## List

`gpu_droplets.autoscale.list(AutoscaleListParams**kwargs)  -> AutoscaleListResponse`

**get** `/v2/droplets/autoscale`

To list all autoscale pools in your team, send a GET request to `/v2/droplets/autoscale`.
The response body will be a JSON object with a key of `autoscale_pools` containing an array of autoscale pool objects.
These each contain the standard autoscale pool attributes.

### Parameters

- **name:** `str`

  The name of the autoscale pool

- **page:** `int`

  Which 'page' of paginated results to return.

- **per\_page:** `int`

  Number of items returned per page

### Returns

- `class AutoscaleListResponse`

  - **meta:** `MetaProperties`

    Information about the response itself.

  - **autoscale\_pools:** `Optional[List[AutoscalePool]]`

    - **id:** `str`

      A unique identifier for each autoscale pool instance. This is automatically generated upon autoscale pool creation.

    - **active\_resources\_count:** `int`

      The number of active Droplets in the autoscale pool.

    - **config:** `Config`

      The scaling configuration for an autoscale pool, which is how the pool scales up and down (either by resource utilization or static configuration).

      - `AutoscalePoolStaticConfig`

      - `AutoscalePoolDynamicConfig`

    - **created\_at:** `datetime`

      A time value given in ISO8601 combined date and time format that represents when the autoscale pool was created.

    - **droplet\_template:** `AutoscalePoolDropletTemplate`

    - **name:** `str`

      The human-readable name set for the autoscale pool.

    - **status:** `Literal["active", "deleting", "error"]`

      The current status of the autoscale pool.

      - `"active"`

      - `"deleting"`

      - `"error"`

    - **updated\_at:** `datetime`

      A time value given in ISO8601 combined date and time format that represents when the autoscale pool was last updated.

    - **current\_utilization:** `Optional[CurrentUtilization]`

  - **links:** `Optional[PageLinks]`

### Example

```python
from do_gradientai import GradientAI

client = GradientAI()
autoscales = client.gpu_droplets.autoscale.list()
print(autoscales.meta)
```