort a run automatically when the loss curve goes the wrong way, and stop the GPU on the backend too.             | [`onLog`](/sdk/callbacks), [`AbortSignal`](/sdk/trainer-control), [`trainer.cancel()`](/sdk/trainer-control) |
| [Slack / Discord notifications](/cookbook/notifications)             | Post to a webhook on completion or failure, without leaving the trainer file.                                      | [`onCompleted` / `onFailed`](/sdk/callbacks), `fetch`                                                        |
| [Programmatic runs (no CLI)](/cookbook/programmatic-runs)            | Drive training from a Next.js API route, a cron worker, or CI without going through `arkor dev` / `arkor start`.   | [`runTrainer`](/sdk/overview), [`Trainer.start / wait`](/sdk/trainer-control)                                |
| [Customizing the starter templates](/cookbook/customizing-templates) | Treat the scaffolded templates as starting points. Change the dataset, hyperparameters, callbacks, and base model. | [`createTrainer`](/sdk/create-trainer), [`DatasetSource`](/sdk/dataset)                                      |

## A note on callback exceptions before you start

Three of the recipes below put logic inside the lifecycle callbacks. The runtime catches errors thrown out of a callback and routes them through the SSE reconnect loop ([SDK § Lifecycle callbacks](/sdk/callbacks)), and `maxReconnectAttempts` defaults to unlimited. In practice that means a `throw` inside a callback can be silently retried.

The recipes here use a simple convention to stay deterministic:

* **State changes go through outer variables.** Use an `AbortController`, a closure flag, or a returned Promise rather than throwing.
* **Side effects are guarded with `try / catch` inside the callback.** If a Slack post fails, log it and continue; do not let it bubble.

This is a pattern, not a limit on what you can do. Once you have it, the recipes compose cleanly.

## Things this section does not cover

* **Production deploys.** Arkor today only runs on managed GPUs, and `createArkor`'s `deploy` slot is a reserved type field with no implementation. Serving recipes will land when that surface lands.
* **Multiple trainers per project.** `createArkor` accepts a single `trainer`; running several together is a programmatic-run pattern (see the recipe), not a manifest pattern.
* **Custom base models beyond what the backend accepts.** The `model` field is forwarded to the cloud API verbatim; today the curated path is Gemma. Recipes do not pretend other models work end to end.