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entities Package

  • Reference

Contains entities and SDK objects for Azure Machine Learning SDKv2.

Main areas include managing compute targets, creating/managing workspaces and jobs, and submitting/accessing model, runs and run output/logging etc.

Classes

APIKeyConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A generic connection for any API key-based service.
AadCredentialConfiguration

Azure Active Directory Credential Configuration
AccessKeyConfiguration

Access Key Credentials.
AccountKeyConfiguration
AlertNotification

Alert notification configuration for monitoring jobs
AmlCompute

AzureML Compute resource.
AmlComputeNodeInfo

Compute node information related to AmlCompute.
AmlComputeSshSettings

SSH settings to access a AML compute target.
AmlTokenConfiguration

AzureML Token identity configuration.
ApiKeyConfiguration

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Api Key Credentials.
Asset

Base class for asset.

This class should not be instantiated directly. Instead, use one of its subclasses.
AssignedUserConfiguration

Settings to create a compute resource on behalf of another user.
AutoPauseSettings

Auto pause settings for Synapse Spark compute.
AutoScaleSettings

Auto-scale settings for Synapse Spark compute.
AzureAISearchConfig

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Config class for creating an Azure AI Search index.
AzureAISearchConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Connection that is specifically designed for handling connections to Azure AI Search.
AzureAIServicesConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Connection geared towards Azure AI services.
AzureBlobDatastore

Azure blob storage that is linked to an Azure ML workspace.
AzureBlobStoreConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A connection to an Azure Blob Store.
AzureContentSafetyConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Connection geared towards a Azure Content Safety service.
AzureDataLakeGen1Datastore

Azure Data Lake aka Gen 1 datastore that is linked to an Azure ML workspace.
AzureDataLakeGen2Datastore

Azure data lake gen 2 that is linked to an Azure ML workspace.
AzureFileDatastore

Azure file share that is linked to an Azure ML workspace.
AzureMLBatchInferencingServer

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Azure ML batch inferencing configurations.
AzureMLOnlineInferencingServer

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Azure ML online inferencing configurations.
AzureOpenAIConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Connection that is specifically designed for handling connections to Azure Open AI.
AzureOpenAIDeployment

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Azure OpenAI Deployment Information.

Readonly variables are only populated by the server, and will be ignored when sending a request.

ivar name: The deployment name.

vartype name: str

ivar model_name: The name of the model to deploy.

vartype model_name: str

ivar model_version: The model version to deploy.

vartype model_version: str

ivar connection_name: The name of the connection to deploy to.

vartype connection_name: str

ivar target_url: The target URL of the AOAI resource for the deployment.

vartype target_url: str

ivar id: The ARM resource id of the deployment.

vartype id: str

ivar properties: Properties of the deployment.

vartype properties: dict[str, str]

ivar tags: Tags of the deployment.

vartype tags: dict[str, str]

ivar system_data: System data of the deployment.

vartype system_data: ~azure.ai.ml.entities.SystemData
AzureSpeechServicesConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Connection geared towards an Azure Speech service.
BaseEnvironment

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Base environment type.

All required parameters must be populated in order to send to Azure.
BaselineDataRange

Baseline data range for monitoring.

This class is used when initializing a data_window for a ReferenceData object. For trailing input, set lookback_window_size and lookback_window_offset to a desired value. For static input, set window_start and window_end to a desired value.
BatchDeployment

Batch endpoint deployment entity.
BatchEndpoint

Batch endpoint entity.
BatchJob

Batch jobs that are created with batch deployments/endpoints invocation.

This class shouldn't be instantiated directly. Instead, it is used as the return type of batch deployment/endpoint invocation and job listing.
BatchRetrySettings

Retry settings for batch deployment.
BuildContext

Docker build context for Environment.
CategoricalDriftMetrics

Categorical Drift Metrics
CertificateConfiguration
CodeConfiguration

Code configuration for a scoring job.
Command

Base class for command node, used for command component version consumption.

You should not instantiate this class directly. Instead, you should create it using the builder function: command().
CommandComponent

Command component version, used to define a Command Component or Job.
CommandJob

Command job.

Note

For sweep jobs, inputs, outputs, and parameters are accessible as environment variables using the prefix

AZUREML_PARAMETER_. For example, if you have a parameter named "input_data", you can access it as

AZUREML_PARAMETER_input_data.
CommandJobLimits

Limits for Command Jobs.
Component

Base class for component version, used to define a component. Can't be instantiated directly.
Compute

Base class for compute resources.

This class should not be instantiated directly. Instead, use one of its subclasses.
ComputeConfiguration

Compute resource configuration
ComputeInstance

Compute Instance resource.
ComputeInstanceSshSettings

Credentials for an administrator user account to SSH into the compute node.

Can only be configured if ssh_public_access_enabled is set to true on compute resource.
ComputeRuntime

Spark compute runtime configuration.
ComputeSchedules

Compute schedules.
ComputeStartStopSchedule

Schedules for compute start or stop scenario.
ContainerRegistryCredential

Key for ACR associated with given workspace.
CronTrigger

Cron Trigger for a job schedule.
CustomApplications

Specifies the custom service application configuration.
CustomInferencingServer

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Custom inferencing configurations.
CustomMonitoringMetricThreshold

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Feature attribution drift metric threshold
CustomMonitoringSignal

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Custom monitoring signal.
CustomerManagedKey

Key vault details for encrypting data with customer-managed keys.
Data

Data for training and scoring.
DataAsset

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Data Asset entity
DataCollector

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Data Capture deployment entity.
DataColumn

A dataframe column
DataDriftMetricThreshold

Data drift metric threshold
DataDriftSignal

Data drift signal.
DataImport

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Data asset with a creating data import job.
DataQualityMetricThreshold

Data quality metric threshold
DataQualityMetricsCategorical

Data Quality Categorical Metrics
DataQualityMetricsNumerical

Data Quality Numerical Metrics
DataQualitySignal

Data quality signal
DataSegment

Data segment for monitoring.
Datastore

Datastore of an Azure ML workspace, abstract class.
DefaultScaleSettings

Default scale settings.
Deployment

Endpoint Deployment base class.

Endpoint Deployment base class.

Constructor of Endpoint Deployment base class.
DeploymentCollection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Collection entity
DiagnoseRequestProperties

DiagnoseRequestProperties.
DiagnoseResponseResult

DiagnoseResponseResult.
DiagnoseResponseResultValue

DiagnoseResponseResultValue.
DiagnoseResult

Result of Diagnose.
DiagnoseWorkspaceParameters

Parameters to diagnose a workspace.
Endpoint

Endpoint base class.

Endpoint base class.

Constructor for Endpoint base class.
EndpointAadToken

Endpoint aad token.

Constructor for Endpoint aad token.
EndpointAuthKeys

Keys for endpoint authentication.

Constructor for keys for endpoint authentication.
EndpointAuthToken

Endpoint authentication token.

Constuctor for Endpoint authentication token.
EndpointConnection

Private Endpoint Connection related to a workspace private endpoint.
EndpointsSettings

Specifies an endpoint configuration for a Custom Application.
Environment

Environment for training.
FADProductionData

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Feature Attribution Production Data
Feature
FeatureAttributionDriftMetricThreshold

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Feature attribution drift metric threshold
FeatureAttributionDriftSignal

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Feature attribution drift signal
FeatureSet

Feature Set
FeatureSetBackfillMetadata

Feature Set Backfill Metadata
FeatureSetBackfillRequest

Feature Set Backfill Request
FeatureSetMaterializationMetadata

Feature Set Materialization Metadata
FeatureSetSpecification

Feature Set Specification
FeatureStore

Feature Store
FeatureStoreEntity

Feature Store Entity
FeatureStoreSettings

Feature Store Settings
FeatureWindow

Feature window :keyword feature_window_end: Specifies the feature window end time. :paramtype feature_window_end: ~datetime.datetime :keyword feature_window_start: Specifies the feature window start time. :paramtype feature_window_start: ~datetime.datetime
FixedInputData
FqdnDestination

Class representing a FQDN outbound rule.

Creating a FqdnDestination outbound rule object.


   from azure.ai.ml.entities import FqdnDestination

   # Example FQDN rule
   pypirule = FqdnDestination(name="rulename", destination="pypi.org")
GenerationSafetyQualityMonitoringMetricThreshold

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Generation safety quality metric threshold
GenerationSafetyQualitySignal

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Generation Safety Quality monitoring signal.
GenerationTokenStatisticsMonitorMetricThreshold

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Generation token statistics metric threshold definition.

All required parameters must be populated in order to send to Azure.
GenerationTokenStatisticsSignal

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Generation token statistics signal definition.
GitSource

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Config class for creating an ML index from files located in a git repository.
Hub

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Hub is a special type of workspace that acts as a parent and resource container for lightweight child workspaces called projects. Resources like the hub's storage account, key vault, and container registry are shared by all child projects.

As a type of workspace, hub management is controlled by an MLClient's workspace operations.

Creating a Hub object.


   from azure.ai.ml.entities import Hub

   ws = Hub(name="sample-ws", location="eastus", description="a sample workspace hub object")
IdentityConfiguration

Identity configuration used to represent identity property on compute, endpoint, and registry resources.
ImageMetadata

Metadata about the operating system image for the compute instance.
ImageSettings

Specifies an image configuration for a Custom Application.
ImportDataSchedule

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

ImportDataSchedule object.
Index

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Index asset.
IndexDataSource

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Base class for configs that define data that will be processed into an ML index. This class should not be instantiated directly. Use one of its child classes instead.
InputPort
IntellectualProperty

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Intellectual property settings definition.
IsolationMode

IsolationMode for the workspace managed network.
Job

Base class for jobs.

This class should not be instantiated directly. Instead, use one of its subclasses.
JobResourceConfiguration

Job resource configuration class, inherited and extended functionalities from ResourceConfiguration.
JobSchedule

Class for managing job schedules.
JobService

Basic job service configuration for backward compatibility.

This class is not intended to be used directly. Instead, use one of its subclasses specific to your job type.
JupyterLabJobService

JupyterLab job service configuration.
KubernetesCompute

Kubernetes Compute resource.
KubernetesOnlineDeployment

Kubernetes Online endpoint deployment entity.

Kubernetes Online endpoint deployment entity.

Constructor for Kubernetes Online endpoint deployment entity.
KubernetesOnlineEndpoint

K8s Online endpoint entity.

K8s Online endpoint entity.

Constructor for K8s Online endpoint entity.
LlmData

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

LLM Request Response Data
LocalSource

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Config class for creating an ML index from a collection of local files.
ManagedIdentityConfiguration

Managed Identity credential configuration.
ManagedNetwork

Managed Network settings for a workspace.

Creating a ManagedNetwork object with one of each rule type.


   from azure.ai.ml.entities import (
       Workspace,
       ManagedNetwork,
       IsolationMode,
       ServiceTagDestination,
       PrivateEndpointDestination,
       FqdnDestination,
   )

   # Example private endpoint outbound to a blob
   blobrule = PrivateEndpointDestination(
       name="blobrule",
       service_resource_id="/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.Storage/storageAccounts/storage-name",
       subresource_target="blob",
       spark_enabled=False,
   )

   # Example service tag rule
   datafactoryrule = ServiceTagDestination(
       name="datafactory", service_tag="DataFactory", protocol="TCP", port_ranges="80, 8080-8089"
   )

   # Example FQDN rule
   pypirule = FqdnDestination(name="pypirule", destination="pypi.org")

   network = ManagedNetwork(
       isolation_mode=IsolationMode.ALLOW_ONLY_APPROVED_OUTBOUND,
       outbound_rules=[blobrule, datafactoryrule, pypirule],
   )

   # Workspace configuration
   ws = Workspace(name="ws-name", location="eastus", managed_network=network)
ManagedNetworkProvisionStatus

ManagedNetworkProvisionStatus.
ManagedOnlineDeployment

Managed Online endpoint deployment entity.

Managed Online endpoint deployment entity.

Constructor for Managed Online endpoint deployment entity.
ManagedOnlineEndpoint

Managed Online endpoint entity.

Managed Online endpoint entity.

Constructor for Managed Online endpoint entity.
MarketplacePlan

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
MarketplaceSubscription

Marketplace Subscription Definition.

Readonly variables are only populated by the server, and will be ignored when sending a request.

All required parameters must be populated in order to send to server.
MaterializationComputeResource

Materialization Compute resource
MaterializationSettings

Defines materialization settings.
MaterializationStore

Materialization Store
MicrosoftOneLakeConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A connection to a Microsoft One Lake. Connections of this type are further specified by their artifact class type, although the number of artifact classes is currently limited.
Model

Model for training and scoring.
ModelBatchDeployment

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Job Definition entity.
ModelBatchDeploymentSettings

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Model Batch Deployment Settings entity.
ModelConfiguration

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

ModelConfiguration.
ModelPackage

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Model package.
ModelPackageInput

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Model package input.
ModelPerformanceClassificationThresholds

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
ModelPerformanceMetricThreshold

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
ModelPerformanceRegressionThresholds

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
ModelPerformanceSignal

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Model performance signal.
MonitorDefinition

Monitor definition
MonitorFeatureFilter

Monitor feature filter
MonitorInputData

Monitor input data.
MonitorSchedule

Monitor schedule.
MonitoringTarget

Monitoring target.
NetworkSettings

Network settings for a compute resource. If the workspace and VNet are in different resource groups, please provide the full URI for subnet and leave vnet_name as None.
NoneCredentialConfiguration

None Credential Configuration. In many uses cases, the presence of this credential configuration indicates that the user's Entra ID will be implicitly used instead of any other form of authentication.
NotebookAccessKeys

Key for notebook resource associated with given workspace.
Notification

Configuration for notification.
NumericalDriftMetrics

Numerical Drift Metrics
OneLakeArtifact

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

OneLake artifact (data source) backing the OneLake workspace.
OneLakeConnectionArtifact

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Artifact class used by the Connection subclass known as a MicrosoftOneLakeConnection. Supplying this class further specifies the connection as a Lake House connection.
OneLakeDatastore

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

OneLake datastore that is linked to an Azure ML workspace.
OnlineDeployment

Online endpoint deployment entity.

Online endpoint deployment entity.

Constructor for Online endpoint deployment entity
OnlineEndpoint

Online endpoint entity.

Online endpoint entity.

Constructor for an Online endpoint entity.
OnlineRequestSettings

Request Settings entity.
OnlineScaleSettings

Scale settings for online deployment.
OpenAIConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A connection geared towards direct connections to Open AI. Not to be confused with the AzureOpenAIWorkspaceConnection, which is for Azure's Open AI services.
OutboundRule

Base class for Outbound Rules, cannot be instantiated directly. Please see FqdnDestination, PrivateEndpointDestination, and ServiceTagDestination objects to create outbound rules.
PackageInputPathId

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Package input path specified with a resource ID.
PackageInputPathUrl

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Package input path specified with a url.
PackageInputPathVersion

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Package input path specified with a resource name and version.
Parallel

Base class for parallel node, used for parallel component version consumption.

You should not instantiate this class directly. Instead, you should create from builder function: parallel.
ParallelComponent

Parallel component version, used to define a parallel component.
ParallelTask

Parallel task.
ParameterizedCommand

Command component version that contains the command and supporting parameters for a Command component or job.

This class should not be instantiated directly. Instead, use the child class ~azure.ai.ml.entities.CommandComponent.
PatTokenConfiguration

Personal access token credentials.
Pipeline

Base class for pipeline node, used for pipeline component version consumption. You should not instantiate this class directly. Instead, you should use @pipeline decorator to create a pipeline node.
PipelineComponent

Pipeline component, currently used to store components in an azure.ai.ml.dsl.pipeline.
PipelineComponentBatchDeployment

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Pipeline Component Batch Deployment entity.
PipelineJob

Pipeline job.

You should not instantiate this class directly. Instead, you should use the @pipeline decorator to create a PipelineJob.

] :param compute: Compute target name of the built pipeline. Defaults to None :type compute: str :param tags: Tag dictionary. Tags can be added, removed, and updated. Defaults to None :type tags: dict[str, str] :param kwargs: A dictionary of additional configuration parameters. Defaults to None :type kwargs: dict
PipelineJobSettings

Settings of PipelineJob.
PredictionDriftMetricThreshold

Prediction drift metric threshold
PredictionDriftSignal

Prediction drift signal.
PrivateEndpoint

Private Endpoint of a workspace.
PrivateEndpointDestination

Class representing a Private Endpoint outbound rule.

Creating a PrivateEndpointDestination outbound rule object.


   from azure.ai.ml.entities import PrivateEndpointDestination

   # Example private endpoint outbound to a blob
   blobrule = PrivateEndpointDestination(
       name="blobrule",
       service_resource_id="/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.Storage/storageAccounts/storage-name",
       subresource_target="blob",
       spark_enabled=False,
   )

   # Example private endpoint outbound to an application gateway
   appGwRule = PrivateEndpointDestination(
       name="appGwRule",
       service_resource_id="/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/test-rg/providers/Microsoft.Network/applicationGateways/appgw-name",  # cspell:disable-line
       subresource_target="appGwPrivateFrontendIpIPv4",
       spark_enabled=False,
       fqdns=["contoso.com", "contoso2.com"],
   )
ProbeSettings

Settings on how to probe an endpoint.
ProductionData

Production Data
Project

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A Project is a lightweight object for orchestrating AI applications, and is parented by a hub. Unlike a standard workspace, a project does not have a variety of sub-resources directly associated with it. Instead, its parent hub managed these resources, which are then used by the project and its siblings.

As a type of workspace, project management is controlled by an MLClient's workspace operations.
QueueSettings

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Queue settings for a pipeline job.
RecurrencePattern

Recurrence pattern for a job schedule.
RecurrenceTrigger

Recurrence trigger for a job schedule.
ReferenceData

Reference Data
Registry

Azure ML registry.
RegistryRegionDetails

Details for each region a registry is in.
RequestLogging

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Request Logging deployment entity.
Resource

Base class for entity classes.

Resource is an abstract object that serves as a base for creating resources. It contains common properties and methods for all resources.

This class should not be instantiated directly. Instead, use one of its subclasses.
ResourceConfiguration

Resource configuration for a job.

This class should not be instantiated directly. Instead, use its subclasses.
ResourceRequirementsSettings

Resource requirements settings for a container.
ResourceSettings

Resource settings for a container.

This class uses Kubernetes Resource unit formats. For more information, see https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/.
RetrySettings

Parallel RetrySettings.
Route

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Route.
SasTokenConfiguration
Schedule

Schedule object used to create and manage schedules.

This class should not be instantiated directly. Instead, please use the subclasses.
ScheduleTriggerResult

Schedule trigger result returned by trigger an enabled schedule once.

This class shouldn't be instantiated directly. Instead, it is used as the return type of schedule trigger.
ScriptReference

Script reference.
SerpConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A connection geared towards a Serp service (Open source search API Service)
ServerlessComputeSettings

Settings regarding serverless compute(s) in an Azure ML workspace.
ServerlessConnection

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

A connection geared towards a MaaS endpoint (Serverless).
ServerlessEndpoint

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Serverless Endpoint Definition.

Readonly variables are only populated by the server, and will be ignored when sending a request.

All required parameters must be populated in order to send to server.

ivar name: The deployment name. Required.

vartype name: str

ivar auth_mode: Authentication mode of the endpoint.

vartype auth_mode: str

ivar model_id: The id of the model to deploy. Required.

vartype model_id: str

ivar location: Location in which to create endpoint.

vartype location: str

ivar provisioning_state: Provisioning state of the endpoint. Possible values are: "creating", "deleting", "succeeded", "failed", "updating", and "canceled".

vartype provisioning_state: str

ivar tags: Tags for the endpoint.

vartype tags: dict[str, str]

ivar properties: Properties of the endpoint.

vartype properties: dict[str, str]

ivar description: Descripton of the endpoint.

vartype description: str

ivar scoring_uri: Scoring uri of the endpoint.

vartype scoring_uri: str

ivar id: ARM resource id of the endpoint.

vartype id: str

ivar headers: Headers required to hit the endpoint.

vartype id: dict[str, str]

ivar system_data: System data of the endpoint.

vartype system_data: ~azure.ai.ml.entities.SystemData
ServerlessSparkCompute

Serverless Spark compute.
ServiceInstance

Service Instance Result.
ServicePrincipalConfiguration

Service Principal credentials configuration.
ServiceTagDestination

Class representing a Service Tag outbound rule.

Creating a ServiceTagDestination outbound rule object.


   from azure.ai.ml.entities import ServiceTagDestination

   # Example service tag rule
   datafactoryrule = ServiceTagDestination(
       name="datafactory", service_tag="DataFactory", protocol="TCP", port_ranges="80, 8080-8089"
   )

   # Example service tag rule using custom address prefixes
   customAddressPrefixesRule = ServiceTagDestination(
       name="customAddressPrefixesRule",
       address_prefixes=["168.63.129.16", "10.0.0.0/24"],
       protocol="TCP",
       port_ranges="80, 443, 8080-8089",
   )
SetupScripts

Customized setup scripts.
Spark

Base class for spark node, used for spark component version consumption.

You should not instantiate this class directly. Instead, you should create it from the builder function: spark.

]

]]
SparkComponent

Spark component version, used to define a Spark Component or Job.
SparkJob

A standalone Spark job.
SparkJobEntry

Entry for Spark job.
SparkJobEntryType

Type of Spark job entry. Possibilities are Python file entry or Scala class entry.
SparkResourceConfiguration

Compute resource configuration for Spark component or job.
SshJobService

SSH job service configuration.
StaticInputData
Sweep

Base class for sweep node.

This class should not be instantiated directly. Instead, it should be created via the builder function: sweep.

]

]]

]]

]
SynapseSparkCompute

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

SynapseSpark Compute resource.
SystemCreatedAcrAccount

Azure ML ACR account.
SystemCreatedStorageAccount
SystemData

Metadata related to the creation and most recent modification of a resource.
TargetUtilizationScaleSettings

Auto scale settings.
TensorBoardJobService

TensorBoard job service configuration.
TrailingInputData
TritonInferencingServer

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Azure ML triton inferencing configurations.
UnsupportedCompute

Unsupported compute resource.

Only used for displaying compute properties for resources not fully supported in the SDK.
Usage

AzureML resource usage.
UsageName

The usage name.
UserIdentityConfiguration

User identity configuration.
UsernamePasswordConfiguration

Username and password credentials.
ValidationResult

Represents the result of job/asset validation.

This class is used to organize and parse diagnostics from both client & server side before expose them. The result is immutable.
VirtualMachineCompute

Virtual Machine Compute resource.
VirtualMachineSshSettings

SSH settings for a virtual machine.
VmSize

Virtual Machine Size.
VolumeSettings

Specifies the Bind Mount settings for a Custom Application.
VsCodeJobService

VS Code job service configuration.
Workspace

Azure ML workspace.

Creating a Workspace object.


   from azure.ai.ml.entities import Workspace

   ws = Workspace(name="sample-ws", location="eastus", description="a sample workspace object")
WorkspaceConnection

Azure ML connection provides a secure way to store authentication and configuration information needed to connect and interact with the external resources.

Note: For connections to OpenAI, Cognitive Search, and Cognitive Services, use the respective subclasses (ex: ~azure.ai.ml.entities.OpenAIConnection) instead of instantiating this class directly.
WorkspaceKeys

Workspace Keys.

Enums

ComputePowerAction

[Required] The compute power action.
CreatedByType

The type of identity that created the resource.
DataAvailabilityStatus

DataAvailabilityStatus.
DataColumnType

Dataframe Column Type Enum
MaterializationType

Materialization Type Enum
UsageUnit

An enum describing the unit of usage measurement.