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Returns
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RawMlClient
r r s r with_raw_responsezMlClient.with_raw_response r Nprojectrequest_optionsr r! c T | j j || }|j S )a
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
-------
typing.List[MlBackend]
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.list()
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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
-------
MlBackend
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.create()
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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
-------
MlBackend
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.get(
id=1,
)
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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
-------
None
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.delete(
id=1,
)
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| | }
|
j S )a
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
-------
MlBackend
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.update(
id=1,
)
r( r updater% r r7 r) r* r+ r, r- r. r r/ r0 r1 r! r' s r rA zMlClient.update sP Z $$++ # + +#%) + ,
~~ r contextr! taskrD c X | j j |||| }|j S )a
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
-------
None
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.predict_interactive(
id=1,
task=1,
)
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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
-------
None
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.predict_all_tasks(
id=1,
)
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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
-------
None
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.train(
id=1,
)
rQ r trainr% r r7 rR r! r' s r rU zMlClient.train s. R $$**2@Pbq*r ~~ r c T | j j || }|j S )a&
Get available versions of the model.
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
ListModelVersionsMlResponse
List of available versions.
Examples
--------
from label_studio_sdk import LabelStudio
client = LabelStudio(
api_key="YOUR_API_KEY",
)
client.ml.list_model_versions(
id=1,
)
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AsyncMlClientr c & t | | _ y r )r r r s r r zAsyncMlClient.__init__ s +>Jr r c | j S )z
Retrieves a raw implementation of this client that returns raw responses.
Returns
-------
AsyncRawMlClient
r r s r r z AsyncMlClient.with_raw_response r r Nr r r! c p K | j j || d{ }|j S 7 w)a.
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
-------
typing.List[MlBackend]
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.list()
asyncio.run(main())
r Nr# r& s r r$ zAsyncMlClient.list s8 R **//Q`/aa ~~ b !646r( r) r* r+ r, r- r. r/ r0 r1 c
K | j j ||||||||| |
| d{ } | j S 7 w)a
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
-------
MlBackend
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.create()
asyncio.run(main())
r( Nr3 r5 s
r r4 zAsyncMlClient.create+ s[ b **11# + +#%) + 2
~~
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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
-------
MlBackend
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.get(
id=1,
)
asyncio.run(main())
r6 Nr9 r; s r r: zAsyncMlClient.get s7 P **..r?.SS ~~ Trm c p K | j j || d{ }|j S 7 w)a
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
-------
None
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.delete(
id=1,
)
asyncio.run(main())
r6 Nr= r; s r r> zAsyncMlClient.delete s7 N **11"o1VV ~~ Wrm c K | j j ||||||||| |
| | d{ }
|
j S 7 w)a/
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
-------
MlBackend
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.update(
id=1,
)
asyncio.run(main())
r( Nr@ rB s r rA zAsyncMlClient.update s^ j **11 # + +#%) + 2
~~
s +A >A rC rE rD c t K | j j |||| d{ }|j S 7 w)aE
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
-------
None
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.predict_interactive(
id=1,
task=1,
)
asyncio.run(main())
rG NrH rJ s r rI z!AsyncMlClient.predict_interactiveE sC j **>> T7O ?
~~
s #868rK rL c r K | j j ||| d{ }|j S 7 w)aP
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
-------
None
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.predict_all_tasks(
id=1,
)
asyncio.run(main())
rK NrN rP s r rO z AsyncMlClient.predict_all_tasks s: p **< None:
await client.ml.train(
id=1,
)
asyncio.run(main())
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Get available versions of the model.
Parameters
----------
id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
ListModelVersionsMlResponse
List of available versions.
Examples
--------
import asyncio
from label_studio_sdk import AsyncLabelStudio
client = AsyncLabelStudio(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.ml.list_model_versions(
id=1,
)
asyncio.run(main())
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