# This file was auto-generated by Fern from our API Definition.
import datetime as dt
import typing
from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper
from ...core.request_options import RequestOptions
from ...types.cancel_model_run_response import CancelModelRunResponse
from ...types.model_run import ModelRun
from ...types.project_subset_enum import ProjectSubsetEnum
from .raw_client import AsyncRawRunsClient, RawRunsClient
from .types.list_runs_request_project_subset import ListRunsRequestProjectSubset
# this is used as the default value for optional parameters
OMIT = typing.cast(typing.Any, ...)
class RunsClient:
def __init__(self, *, client_wrapper: SyncClientWrapper):
self._raw_client = RawRunsClient(client_wrapper=client_wrapper)
@property
def with_raw_response(self) -> RawRunsClient:
"""
Retrieves a raw implementation of this client that returns raw responses.
Returns
-------
RawRunsClient
"""
return self._raw_client
def list(
self,
prompt_id: int,
version_id: int,
*,
ordering: typing.Optional[str] = None,
parent_model: typing.Optional[int] = None,
project: typing.Optional[int] = None,
project_subset: typing.Optional[ListRunsRequestProjectSubset] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> typing.List[ModelRun]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Get information (status, metadata, etc) about an existing inference run
Parameters
----------
prompt_id : int
version_id : int
ordering : typing.Optional[str]
Which field to use when ordering the results.
parent_model : typing.Optional[int]
The ID of the parent model for this Inference Run
project : typing.Optional[int]
The ID of the project this Inference Run makes predictions on
project_subset : typing.Optional[ListRunsRequestProjectSubset]
Defines which tasks are operated on (e.g. HasGT will only operate on tasks with a ground truth annotation, but All will operate on all records)
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[ModelRun]
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.prompts.runs.list(
prompt_id=1,
version_id=1,
)
"""
_response = self._raw_client.list(
prompt_id,
version_id,
ordering=ordering,
parent_model=parent_model,
project=project,
project_subset=project_subset,
request_options=request_options,
)
return _response.data
def create(
self,
prompt_id: int,
version_id: int,
*,
project: int,
job_id: typing.Optional[str] = OMIT,
only_missing_predictions: typing.Optional[bool] = OMIT,
organization: typing.Optional[int] = OMIT,
predictions_updated_at: typing.Optional[dt.datetime] = OMIT,
project_subset: typing.Optional[ProjectSubsetEnum] = OMIT,
total_correct_predictions: typing.Optional[int] = OMIT,
total_predictions: typing.Optional[int] = OMIT,
total_tasks: typing.Optional[int] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> ModelRun:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Run a prompt inference.
Parameters
----------
prompt_id : int
version_id : int
project : int
job_id : typing.Optional[str]
Job ID for inference job for a ModelRun e.g. Adala job ID
only_missing_predictions : typing.Optional[bool]
When true, only tasks without successful predictions for this prompt version are submitted for inference.
organization : typing.Optional[int]
predictions_updated_at : typing.Optional[dt.datetime]
project_subset : typing.Optional[ProjectSubsetEnum]
total_correct_predictions : typing.Optional[int]
total_predictions : typing.Optional[int]
total_tasks : typing.Optional[int]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
ModelRun
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.prompts.runs.create(
prompt_id=1,
version_id=1,
project=1,
)
"""
_response = self._raw_client.create(
prompt_id,
version_id,
project=project,
job_id=job_id,
only_missing_predictions=only_missing_predictions,
organization=organization,
predictions_updated_at=predictions_updated_at,
project_subset=project_subset,
total_correct_predictions=total_correct_predictions,
total_predictions=total_predictions,
total_tasks=total_tasks,
request_options=request_options,
)
return _response.data
def cancel(
self,
prompt_id: int,
version_id: int,
inference_run_id: int,
*,
request_options: typing.Optional[RequestOptions] = None,
) -> CancelModelRunResponse:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Cancel the inference run for the given api
Parameters
----------
prompt_id : int
version_id : int
inference_run_id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
CancelModelRunResponse
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.prompts.runs.cancel(
prompt_id=1,
version_id=1,
inference_run_id=1,
)
"""
_response = self._raw_client.cancel(prompt_id, version_id, inference_run_id, request_options=request_options)
return _response.data
class AsyncRunsClient:
def __init__(self, *, client_wrapper: AsyncClientWrapper):
self._raw_client = AsyncRawRunsClient(client_wrapper=client_wrapper)
@property
def with_raw_response(self) -> AsyncRawRunsClient:
"""
Retrieves a raw implementation of this client that returns raw responses.
Returns
-------
AsyncRawRunsClient
"""
return self._raw_client
async def list(
self,
prompt_id: int,
version_id: int,
*,
ordering: typing.Optional[str] = None,
parent_model: typing.Optional[int] = None,
project: typing.Optional[int] = None,
project_subset: typing.Optional[ListRunsRequestProjectSubset] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> typing.List[ModelRun]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Get information (status, metadata, etc) about an existing inference run
Parameters
----------
prompt_id : int
version_id : int
ordering : typing.Optional[str]
Which field to use when ordering the results.
parent_model : typing.Optional[int]
The ID of the parent model for this Inference Run
project : typing.Optional[int]
The ID of the project this Inference Run makes predictions on
project_subset : typing.Optional[ListRunsRequestProjectSubset]
Defines which tasks are operated on (e.g. HasGT will only operate on tasks with a ground truth annotation, but All will operate on all records)
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[ModelRun]
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.prompts.runs.list(
prompt_id=1,
version_id=1,
)
asyncio.run(main())
"""
_response = await self._raw_client.list(
prompt_id,
version_id,
ordering=ordering,
parent_model=parent_model,
project=project,
project_subset=project_subset,
request_options=request_options,
)
return _response.data
async def create(
self,
prompt_id: int,
version_id: int,
*,
project: int,
job_id: typing.Optional[str] = OMIT,
only_missing_predictions: typing.Optional[bool] = OMIT,
organization: typing.Optional[int] = OMIT,
predictions_updated_at: typing.Optional[dt.datetime] = OMIT,
project_subset: typing.Optional[ProjectSubsetEnum] = OMIT,
total_correct_predictions: typing.Optional[int] = OMIT,
total_predictions: typing.Optional[int] = OMIT,
total_tasks: typing.Optional[int] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> ModelRun:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Run a prompt inference.
Parameters
----------
prompt_id : int
version_id : int
project : int
job_id : typing.Optional[str]
Job ID for inference job for a ModelRun e.g. Adala job ID
only_missing_predictions : typing.Optional[bool]
When true, only tasks without successful predictions for this prompt version are submitted for inference.
organization : typing.Optional[int]
predictions_updated_at : typing.Optional[dt.datetime]
project_subset : typing.Optional[ProjectSubsetEnum]
total_correct_predictions : typing.Optional[int]
total_predictions : typing.Optional[int]
total_tasks : typing.Optional[int]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
ModelRun
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.prompts.runs.create(
prompt_id=1,
version_id=1,
project=1,
)
asyncio.run(main())
"""
_response = await self._raw_client.create(
prompt_id,
version_id,
project=project,
job_id=job_id,
only_missing_predictions=only_missing_predictions,
organization=organization,
predictions_updated_at=predictions_updated_at,
project_subset=project_subset,
total_correct_predictions=total_correct_predictions,
total_predictions=total_predictions,
total_tasks=total_tasks,
request_options=request_options,
)
return _response.data
async def cancel(
self,
prompt_id: int,
version_id: int,
inference_run_id: int,
*,
request_options: typing.Optional[RequestOptions] = None,
) -> CancelModelRunResponse:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Cancel the inference run for the given api
Parameters
----------
prompt_id : int
version_id : int
inference_run_id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
CancelModelRunResponse
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.prompts.runs.cancel(
prompt_id=1,
version_id=1,
inference_run_id=1,
)
asyncio.run(main())
"""
_response = await self._raw_client.cancel(
prompt_id, version_id, inference_run_id, request_options=request_options
)
return _response.data