BatchEndpointOperations Class

BatchEndpointOperations.

You should not instantiate this class directly. Instead, you should create an MLClient instance that instantiates it for you and attaches it as an attribute.

Inheritance
azure.ai.ml._scope_dependent_operations._ScopeDependentOperations
BatchEndpointOperations

Constructor

BatchEndpointOperations(operation_scope: azure.ai.ml._scope_dependent_operations.OperationScope, operation_config: azure.ai.ml._scope_dependent_operations.OperationConfig, service_client_05_2022: azure.ai.ml._restclient.v2022_05_01._azure_machine_learning_workspaces.AzureMachineLearningWorkspaces, all_operations: azure.ai.ml._scope_dependent_operations.OperationsContainer, credentials: Optional[azure.core.credentials.TokenCredential] = None, **kwargs: Dict)

Parameters

operation_scope
operation_config
service_client_05_2022
all_operations
credentials
default value: None

Methods

begin_create_or_update

Create or update a batch endpoint.

begin_delete

Delete a batch Endpoint.

get

Get a Endpoint resource.

invoke

Invokes the batch endpoint with the provided payload.

list

List endpoints of the workspace.

list_jobs

List jobs under the provided batch endpoint deployment. This is only valid for batch endpoint.

begin_create_or_update

Create or update a batch endpoint.

begin_create_or_update(endpoint: azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint) -> azure.core.polling._poller.LROPoller[azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint]

Parameters

endpoint
BatchEndpoint
Required

The endpoint entity.

Returns

A poller to track the operation status.

Return type

begin_delete

Delete a batch Endpoint.

begin_delete(name: str) -> azure.core.polling._poller.LROPoller[None]

Parameters

name
str
Required

Name of the batch endpoint.

Returns

A poller to track the operation status.

Return type

get

Get a Endpoint resource.

get(name: str) -> azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint

Parameters

name
str
Required

Name of the endpoint.

Returns

Endpoint object retrieved from the service.

Return type

invoke

Invokes the batch endpoint with the provided payload.

invoke(endpoint_name: str, *, deployment_name: str = None, input: azure.ai.ml.entities._inputs_outputs.input.Input = None, params_override=None, **kwargs) -> azure.ai.ml._restclient.v2020_09_01_dataplanepreview.models._models_py3.BatchJobResource

Parameters

endpoint_name
str
Required

The endpoint name.

deployment_name
<xref:Optional>[str]
Required

The name of a specific deployment to invoke. This is optional. By default requests are routed to any of the deployments according to the traffic rules.

input
<xref:Optional>[Input]
Required

An existing data asset, public uri file or folder to use with the deployment

params_override
<xref:Dict>
Required

Parameters to overwrite deployment configurations, for batch endpoints only.

Returns

The invoked batch deployment job.

Return type

<xref:BatchJobResource>

Exceptions

Raised if deployment cannot be successfully validated. Details will be provided in the error message.

Raised if BatchEndpoint assets (e.g. Data, Code, Model, Environment) cannot be successfully validated. Details will be provided in the error message.

Raised if BatchEndpoint model cannot be successfully validated. Details will be provided in the error message.

Raised if local path provided points to an empty directory.

list

List endpoints of the workspace.

list() -> azure.core.paging.ItemPaged[azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint]

Returns

A list of endpoints

Return type

list_jobs

List jobs under the provided batch endpoint deployment. This is only valid for batch endpoint.

list_jobs(endpoint_name: str) -> List[azure.ai.ml._restclient.v2020_09_01_dataplanepreview.models._models_py3.BatchJobResource]

Parameters

endpoint_name
str
Required

The endpoint name

Returns

List of jobs

Return type

list[<xref:BatchJobResource>]