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

# orbitra_deploy

# `orbitra.flows.orbitra_deploy`

## Functions

### `get_deployment_meta`

```python theme={null}
get_deployment_meta(fn: Callable) -> OrbitraDeploymentMeta
```

Look up the OrbitraDeploymentMeta registered for a decorated callable.

**Args:**

* `fn`: The callable decorated with @orbitra\_deployment (a PrefectFlow after decoration).

**Returns:**

* The registered metadata for the callable.

**Raises:**

* `ValueError`: If no deployment is registered for the given callable.

### `get_registered_deployments`

```python theme={null}
get_registered_deployments() -> list[OrbitraDeploymentMeta]
```

### `orbitra_deployment`

```python theme={null}
orbitra_deployment(name: Optional[str] = None, description: Optional[str] = None, concurrency: Optional[int] = 1, schedules: Optional[List[str]] = None, enable_schedules_on_creation: bool = False, tags: Optional[List[str]] = None, flow_config: Callable[..., PrefectFlow] = prefect_flow(log_prints=True), schedules_timezone: Optional[str] = None, container_size: ContainerSizeType = None, collision_strategy: Optional[Literal['ENQUEUE', 'CANCEL_NEW']] = 'CANCEL_NEW', work_pool: Optional[str] = None, work_queue: Optional[str] = None) -> Callable[[Callable[..., Any]], PrefectFlow]
```

Decorator to register an Orbitra deployment.

This decorator records metadata of a flow so it can later be deployed.
It also automatically transforms the decorated function into a Prefect Flow
(equivalent to `@flow`).

**Args:**

* `name`: A friendly name for the deployment. Defaults to the function's
  name if not provided.
* `description`: A free-form description of the deployment. Can be used to
  document the purpose, ownership, or context of the flow.
* `concurrency`: Maximum number of concurrent runs allowed for this
  deployment. If omitted, no concurrency limit is enforced.
* `schedules`: A list of schedule definitions attached to the deployment.
  Supported formats include
  * `"cron={expr}"` for cron-based schedules.
  * `"interval={seconds}"` for fixed interval schedules.
  * Any valid RRULE string (RFC 5545) for advanced recurrence rules.
* `enable_schedules_on_creation`: If `True`, schedules are created on
  active state for new deployments. Useful for defining schedules and
  enabling them by default.
* `tags`: A list of tags to associate with the deployment. Tags can be
  used for organization, filtering, or triggering rules.
* `flow_config`: A callable returned by `prefect.flow(...)`. The
  decorated function will be wrapped with it.
* `schedules_timezone`: Timezone name to apply to all schedules for this
  deployment (e.g., `"America/Sao_Paulo"`). If omitted,
  `"America/Sao_Paulo"` will be used as the default timezone.
* `container_size`: Defines the container
  resources to be used when generating the deployment infrastructure.
  Supported presets are `"XS"`(default), `"S"`, `"M"`, and `"L"`, which map to
  * XS -> 1 GB memory / 1 CPU
  * S  -> 2 GB memory / 1 CPU
  * M  -> 4 GB memory / 2 CPUs
  * L  -> 8 GB memory / 4 CPUs

Pass `ContainerSize(memory_gb=..., cpu_cores=...)` to define bespoke resources.
Both `memory_gb` and `cpu_cores` must be integers; floats are not accepted.
Values must also stay within the Azure Container Apps limits (1-240 GB RAM,
1-31 vCPU). If not defined, the default for the work pool will be used.

* `collision_strategy`: Strategy to use when a new run is started
  while another is already running.
* `work_pool`: Override the work pool for this specific deployment.
  When omitted, the deployment uses the project's default work pool.
* `work_queue`: Pin this deployment to a specific work queue
  inside its work pool. When omitted, Prefect uses the pool's `default`
  queue. The named queue must already exist in the target work pool
  (provisioned via terraform).

**Examples:**

```python theme={null}
from prefect import flow
from orbitra.flows.orbitra_deploy import orbitra_deployment, ContainerSize

@orbitra_deployment()
def manual_flow():
    ...

@orbitra_deployment(
    name="reporting-prod",
    flow_config=flow(name="daily-reporting", log_prints=True),
    schedules=["cron=0 7 * * *"],
    tags=["prod", "finance"],
    container_size="M",
)
def generate_reports(day: str = "yesterday"):
    ...

@orbitra_deployment(
    flow_config=flow(name="etl-runner", retries=2, retry_delay_seconds=60),
    schedules=["interval=3600"],
    container_size=ContainerSize(memory_gb=16, cpu_cores=6),
)
def hourly_etl():
    ...
```

## Classes

### `ContainerSize`

### `OrbitraDeploymentMeta`