BatchDeploymentOperations Class  
BatchDeploymentOperations.
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.
Constructor
BatchDeploymentOperations(operation_scope: OperationScope, operation_config: OperationConfig, service_client_01_2024_preview: AzureMachineLearningWorkspaces, all_operations: OperationsContainer, credentials: TokenCredential | None = None, **kwargs: Any)
		Parameters
| Name | Description | 
|---|---|
| 
		 operation_scope 
			
				Required
			 
	 | 
	
		 
				<xref:azure.ai.ml._scope_dependent_operations.OperationScope>
		 
		Scope variables for the operations classes of an MLClient object.  | 
| 
		 operation_config 
			
				Required
			 
	 | 
	
		 
				<xref:azure.ai.ml._scope_dependent_operations.OperationConfig>
		 
		Common configuration for operations classes of an MLClient object.  | 
| 
		 service_client_05_2022 
			
				Required
			 
	 | 
	
		 
				<xref:<xref:azure.ai.ml._restclient.v2022_05_01._azure_machine_learning_workspaces. AzureMachineLearningWorkspaces>>
		 
		Service client to allow end users to operate on Azure Machine Learning Workspace resources.  | 
| 
		 all_operations 
			
				Required
			 
	 | 
	
		 
				<xref:azure.ai.ml._scope_dependent_operations.OperationsContainer>
		 
		All operations classes of an MLClient object.  | 
| 
		 credentials 
	 | 
	
		
		 Credential to use for authentication. Default value: None 
			 | 
| 
		 service_client_01_2024_preview 
			
				Required
			 
	 | 
	
		 | 
Methods
| begin_create_or_update | 
					 Create or update a batch deployment.  | 
			
| begin_delete | 
					 Delete a batch deployment.  | 
			
| get | 
					 Get a deployment resource.  | 
			
| list | 
					 List a deployment resource.  | 
			
| 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 deployment.
begin_create_or_update(deployment: DeploymentType, *, skip_script_validation: bool = False, **kwargs: Any) -> LROPoller[DeploymentType]
		Parameters
| Name | Description | 
|---|---|
| 
		 deployment 
			
				Required
			 
	 | 
	
		
		 The deployment entity.  | 
Keyword-Only Parameters
| Name | Description | 
|---|---|
| 
		 skip_script_validation 
	 | 
	
		
		 If set to True, the script validation will be skipped. Defaults to False. Default value: False 
			 | 
Returns
| Type | Description | 
|---|---|
| 
					 A poller to track the operation status.  | 
		
Exceptions
| Type | Description | 
|---|---|
| 
					 Raised if BatchDeployment cannot be successfully validated. Details will be provided in the error message.  | 
			|
| 
					 Raised if BatchDeployment assets (e.g. Data, Code, Model, Environment) cannot be successfully validated. Details will be provided in the error message.  | 
			|
| 
					 Raised if BatchDeployment model cannot be successfully validated. Details will be provided in the error message.  | 
			
Examples
Create example.
   from azure.ai.ml import load_batch_deployment
   from azure.ai.ml.entities import BatchDeployment
   deployment_example = load_batch_deployment(
       source="./sdk/ml/azure-ai-ml/tests/test_configs/deployments/batch/batch_deployment_anon_env_with_image.yaml",
       params_override=[{"name": f"deployment-{randint(0, 1000)}", "endpoint_name": endpoint_example.name}],
   )
   ml_client.batch_deployments.begin_create_or_update(deployment=deployment_example, skip_script_validation=True)
begin_delete
Delete a batch deployment.
begin_delete(name: str, endpoint_name: str) -> LROPoller[None]
		Parameters
| Name | Description | 
|---|---|
| 
		 name 
			
				Required
			 
	 | 
	
		
		 Name of the batch deployment.  | 
| 
		 endpoint_name 
			
				Required
			 
	 | 
	
		
		 Name of the batch endpoint  | 
Returns
| Type | Description | 
|---|---|
| 
					 A poller to track the operation status.  | 
		
Examples
Delete example.
   ml_client.batch_deployments.begin_delete(deployment_name, endpoint_name)
get
Get a deployment resource.
get(name: str, endpoint_name: str) -> BatchDeployment
		Parameters
| Name | Description | 
|---|---|
| 
		 name 
			
				Required
			 
	 | 
	
		
		 The name of the deployment  | 
| 
		 endpoint_name 
			
				Required
			 
	 | 
	
		
		 The name of the endpoint  | 
Returns
| Type | Description | 
|---|---|
| 
					 A deployment entity  | 
		
Examples
Get example.
   ml_client.batch_deployments.get(deployment_name, endpoint_name)
list
List a deployment resource.
list(endpoint_name: str) -> ItemPaged[BatchDeployment]
		Parameters
| Name | Description | 
|---|---|
| 
		 endpoint_name 
			
				Required
			 
	 | 
	
		
		 The name of the endpoint  | 
Returns
| Type | Description | 
|---|---|
| 
					 An iterator of deployment entities  | 
		
Examples
List deployment resource example.
   ml_client.batch_deployments.list(endpoint_name)
list_jobs
List jobs under the provided batch endpoint deployment. This is only valid for batch endpoint.
list_jobs(endpoint_name: str, *, name: str | None = None) -> ItemPaged[BatchJob]
		Parameters
| Name | Description | 
|---|---|
| 
		 endpoint_name 
			
				Required
			 
	 | 
	
		
		 Name of endpoint.  | 
Keyword-Only Parameters
| Name | Description | 
|---|---|
| 
		 name 
	 | 
	
		
		 (Optional) Name of deployment. Default value: None 
			 | 
Returns
| Type | Description | 
|---|---|
| 
					 List of jobs  | 
		
Examples
List jobs example.
   ml_client.batch_deployments.list_jobs(deployment_name, endpoint_name)