runner-store-training

# Runner, store, and training loop Use this sub-skill when a user asks how to run training, manage rollouts/resources, debug store status, write algorithms, or use `Trainer.fit`/`Trainer.dev`. ## Route by task | Request | Read/run | | --- | --- | | Understand `LightningStore` rollouts, attempts, resources, workers, spans | [references/store-and-trainer-workflows.md](references/store-and-trainer-workflows.md) and [references/status-model.md](references/status-model.md) | | Write a custom algorithm or use `@algo` | [references/store-and-trainer-workflows.md](references/store-and-trainer-workflows.md#custom-algorithms) | | Configure `Trainer.fit` or `Trainer.dev` | [references/store-and-trainer-workflows.md](references/store-and-trainer-workflows.md#trainer-workflows) | | Debug retry/timeout/unresponsive statuses | [references/status-model.md](references/status-model.md) and [references/troubleshooting.md](references/troubleshooting.md) | | Run a CPU-safe store lifecycle smoke | `python scripts/store_status_smoke.py` | | Start a store server via CLI | route to [../cli-and-services/SKILL.md](../cli-and-services/SKILL.md) | ## Key rules - Use `InMemoryLightningStore` for local CPU tests and `LightningStoreClient` for an external store server. - Algorithms enqueue rollouts and update resources; runners dequeue/start rollouts and write spans; `Trainer` wires both sides. - `Runner.step` runs one rollout immediately. `Runner.iter` polls store queues until stopped or exhausted. - `Trainer.dev` is the safest first dry-run because it exercises the runner/store/trainer infrastructure with a lightweight baseline algorithm. - `RolloutConfig` controls timeout, unresponsive detection, max attempts, and retry conditions. ## Minimal store loop ```python import agentlightning as agl store = agl.InMemoryLightningStore() resources_update = await store.add_resources({ "prompt_template": agl.PromptTemplate(template="Task: {task}", engine="f-string") }) rollout = await store.enqueue_rollout("hello", resources_id=resources_update.resources_id) attempted = await store.dequeue_rollout(worker_id="worker-1") ``` Run `scripts/store_status_smoke.py` for a self-contained status/resource/span check. ## Boundary This sub-skill owns the control plane and training loop. Agent implementation details route to [agent-authoring](../agent-authoring/SKILL.md). Trace span semantics and adapters route to [tracing-and-instrumentation](../tracing-and-instrumentation/SKILL.md). CLI/server commands route to [cli-and-services](../cli-and-services/SKILL.md). Example-specific optional dependencies route to [examples-and-recipes](../examples-and-recipes/SKILL.md).