## List History `gpu_droplets.autoscale.list_history(strautoscale_pool_id, AutoscaleListHistoryParams**kwargs) -> AutoscaleListHistoryResponse` **get** `/v2/droplets/autoscale/{autoscale_pool_id}/history` To list all of the scaling history events of an autoscale pool, send a GET request to `/v2/droplets/autoscale/$AUTOSCALE_POOL_ID/history`. The response body will be a JSON object with a key of `history`. This will be set to an array containing objects each representing a history event. ### Parameters - **autoscale\_pool\_id:** `str` - **page:** `int` Which 'page' of paginated results to return. - **per\_page:** `int` Number of items returned per page ### Returns - `class AutoscaleListHistoryResponse` - **meta:** `MetaProperties` Information about the response itself. - **history:** `Optional[List[History]]` - **created\_at:** `datetime` The creation time of the history event in ISO8601 combined date and time format. - **current\_instance\_count:** `int` The current number of Droplets in the autoscale pool. - **desired\_instance\_count:** `int` The target number of Droplets for the autoscale pool after the scaling event. - **history\_event\_id:** `str` The unique identifier of the history event. - **reason:** `Literal["CONFIGURATION_CHANGE", "SCALE_UP", "SCALE_DOWN"]` The reason for the scaling event. - `"CONFIGURATION_CHANGE"` - `"SCALE_UP"` - `"SCALE_DOWN"` - **status:** `Literal["in_progress", "success", "error"]` The status of the scaling event. - `"in_progress"` - `"success"` - `"error"` - **updated\_at:** `datetime` The last updated time of the history event in ISO8601 combined date and time format. - **links:** `Optional[PageLinks]` ### Example ```python from do_gradientai import GradientAI client = GradientAI() response = client.gpu_droplets.autoscale.list_history( autoscale_pool_id="0d3db13e-a604-4944-9827-7ec2642d32ac", ) print(response.meta) ```