azure.mgmt.machinelearningservices.models module

exception azure.mgmt.machinelearningservices.models.MachineLearningServiceErrorException(deserialize, response, *args)[source]

Bases: msrest.exceptions.HttpOperationError

Server responsed with exception of type: ‘MachineLearningServiceError’.

Parameters

  • deserialize – A deserializer

  • response – Server response to be deserialized.

class azure.mgmt.machinelearningservices.models.AKS(*, compute_location: str = None, description: str = None, resource_id: str = None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

A Machine Learning compute based on AKS.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

  • properties (AKSProperties) – AKS properties

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.AksComputeSecrets(*, user_kube_config: str = None, admin_kube_config: str = None, image_pull_secret_name: str = None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.ComputeSecrets

Secrets related to a Machine Learning compute based on AKS.

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

Parameters

  • compute_type (str) – Required. Constant filled by server.

  • user_kube_config (str) – Content of kubeconfig file that can be used to connect to the Kubernetes cluster.

  • admin_kube_config (str) – Content of kubeconfig file that can be used to connect to the Kubernetes cluster.

  • image_pull_secret_name (str) – Image registry pull secret.

class azure.mgmt.machinelearningservices.models.AksNetworkingConfiguration(*, subnet_id: str = None, service_cidr: str = None, dns_service_ip: str = None, docker_bridge_cidr: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Advance configuration for AKS networking.

Parameters

  • subnet_id (str) – Virtual network subnet resource ID the compute nodes belong to

  • service_cidr (str) – A CIDR notation IP range from which to assign service cluster IPs. It must not overlap with any Subnet IP ranges.

  • dns_service_ip (str) – An IP address assigned to the Kubernetes DNS service. It must be within the Kubernetes service address range specified in serviceCidr.

  • docker_bridge_cidr (str) – A CIDR notation IP range assigned to the Docker bridge network. It must not overlap with any Subnet IP ranges or the Kubernetes service address range.

class azure.mgmt.machinelearningservices.models.AKSProperties(*, cluster_fqdn: str = None, agent_count: int = None, agent_vm_size: str = None, ssl_configuration=None, aks_networking_configuration=None, **kwargs)[source]

Bases: msrest.serialization.Model

AKS properties.

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

Parameters

  • cluster_fqdn (str) – Cluster full qualified domain name

  • agent_count (int) – Number of agents

  • agent_vm_size (str) – Agent virtual machine size

  • ssl_configuration (SslConfiguration) – SSL configuration

  • aks_networking_configuration (AksNetworkingConfiguration) – AKS networking configuration for vnet

Variables

system_services (list[SystemService]) – System services

class azure.mgmt.machinelearningservices.models.AmlCompute(*, compute_location: str = None, description: str = None, resource_id: str = None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

An Azure Machine Learning compute.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

  • properties (AmlComputeProperties) – AML Compute properties

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.AmlComputeNodeInformation(**kwargs)[source]

Bases: msrest.serialization.Model

Compute node information related to a AmlCompute.

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

Variables

  • node_id (str) – Node ID. ID of the compute node.

  • ip_address (str) – IP address. Public IP address of the compute node.

  • port (float) – Port. SSH port number of the node.

class azure.mgmt.machinelearningservices.models.AmlComputeNodesInformation(**kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.ComputeNodesInformation

Compute node information related to a AmlCompute.

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 Azure.

Variables
Parameters

compute_type (str) – Required. Constant filled by server.

class azure.mgmt.machinelearningservices.models.AmlComputeProperties(*, vm_size: str = None, vm_priority=None, scale_settings=None, user_account_credentials=None, subnet=None, **kwargs)[source]

Bases: msrest.serialization.Model

AML Compute properties.

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

Parameters

  • vm_size (str) – Virtual Machine Size

  • vm_priority (str or VmPriority) – Virtual Machine priority. Possible values include: ‘Dedicated’, ‘LowPriority’

  • scale_settings (ScaleSettings) – Scale settings for AML Compute

  • user_account_credentials (UserAccountCredentials) – User account credentials. Credentials for an administrator user account that will be created on each compute node.

  • subnet (ResourceId) – Subnet. Virtual network subnet resource ID the compute nodes belong to.

Variables

  • allocation_state (str or AllocationState) – Allocation state. Allocation state of the compute. Possible values are: steady - Indicates that the compute is not resizing. There are no changes to the number of compute nodes in the compute in progress. A compute enters this state when it is created and when no operations are being performed on the compute to change the number of compute nodes. resizing - Indicates that the compute is resizing; that is, compute nodes are being added to or removed from the compute. Possible values include: ‘Steady’, ‘Resizing’

  • allocation_state_transition_time (datetime) – Allocation state transition time. The time at which the compute entered its current allocation state.

  • errors (list[MachineLearningServiceError]) – Errors. Collection of errors encountered by various compute nodes during node setup.

  • current_node_count (int) – Current node count. The number of compute nodes currently assigned to the compute.

  • target_node_count (int) – Target node count. The target number of compute nodes for the compute. If the allocationState is resizing, this property denotes the target node count for the ongoing resize operation. If the allocationState is steady, this property denotes the target node count for the previous resize operation.

  • node_state_counts (NodeStateCounts) – Node state counts. Counts of various node states on the compute.

class azure.mgmt.machinelearningservices.models.ClusterUpdateParameters(*, scale_settings=None, **kwargs)[source]

Bases: msrest.serialization.Model

AmlCompute update parameters.

Parameters

scale_settings (ScaleSettings) – Scale settings. Desired scale settings for the amlCompute.

class azure.mgmt.machinelearningservices.models.Compute(*, compute_location: str = None, description: str = None, resource_id: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Machine Learning compute object.

You probably want to use the sub-classes and not this class directly. Known sub-classes are: AKS, AmlCompute, VirtualMachine, HDInsight, DataFactory, Databricks, DataLakeAnalytics

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.ComputeNodesInformation(**kwargs)[source]

Bases: msrest.serialization.Model

Compute nodes information related to a Machine Learning compute. Might differ for every type of compute.

You probably want to use the sub-classes and not this class directly. Known sub-classes are: AmlComputeNodesInformation

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 Azure.

Variables

next_link (str) – The continuation token.

Parameters

compute_type (str) – Required. Constant filled by server.

class azure.mgmt.machinelearningservices.models.ComputeResource(*, location: str = None, tags=None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Resource

Machine Learning compute object wrapped into ARM resource envelope.

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

Variables

  • id (str) – Specifies the resource ID.

  • name (str) – Specifies the name of the resource.

  • identity (Identity) – The identity of the resource.

  • type (str) – Specifies the type of the resource.

Parameters

  • location (str) – Specifies the location of the resource.

  • tags (dict[str, str]) – Contains resource tags defined as key/value pairs.

  • properties (Compute) – Compute properties

class azure.mgmt.machinelearningservices.models.ComputeSecrets(**kwargs)[source]

Bases: msrest.serialization.Model

Secrets related to a Machine Learning compute. Might differ for every type of compute.

You probably want to use the sub-classes and not this class directly. Known sub-classes are: AksComputeSecrets, VirtualMachineSecrets, DatabricksComputeSecrets

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

Parameters

compute_type (str) – Required. Constant filled by server.

class azure.mgmt.machinelearningservices.models.Databricks(*, compute_location: str = None, description: str = None, resource_id: str = None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

A DataFactory compute.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

  • properties (DatabricksProperties) –

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.DatabricksComputeSecrets(*, databricks_access_token: str = None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.ComputeSecrets

Secrets related to a Machine Learning compute based on Databricks.

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

Parameters

  • compute_type (str) – Required. Constant filled by server.

  • databricks_access_token (str) – access token for databricks account.

class azure.mgmt.machinelearningservices.models.DatabricksProperties(*, databricks_access_token: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

DatabricksProperties.

Parameters

databricks_access_token (str) – Databricks access token

class azure.mgmt.machinelearningservices.models.DataFactory(*, compute_location: str = None, description: str = None, resource_id: str = None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

A DataFactory compute.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.DataLakeAnalytics(*, compute_location: str = None, description: str = None, resource_id: str = None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

A DataLakeAnalytics compute.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

  • properties (DataLakeAnalyticsProperties) –

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.DataLakeAnalyticsProperties(*, data_lake_store_account_name: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

DataLakeAnalyticsProperties.

Parameters

data_lake_store_account_name (str) – DataLake Store Account Name

class azure.mgmt.machinelearningservices.models.ErrorDetail(*, code: str, message: str, **kwargs)[source]

Bases: msrest.serialization.Model

Error detail information.

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

Parameters

  • code (str) – Required. Error code.

  • message (str) – Required. Error message.

class azure.mgmt.machinelearningservices.models.ErrorResponse(**kwargs)[source]

Bases: msrest.serialization.Model

Error response information.

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

Variables

  • code (str) – Error code.

  • message (str) – Error message.

  • details (list[ErrorDetail]) – An array of error detail objects.

class azure.mgmt.machinelearningservices.models.HDInsight(*, compute_location: str = None, description: str = None, resource_id: str = None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

A HDInsight compute.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

  • properties (HDInsightProperties) –

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.HDInsightProperties(*, ssh_port: int = None, address: str = None, administrator_account=None, **kwargs)[source]

Bases: msrest.serialization.Model

HDInsightProperties.

Parameters

  • ssh_port (int) – Port open for ssh connections on the master node of the cluster.

  • address (str) – Public IP address of the master node of the cluster.

  • administrator_account (VirtualMachineSshCredentials) – Admin credentials for master node of the cluster

class azure.mgmt.machinelearningservices.models.Identity(*, type=None, **kwargs)[source]

Bases: msrest.serialization.Model

Identity for the resource.

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

Variables

  • principal_id (str) – The principal ID of resource identity.

  • tenant_id (str) – The tenant ID of resource.

Parameters

type (str or ResourceIdentityType) – The identity type. Possible values include: ‘SystemAssigned’

class azure.mgmt.machinelearningservices.models.ListWorkspaceKeysResult(**kwargs)[source]

Bases: msrest.serialization.Model

ListWorkspaceKeysResult.

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

Variables
class azure.mgmt.machinelearningservices.models.MachineLearningServiceError(**kwargs)[source]

Bases: msrest.serialization.Model

Wrapper for error response to follow ARM guidelines.

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

Variables

error (ErrorResponse) – The error response.

class azure.mgmt.machinelearningservices.models.NodeStateCounts(**kwargs)[source]

Bases: msrest.serialization.Model

Counts of various compute node states on the amlCompute.

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

Variables

  • idle_node_count (int) – Idle node count. Number of compute nodes in idle state.

  • running_node_count (int) – Running node count. Number of compute nodes which are running jobs.

  • preparing_node_count (int) – Preparing node count. Number of compute nodes which are being prepared.

  • unusable_node_count (int) – Unusable node count. Number of compute nodes which are in unusable state.

  • leaving_node_count (int) – Leaving node count. Number of compute nodes which are leaving the amlCompute.

  • preempted_node_count (int) – Preempted node count. Number of compute nodes which are in preempted state.

class azure.mgmt.machinelearningservices.models.Operation(*, name: str = None, display=None, **kwargs)[source]

Bases: msrest.serialization.Model

Azure Machine Learning workspace REST API operation.

Parameters

  • name (str) – Operation name: {provider}/{resource}/{operation}

  • display (OperationDisplay) – Display name of operation

class azure.mgmt.machinelearningservices.models.OperationDisplay(*, provider: str = None, resource: str = None, operation: str = None, description: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Display name of operation.

Parameters

  • provider (str) – The resource provider name: Microsoft.MachineLearningExperimentation

  • resource (str) – The resource on which the operation is performed.

  • operation (str) – The operation that users can perform.

  • description (str) – The description for the operation.

class azure.mgmt.machinelearningservices.models.Password(**kwargs)[source]

Bases: msrest.serialization.Model

Password.

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

Variables

  • name (str) –

  • value (str) –

class azure.mgmt.machinelearningservices.models.RegistryListCredentialsResult(*, passwords=None, **kwargs)[source]

Bases: msrest.serialization.Model

RegistryListCredentialsResult.

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

Variables

  • location (str) –

  • username (str) –

Parameters

passwords (list[Password]) –

class azure.mgmt.machinelearningservices.models.Resource(*, location: str = None, tags=None, **kwargs)[source]

Bases: msrest.serialization.Model

Azure Resource Manager resource envelope.

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

Variables

  • id (str) – Specifies the resource ID.

  • name (str) – Specifies the name of the resource.

  • identity (Identity) – The identity of the resource.

  • type (str) – Specifies the type of the resource.

Parameters

  • location (str) – Specifies the location of the resource.

  • tags (dict[str, str]) – Contains resource tags defined as key/value pairs.

class azure.mgmt.machinelearningservices.models.ResourceId(*, id: str, **kwargs)[source]

Bases: msrest.serialization.Model

Represents a resource ID. For example, for a subnet, it is the resource URL for the subnet.

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

Parameters

id (str) – Required. The ID of the resource

class azure.mgmt.machinelearningservices.models.ScaleSettings(*, max_node_count: int, min_node_count: int = 0, node_idle_time_before_scale_down=None, **kwargs)[source]

Bases: msrest.serialization.Model

scale settings for AML Compute.

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

Parameters

  • max_node_count (int) – Required. Max number of nodes to use

  • min_node_count (int) – Min number of nodes to use. Default value: 0 .

  • node_idle_time_before_scale_down (timedelta) – Node Idle Time before scaling down amlCompute

class azure.mgmt.machinelearningservices.models.ServicePrincipalCredentials(*, client_id: str, client_secret: str, **kwargs)[source]

Bases: msrest.serialization.Model

Service principal credentials.

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

Parameters

  • client_id (str) – Required. Client Id

  • client_secret (str) – Required. Client secret

class azure.mgmt.machinelearningservices.models.SslConfiguration(*, status=None, cert: str = None, key: str = None, cname: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

The ssl configuration for scoring.

Parameters

  • status (str or enum) – Enable or disable ssl for scoring. Possible values include: ‘Disabled’, ‘Enabled’

  • cert (str) – Cert data

  • key (str) – Key data

  • cname (str) – CNAME of the cert

class azure.mgmt.machinelearningservices.models.SystemService(**kwargs)[source]

Bases: msrest.serialization.Model

A system service running on a compute.

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

Variables

  • system_service_type (str) – The type of this system service.

  • public_ip_address (str) – Public IP address

  • version (str) – The version for this type.

class azure.mgmt.machinelearningservices.models.Usage(**kwargs)[source]

Bases: msrest.serialization.Model

Describes AML Resource Usage.

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

Variables

  • id (str) – Specifies the resource ID.

  • type (str) – Specifies the resource type.

  • unit (str or UsageUnit) – An enum describing the unit of usage measurement. Possible values include: ‘Count’

  • current_value (long) – The current usage of the resource.

  • limit (long) – The maximum permitted usage of the resource.

  • name (UsageName) – The name of the type of usage.

class azure.mgmt.machinelearningservices.models.UsageName(**kwargs)[source]

Bases: msrest.serialization.Model

The Usage Names.

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

Variables

  • value (str) – The name of the resource.

  • localized_value (str) – The localized name of the resource.

class azure.mgmt.machinelearningservices.models.UserAccountCredentials(*, admin_user_name: str, admin_user_ssh_public_key: str = None, admin_user_password: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Settings for user account that gets created on each on the nodes of a compute.

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

Parameters

  • admin_user_name (str) – Required. User name. Name of the administrator user account which can be used to SSH to nodes.

  • admin_user_ssh_public_key (str) – SSH public key. SSH public key of the administrator user account.

  • admin_user_password (str) – Password. Password of the administrator user account.

class azure.mgmt.machinelearningservices.models.VirtualMachine(*, compute_location: str = None, description: str = None, resource_id: str = None, properties=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Compute

A Machine Learning compute based on Azure Virtual Machines.

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 Azure.

Parameters

  • compute_location (str) – Location for the underlying compute

  • description (str) – The description of the Machine Learning compute.

  • resource_id (str) – ARM resource id of the underlying compute

  • compute_type (str) – Required. Constant filled by server.

  • properties (VirtualMachineProperties) –

Variables

  • provisioning_state (str or ProvisioningState) – The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

  • created_on (datetime) – The date and time when the compute was created.

  • modified_on (datetime) – The date and time when the compute was last modified.

  • provisioning_errors (list[MachineLearningServiceError]) – Errors during provisioning

  • is_attached_compute (bool) – Indicating whether the compute was provisioned by user and brought from outside if true, or machine learning service provisioned it if false.

class azure.mgmt.machinelearningservices.models.VirtualMachineProperties(*, virtual_machine_size: str = None, ssh_port: int = None, address: str = None, administrator_account=None, **kwargs)[source]

Bases: msrest.serialization.Model

VirtualMachineProperties.

Parameters

  • virtual_machine_size (str) – Virtual Machine size

  • ssh_port (int) – Port open for ssh connections.

  • address (str) – Public IP address of the virtual machine.

  • administrator_account (VirtualMachineSshCredentials) – Admin credentials for virtual machine

class azure.mgmt.machinelearningservices.models.VirtualMachineSecrets(*, administrator_account=None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.ComputeSecrets

Secrets related to a Machine Learning compute based on AKS.

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

Parameters

  • compute_type (str) – Required. Constant filled by server.

  • administrator_account (VirtualMachineSshCredentials) – Admin credentials for virtual machine.

class azure.mgmt.machinelearningservices.models.VirtualMachineSize(**kwargs)[source]

Bases: msrest.serialization.Model

Describes the properties of a VM size.

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

Variables

  • name (str) – Virtual Machine size name. The name of the virtual machine size.

  • family (str) – Virtual Machine family name. The family name of the virtual machine size.

  • v_cp_us (int) – Number of vPUs. The number of vCPUs supported by the virtual machine size.

  • os_vhd_size_mb (int) – OS VHD Disk size. The OS VHD disk size, in MB, allowed by the virtual machine size.

  • max_resource_volume_mb (int) – Resource volume size. The resource volume size, in MB, allowed by the virtual machine size.

  • memory_gb (float) – Memory size. The amount of memory, in GB, supported by the virtual machine size.

  • low_priority_capable (bool) – Low priority capable. Specifies if the virtual machine size supports low priority VMs.

  • premium_io (bool) – Premium IO supported. Specifies if the virtual machine size supports premium IO.

class azure.mgmt.machinelearningservices.models.VirtualMachineSizeListResult(*, aml_compute=None, **kwargs)[source]

Bases: msrest.serialization.Model

The List Virtual Machine size operation response.

Parameters

aml_compute (list[VirtualMachineSize]) – The list of virtual machine sizes supported by AmlCompute.

class azure.mgmt.machinelearningservices.models.VirtualMachineSshCredentials(*, username: str = None, password: str = None, public_key_data: str = None, private_key_data: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Admin credentials for virtual machine.

Parameters

  • username (str) – Username of admin account

  • password (str) – Password of admin account

  • public_key_data (str) – Public key data

  • private_key_data (str) – Private key data

class azure.mgmt.machinelearningservices.models.Workspace(*, location: str = None, tags=None, description: str = None, friendly_name: str = None, key_vault: str = None, application_insights: str = None, container_registry: str = None, storage_account: str = None, discovery_url: str = None, **kwargs)[source]

Bases: azure.mgmt.machinelearningservices.models._models_py3.Resource

An object that represents a machine learning workspace.

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

Variables

  • id (str) – Specifies the resource ID.

  • name (str) – Specifies the name of the resource.

  • identity (Identity) – The identity of the resource.

  • type (str) – Specifies the type of the resource.

  • workspace_id (str) – The immutable id associated with this workspace.

  • creation_time (datetime) – The creation time of the machine learning workspace in ISO8601 format.

  • provisioning_state (str or ProvisioningState) – The current deployment state of workspace resource. The provisioningState is to indicate states for resource provisioning. Possible values include: ‘Unknown’, ‘Updating’, ‘Creating’, ‘Deleting’, ‘Succeeded’, ‘Failed’, ‘Canceled’

Parameters

  • location (str) – Specifies the location of the resource.

  • tags (dict[str, str]) – Contains resource tags defined as key/value pairs.

  • description (str) – The description of this workspace.

  • friendly_name (str) – The friendly name for this workspace. This name in mutable

  • key_vault (str) – ARM id of the key vault associated with this workspace. This cannot be changed once the workspace has been created

  • application_insights (str) – ARM id of the application insights associated with this workspace. This cannot be changed once the workspace has been created

  • container_registry (str) – ARM id of the container registry associated with this workspace. This cannot be changed once the workspace has been created

  • storage_account (str) – ARM id of the storage account associated with this workspace. This cannot be changed once the workspace has been created

  • discovery_url (str) – Url for the discovery service to identify regional endpoints for machine learning experimentation services

class azure.mgmt.machinelearningservices.models.WorkspaceUpdateParameters(*, tags=None, description: str = None, friendly_name: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

The parameters for updating a machine learning workspace.

Parameters

  • tags (dict[str, str]) – The resource tags for the machine learning workspace.

  • description (str) – The description of this workspace.

  • friendly_name (str) – The friendly name for this workspace.

class azure.mgmt.machinelearningservices.models.OperationPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Operation object

Bring async to Paging.

“async_command” is mandatory keyword argument for this mixin to work.

class azure.mgmt.machinelearningservices.models.WorkspacePaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Workspace object

Bring async to Paging.

“async_command” is mandatory keyword argument for this mixin to work.

class azure.mgmt.machinelearningservices.models.UsagePaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Usage object

Bring async to Paging.

“async_command” is mandatory keyword argument for this mixin to work.

class azure.mgmt.machinelearningservices.models.ComputeResourcePaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of ComputeResource object

Bring async to Paging.

“async_command” is mandatory keyword argument for this mixin to work.

class azure.mgmt.machinelearningservices.models.ProvisioningState[source]

Bases: str, enum.Enum

An enumeration.

canceled = 'Canceled'
creating = 'Creating'
deleting = 'Deleting'
failed = 'Failed'
succeeded = 'Succeeded'
unknown = 'Unknown'
updating = 'Updating'
class azure.mgmt.machinelearningservices.models.UsageUnit[source]

Bases: str, enum.Enum

An enumeration.

count = 'Count'
class azure.mgmt.machinelearningservices.models.ResourceIdentityType[source]

Bases: str, enum.Enum

An enumeration.

system_assigned = 'SystemAssigned'
class azure.mgmt.machinelearningservices.models.VmPriority[source]

Bases: str, enum.Enum

An enumeration.

dedicated = 'Dedicated'
low_priority = 'LowPriority'
class azure.mgmt.machinelearningservices.models.AllocationState[source]

Bases: str, enum.Enum

An enumeration.

resizing = 'Resizing'
steady = 'Steady'
class azure.mgmt.machinelearningservices.models.ComputeType[source]

Bases: str, enum.Enum

An enumeration.

aks = 'AKS'
aml_compute = 'AmlCompute'
data_factory = 'DataFactory'
data_lake_analytics = 'DataLakeAnalytics'
databricks = 'Databricks'
hd_insight = 'HDInsight'
virtual_machine = 'VirtualMachine'
class azure.mgmt.machinelearningservices.models.UnderlyingResourceAction[source]

Bases: str, enum.Enum

An enumeration.

delete = 'Delete'
detach = 'Detach'