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# System Operations
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System-level operations for managing available API operations and system services within clusters. These operations provide discovery and management capabilities for the Azure Machine Learning Compute service.
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## Capabilities
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### API Operations Discovery
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Operations for discovering available API operations and their metadata, useful for building dynamic clients or documentation.
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```python { .api }
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class MachineLearningComputeOperations:
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    """Operations for general Machine Learning Compute management."""
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    def list_available_operations(
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        self,
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        custom_headers: Dict[str, str] = None,
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        raw: bool = False
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    ) -> AvailableOperations:
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        """
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        Gets all available operations for the Microsoft.MachineLearningCompute resource provider.
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        Args:
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            custom_headers (dict, optional): Headers to add to the request
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            raw (bool): Returns direct response alongside deserialized response
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        Returns:
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            AvailableOperations: Container with list of available API operations
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        Raises:
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            CloudError: Service error occurred
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        """
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```
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**Usage Example:**
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```python
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# Get all available operations
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operations = client.machine_learning_compute.list_available_operations()
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# Iterate through available operations
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for operation in operations.value:
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    print(f"Operation: {operation.name}")
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    print(f"Display Name: {operation.display.operation}")
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    print(f"Provider: {operation.display.provider}")
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    print(f"Resource: {operation.display.resource}")
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    print(f"Description: {operation.display.description}")
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    print("---")
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# Filter operations by type
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cluster_operations = [
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    op for op in operations.value 
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    if "operationalizationClusters" in op.name
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]
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print(f"Found {len(cluster_operations)} cluster operations")
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```
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## Operation Response Models
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### AvailableOperations
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Container for the list of available API operations.
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```python { .api }
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class AvailableOperations:
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    """
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    Container for available operations.
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    Attributes:
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        value (List[ResourceOperation]): List of available operations
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    """
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    value: List[ResourceOperation]
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```
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### ResourceOperation
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Individual API operation definition with metadata.
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```python { .api }
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class ResourceOperation:
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    """
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    API operation definition.
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    Attributes:
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        name (str): Operation name (e.g., "Microsoft.MachineLearningCompute/operationalizationClusters/read")
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        display (ResourceOperationDisplay): Display information for the operation
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        origin (str): The operation origin
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    """
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    name: str
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    display: ResourceOperationDisplay
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    origin: str
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```
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### ResourceOperationDisplay
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Display information for API operations, used in Azure portal and documentation.
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```python { .api }
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class ResourceOperationDisplay:
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    """
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    Display information for a resource operation.
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    Attributes:
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        provider (str): Service provider (e.g., "Microsoft Machine Learning Compute")
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        resource (str): Resource type (e.g., "Operationalization Cluster") 
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        operation (str): Operation description (e.g., "Create or Update Operationalization Cluster")
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        description (str): Detailed operation description
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    """
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    provider: str
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    resource: str
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    operation: str
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    description: str
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```
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**Usage Example:**
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```python
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# Explore operation metadata
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operations = client.machine_learning_compute.list_available_operations()
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for op in operations.value:
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    if "create" in op.display.operation.lower():
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        print(f"Create Operation: {op.name}")
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        print(f"  Resource: {op.display.resource}")
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        print(f"  Description: {op.display.description}")
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```
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## Integration with Azure Resource Manager
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The system operations integrate with Azure's Resource Manager infrastructure:
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```python
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# This operation provides the same information available through:
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# az provider operation list --namespace Microsoft.MachineLearningCompute
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operations = client.machine_learning_compute.list_available_operations()
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# Can be used to build custom tooling, documentation, or validation
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operation_names = [op.name for op in operations.value]
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print(f"Total operations: {len(operation_names)}")
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# Check if specific operations are available
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cluster_create_available = any(
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    "operationalizationClusters/write" in op.name 
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    for op in operations.value
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)
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print(f"Cluster creation available: {cluster_create_available}")
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```
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## Error Handling
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System operations use standard Azure error handling patterns:
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```python
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from azure.core.exceptions import ClientAuthenticationError, HttpResponseError
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try:
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    operations = client.machine_learning_compute.list_available_operations()
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except ClientAuthenticationError:
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    print("Authentication failed - check credentials")
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except HttpResponseError as e:
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    print(f"HTTP error: {e.status_code} - {e.message}")
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except Exception as e:
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    print(f"Unexpected error: {e}")
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```