# This file was auto-generated by Fern from our API Definition.
import typing
from json.decoder import JSONDecodeError
from ..core.api_error import ApiError
from ..core.client_wrapper import AsyncClientWrapper, SyncClientWrapper
from ..core.http_response import AsyncHttpResponse, HttpResponse
from ..core.jsonable_encoder import jsonable_encoder
from ..core.request_options import RequestOptions
from ..core.unchecked_base_model import construct_type
from ..errors.internal_server_error import InternalServerError
from ..types.ml_backend import MlBackend
from .types.create_ml_request_auth_method import CreateMlRequestAuthMethod
from .types.list_model_versions_ml_response import ListModelVersionsMlResponse
from .types.update_ml_request_auth_method import UpdateMlRequestAuthMethod
# this is used as the default value for optional parameters
OMIT = typing.cast(typing.Any, ...)
class RawMlClient:
def __init__(self, *, client_wrapper: SyncClientWrapper):
self._client_wrapper = client_wrapper
def list(
self, *, project: typing.Optional[int] = None, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[typing.List[MlBackend]]:
"""
List all configured ML backends for a specific project by ID.
Use the following cURL command:
```bash
curl http://localhost:8000/api/ml?project={project_id} -H 'Authorization: Token abc123'
Parameters
----------
project : typing.Optional[int]
Project ID
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[typing.List[MlBackend]]
"""
_response = self._client_wrapper.httpx_client.request(
"api/ml/",
method="GET",
params={
"project": project,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.List[MlBackend],
construct_type(
type_=typing.List[MlBackend], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def create(
self,
*,
auth_method: typing.Optional[CreateMlRequestAuthMethod] = OMIT,
basic_auth_pass: typing.Optional[str] = OMIT,
basic_auth_user: typing.Optional[str] = OMIT,
description: typing.Optional[str] = OMIT,
extra_params: typing.Optional[typing.Dict[str, typing.Any]] = OMIT,
is_interactive: typing.Optional[bool] = OMIT,
project: typing.Optional[int] = OMIT,
timeout: typing.Optional[int] = OMIT,
title: typing.Optional[str] = OMIT,
url: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[MlBackend]:
"""
Add an ML backend to a project using the Label Studio UI or by sending a POST request using the following cURL
command:
```bash
curl -X POST -H 'Content-type: application/json' http://localhost:8000/api/ml -H 'Authorization: Token abc123'\\
--data '{"url": "http://localhost:9090", "project": {project_id}}'
Parameters
----------
auth_method : typing.Optional[CreateMlRequestAuthMethod]
Auth method
basic_auth_pass : typing.Optional[str]
Basic auth password
basic_auth_user : typing.Optional[str]
Basic auth user
description : typing.Optional[str]
Description
extra_params : typing.Optional[typing.Dict[str, typing.Any]]
Extra parameters
is_interactive : typing.Optional[bool]
Is interactive
project : typing.Optional[int]
Project ID
timeout : typing.Optional[int]
Response model timeout
title : typing.Optional[str]
Title
url : typing.Optional[str]
ML backend URL
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[MlBackend]
"""
_response = self._client_wrapper.httpx_client.request(
"api/ml/",
method="POST",
json={
"auth_method": auth_method,
"basic_auth_pass": basic_auth_pass,
"basic_auth_user": basic_auth_user,
"description": description,
"extra_params": extra_params,
"is_interactive": is_interactive,
"project": project,
"timeout": timeout,
"title": title,
"url": url,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
MlBackend,
construct_type(
type_=MlBackend, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def get(self, id: int, *, request_options: typing.Optional[RequestOptions] = None) -> HttpResponse[MlBackend]:
"""
Get details about a specific ML backend connection by ID. For example, make a GET request using the
following cURL command:
```bash
curl http://localhost:8000/api/ml/{ml_backend_ID} -H 'Authorization: Token abc123'
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[MlBackend]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}",
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
MlBackend,
construct_type(
type_=MlBackend, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def delete(self, id: int, *, request_options: typing.Optional[RequestOptions] = None) -> HttpResponse[None]:
"""
Remove an existing ML backend connection by ID. For example, use the
following cURL command:
```bash
curl -X DELETE http://localhost:8000/api/ml/{ml_backend_ID} -H 'Authorization: Token abc123'
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[None]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}",
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return HttpResponse(response=_response, data=None)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def update(
self,
id: int,
*,
auth_method: typing.Optional[UpdateMlRequestAuthMethod] = OMIT,
basic_auth_pass: typing.Optional[str] = OMIT,
basic_auth_user: typing.Optional[str] = OMIT,
description: typing.Optional[str] = OMIT,
extra_params: typing.Optional[typing.Dict[str, typing.Any]] = OMIT,
is_interactive: typing.Optional[bool] = OMIT,
project: typing.Optional[int] = OMIT,
timeout: typing.Optional[int] = OMIT,
title: typing.Optional[str] = OMIT,
url: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[MlBackend]:
"""
Update ML backend parameters using the Label Studio UI or by sending a PATCH request using the following cURL command:
```bash
curl -X PATCH -H 'Content-type: application/json' http://localhost:8000/api/ml/{ml_backend_ID} -H 'Authorization: Token abc123'\\
--data '{"url": "http://localhost:9091"}'
Parameters
----------
id : int
auth_method : typing.Optional[UpdateMlRequestAuthMethod]
Auth method
basic_auth_pass : typing.Optional[str]
Basic auth password
basic_auth_user : typing.Optional[str]
Basic auth user
description : typing.Optional[str]
Description
extra_params : typing.Optional[typing.Dict[str, typing.Any]]
Extra parameters
is_interactive : typing.Optional[bool]
Is interactive
project : typing.Optional[int]
Project ID
timeout : typing.Optional[int]
Response model timeout
title : typing.Optional[str]
Title
url : typing.Optional[str]
ML backend URL
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[MlBackend]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}",
method="PATCH",
json={
"auth_method": auth_method,
"basic_auth_pass": basic_auth_pass,
"basic_auth_user": basic_auth_user,
"description": description,
"extra_params": extra_params,
"is_interactive": is_interactive,
"project": project,
"timeout": timeout,
"title": title,
"url": url,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
MlBackend,
construct_type(
type_=MlBackend, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def predict_interactive(
self,
id: int,
*,
task: int,
context: typing.Optional[typing.Any] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[None]:
"""
Send a request to the machine learning backend set up to be used for interactive preannotations to retrieve a
predicted region based on annotator input.
See [set up machine learning](https://labelstud.io/guide/ml.html#Get-interactive-preannotations) for more.
Parameters
----------
id : int
A unique integer value identifying this ML backend.
task : int
ID of task to annotate
context : typing.Optional[typing.Any]
Context for ML model
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[None]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/interactive-annotating",
method="POST",
json={
"context": context,
"task": task,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return HttpResponse(response=_response, data=None)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def predict_all_tasks(
self,
id: int,
*,
batch_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[None]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Create predictions for all tasks using a specific ML backend so that you can set up an active learning strategy based on the confidence or uncertainty scores associated with the predictions. Creating predictions requires a Label Studio ML backend set up and configured for your project.
See [Set up machine learning](https://labelstud.io/guide/ml.html) for more details about a Label Studio ML backend.
Reference the ML backend ID in the path of this API call. Get the ML backend ID by [listing the ML backends for a project](https://labelstud.io/api/#operation/api_ml_list).
Parameters
----------
id : int
A unique integer value identifying this ML backend.
batch_size : typing.Optional[int]
Computed number of tasks without predictions that the ML backend needs to predict.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[None]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/predict",
method="POST",
params={
"batch_size": batch_size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return HttpResponse(response=_response, data=None)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def train(
self,
id: int,
*,
use_ground_truth: typing.Optional[bool] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[None]:
"""
After you add an ML backend, call this API with the ML backend ID to start training with
already-labeled tasks.
Get the ML backend ID by [listing the ML backends for a project](https://labelstud.io/api/#operation/api_ml_list).
Parameters
----------
id : int
A unique integer value identifying this ML backend.
use_ground_truth : typing.Optional[bool]
Whether to include ground truth annotations in training
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[None]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/train",
method="POST",
json={
"use_ground_truth": use_ground_truth,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return HttpResponse(response=_response, data=None)
if _response.status_code == 500:
raise InternalServerError(
headers=dict(_response.headers),
body=typing.cast(
typing.Any,
construct_type(
type_=typing.Any, # type: ignore
object_=_response.json(),
),
),
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def list_model_versions(
self, id: int, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[ListModelVersionsMlResponse]:
"""
Get available versions of the model.
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[ListModelVersionsMlResponse]
List of available versions.
"""
_response = self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/versions",
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
ListModelVersionsMlResponse,
construct_type(
type_=ListModelVersionsMlResponse, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
class AsyncRawMlClient:
def __init__(self, *, client_wrapper: AsyncClientWrapper):
self._client_wrapper = client_wrapper
async def list(
self, *, project: typing.Optional[int] = None, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[typing.List[MlBackend]]:
"""
List all configured ML backends for a specific project by ID.
Use the following cURL command:
```bash
curl http://localhost:8000/api/ml?project={project_id} -H 'Authorization: Token abc123'
Parameters
----------
project : typing.Optional[int]
Project ID
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[typing.List[MlBackend]]
"""
_response = await self._client_wrapper.httpx_client.request(
"api/ml/",
method="GET",
params={
"project": project,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.List[MlBackend],
construct_type(
type_=typing.List[MlBackend], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def create(
self,
*,
auth_method: typing.Optional[CreateMlRequestAuthMethod] = OMIT,
basic_auth_pass: typing.Optional[str] = OMIT,
basic_auth_user: typing.Optional[str] = OMIT,
description: typing.Optional[str] = OMIT,
extra_params: typing.Optional[typing.Dict[str, typing.Any]] = OMIT,
is_interactive: typing.Optional[bool] = OMIT,
project: typing.Optional[int] = OMIT,
timeout: typing.Optional[int] = OMIT,
title: typing.Optional[str] = OMIT,
url: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[MlBackend]:
"""
Add an ML backend to a project using the Label Studio UI or by sending a POST request using the following cURL
command:
```bash
curl -X POST -H 'Content-type: application/json' http://localhost:8000/api/ml -H 'Authorization: Token abc123'\\
--data '{"url": "http://localhost:9090", "project": {project_id}}'
Parameters
----------
auth_method : typing.Optional[CreateMlRequestAuthMethod]
Auth method
basic_auth_pass : typing.Optional[str]
Basic auth password
basic_auth_user : typing.Optional[str]
Basic auth user
description : typing.Optional[str]
Description
extra_params : typing.Optional[typing.Dict[str, typing.Any]]
Extra parameters
is_interactive : typing.Optional[bool]
Is interactive
project : typing.Optional[int]
Project ID
timeout : typing.Optional[int]
Response model timeout
title : typing.Optional[str]
Title
url : typing.Optional[str]
ML backend URL
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[MlBackend]
"""
_response = await self._client_wrapper.httpx_client.request(
"api/ml/",
method="POST",
json={
"auth_method": auth_method,
"basic_auth_pass": basic_auth_pass,
"basic_auth_user": basic_auth_user,
"description": description,
"extra_params": extra_params,
"is_interactive": is_interactive,
"project": project,
"timeout": timeout,
"title": title,
"url": url,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
MlBackend,
construct_type(
type_=MlBackend, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def get(
self, id: int, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[MlBackend]:
"""
Get details about a specific ML backend connection by ID. For example, make a GET request using the
following cURL command:
```bash
curl http://localhost:8000/api/ml/{ml_backend_ID} -H 'Authorization: Token abc123'
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[MlBackend]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}",
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
MlBackend,
construct_type(
type_=MlBackend, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def delete(
self, id: int, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[None]:
"""
Remove an existing ML backend connection by ID. For example, use the
following cURL command:
```bash
curl -X DELETE http://localhost:8000/api/ml/{ml_backend_ID} -H 'Authorization: Token abc123'
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[None]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}",
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return AsyncHttpResponse(response=_response, data=None)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def update(
self,
id: int,
*,
auth_method: typing.Optional[UpdateMlRequestAuthMethod] = OMIT,
basic_auth_pass: typing.Optional[str] = OMIT,
basic_auth_user: typing.Optional[str] = OMIT,
description: typing.Optional[str] = OMIT,
extra_params: typing.Optional[typing.Dict[str, typing.Any]] = OMIT,
is_interactive: typing.Optional[bool] = OMIT,
project: typing.Optional[int] = OMIT,
timeout: typing.Optional[int] = OMIT,
title: typing.Optional[str] = OMIT,
url: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[MlBackend]:
"""
Update ML backend parameters using the Label Studio UI or by sending a PATCH request using the following cURL command:
```bash
curl -X PATCH -H 'Content-type: application/json' http://localhost:8000/api/ml/{ml_backend_ID} -H 'Authorization: Token abc123'\\
--data '{"url": "http://localhost:9091"}'
Parameters
----------
id : int
auth_method : typing.Optional[UpdateMlRequestAuthMethod]
Auth method
basic_auth_pass : typing.Optional[str]
Basic auth password
basic_auth_user : typing.Optional[str]
Basic auth user
description : typing.Optional[str]
Description
extra_params : typing.Optional[typing.Dict[str, typing.Any]]
Extra parameters
is_interactive : typing.Optional[bool]
Is interactive
project : typing.Optional[int]
Project ID
timeout : typing.Optional[int]
Response model timeout
title : typing.Optional[str]
Title
url : typing.Optional[str]
ML backend URL
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[MlBackend]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}",
method="PATCH",
json={
"auth_method": auth_method,
"basic_auth_pass": basic_auth_pass,
"basic_auth_user": basic_auth_user,
"description": description,
"extra_params": extra_params,
"is_interactive": is_interactive,
"project": project,
"timeout": timeout,
"title": title,
"url": url,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
MlBackend,
construct_type(
type_=MlBackend, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def predict_interactive(
self,
id: int,
*,
task: int,
context: typing.Optional[typing.Any] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[None]:
"""
Send a request to the machine learning backend set up to be used for interactive preannotations to retrieve a
predicted region based on annotator input.
See [set up machine learning](https://labelstud.io/guide/ml.html#Get-interactive-preannotations) for more.
Parameters
----------
id : int
A unique integer value identifying this ML backend.
task : int
ID of task to annotate
context : typing.Optional[typing.Any]
Context for ML model
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[None]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/interactive-annotating",
method="POST",
json={
"context": context,
"task": task,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return AsyncHttpResponse(response=_response, data=None)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def predict_all_tasks(
self,
id: int,
*,
batch_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[None]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Create predictions for all tasks using a specific ML backend so that you can set up an active learning strategy based on the confidence or uncertainty scores associated with the predictions. Creating predictions requires a Label Studio ML backend set up and configured for your project.
See [Set up machine learning](https://labelstud.io/guide/ml.html) for more details about a Label Studio ML backend.
Reference the ML backend ID in the path of this API call. Get the ML backend ID by [listing the ML backends for a project](https://labelstud.io/api/#operation/api_ml_list).
Parameters
----------
id : int
A unique integer value identifying this ML backend.
batch_size : typing.Optional[int]
Computed number of tasks without predictions that the ML backend needs to predict.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[None]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/predict",
method="POST",
params={
"batch_size": batch_size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return AsyncHttpResponse(response=_response, data=None)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def train(
self,
id: int,
*,
use_ground_truth: typing.Optional[bool] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[None]:
"""
After you add an ML backend, call this API with the ML backend ID to start training with
already-labeled tasks.
Get the ML backend ID by [listing the ML backends for a project](https://labelstud.io/api/#operation/api_ml_list).
Parameters
----------
id : int
A unique integer value identifying this ML backend.
use_ground_truth : typing.Optional[bool]
Whether to include ground truth annotations in training
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[None]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/train",
method="POST",
json={
"use_ground_truth": use_ground_truth,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
return AsyncHttpResponse(response=_response, data=None)
if _response.status_code == 500:
raise InternalServerError(
headers=dict(_response.headers),
body=typing.cast(
typing.Any,
construct_type(
type_=typing.Any, # type: ignore
object_=_response.json(),
),
),
)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def list_model_versions(
self, id: int, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[ListModelVersionsMlResponse]:
"""
Get available versions of the model.
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[ListModelVersionsMlResponse]
List of available versions.
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/ml/{jsonable_encoder(id)}/versions",
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
ListModelVersionsMlResponse,
construct_type(
type_=ListModelVersionsMlResponse, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)