azure.mgmt.batchai.models module

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

Bases: msrest.serialization.Model

Describes Batch AI Resource Usage.

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

Variables:
  • unit (str or UsageUnit) – An enum describing the unit of usage measurement. Possible values include: ‘Count’
  • current_value (int) – 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.batchai.models.UserAccountSettings(*, 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 cluster.

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

Parameters:
  • admin_user_name (str) – Required. Specifies the name of the administrator account.
  • admin_user_ssh_public_key (str) – SSH public keys used to authenticate with linux based VMs. This does not get returned in a GET response body.
  • admin_user_password (str) – Admin user Password (linux only). This does not get returned in a GET response body.
class azure.mgmt.batchai.models.SshConfiguration(*, user_account_settings, public_ips_to_allow=None, **kwargs)[source]

Bases: msrest.serialization.Model

SSH configuration settings for the VM.

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

Parameters:
  • public_ips_to_allow (list[str]) – List of source IP ranges to allow SSH connection to a node. Default value is ‘*’ can be used to match all source IPs. Maximum number of IP ranges that can be specified are 400.
  • user_account_settings (UserAccountSettings) – Required. Settings for user account to be created on a node.
class azure.mgmt.batchai.models.DataDisks(*, disk_size_in_gb: int, disk_count: int, storage_account_type, caching_type='none', **kwargs)[source]

Bases: msrest.serialization.Model

Settings for the data disk which would be created for the File Server.

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

Parameters:
  • disk_size_in_gb (int) – Required. Initial disk size in GB for blank data disks, and the new desired size for resizing existing data disks.
  • caching_type (str or CachingType) – None, ReadOnly, ReadWrite. Default value is None. This property is not patchable. Possible values include: ‘none’, ‘readonly’, ‘readwrite’. Default value: “none” .
  • disk_count (int) – Required. Number of data disks to be attached to the VM. RAID level 0 will be applied in the case of multiple disks.
  • storage_account_type (str or StorageAccountType) – Required. Specifies the type of storage account to be used on the disk. Possible values are: Standard_LRS or Premium_LRS. Possible values include: ‘Standard_LRS’, ‘Premium_LRS’
class azure.mgmt.batchai.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.batchai.models.MountSettings(*, mount_point: str = None, file_server_public_ip: str = None, file_server_internal_ip: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Details of the File Server.

Parameters:
  • mount_point (str) – Path where the NFS is mounted on the Server.
  • file_server_public_ip (str) – Public IP of the File Server VM.
  • file_server_internal_ip (str) – Internal subnet IP which can be used to access the file Server from within the subnet.
class azure.mgmt.batchai.models.FileServer(*, vm_size: str = None, ssh_configuration=None, data_disks=None, subnet=None, **kwargs)[source]

Bases: azure.mgmt.batchai.models.proxy_resource_py3.ProxyResource

Contains information about the File Server.

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

Variables:
  • id (str) – The ID of the resource.
  • name (str) – The name of the resource.
  • type (str) – The type of the resource.
  • mount_settings (MountSettings) – Details of the File Server.
  • provisioning_state_transition_time (datetime) – Time when the status was changed.
  • creation_time (datetime) – Time when the FileServer was created.
  • provisioning_state (str or FileServerProvisioningState) – Specifies the provisioning state of the File Server. Possible values: creating - The File Server is getting created. updating - The File Server creation has been accepted and it is getting updated. deleting - The user has requested that the File Server be deleted, and it is in the process of being deleted. failed - The File Server creation has failed with the specified errorCode. Details about the error code are specified in the message field. succeeded - The File Server creation has succeeded. Possible values include: ‘creating’, ‘updating’, ‘deleting’, ‘succeeded’, ‘failed’
Parameters:
  • vm_size (str) – The size of the virtual machine of the File Server. For information about available VM sizes for File Server from the Virtual Machines Marketplace, see Sizes for Virtual Machines (Linux).
  • ssh_configuration (SshConfiguration) – SSH settings for the File Server.
  • data_disks (DataDisks) – Settings for the data disk which would be created for the File Server.
  • subnet (ResourceId) – Specifies the identifier of the subnet.
class azure.mgmt.batchai.models.KeyVaultSecretReference(*, source_vault, secret_url: str, **kwargs)[source]

Bases: msrest.serialization.Model

Describes a reference to Key Vault Secret.

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

Parameters:
  • source_vault (ResourceId) – Required. Fully qualified resource Id for the Key Vault.
  • secret_url (str) – Required. The URL referencing a secret in a Key Vault.
class azure.mgmt.batchai.models.FileServerCreateParameters(*, vm_size: str, ssh_configuration, data_disks, subnet=None, **kwargs)[source]

Bases: msrest.serialization.Model

Parameters supplied to the Create operation.

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

Parameters:
  • vm_size (str) – Required. The size of the virtual machine of the file server. For information about available VM sizes for fileservers from the Virtual Machines Marketplace, see Sizes for Virtual Machines (Linux).
  • ssh_configuration (SshConfiguration) – Required. SSH configuration for the file server.
  • data_disks (DataDisks) – Required. Settings for the data disk which would be created for the file server.
  • subnet (ResourceId) – Specifies the identifier of the subnet.
class azure.mgmt.batchai.models.ManualScaleSettings(*, target_node_count: int = 0, node_deallocation_option='requeue', **kwargs)[source]

Bases: msrest.serialization.Model

Manual scale settings for the cluster.

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

Parameters:
  • target_node_count (int) – Required. The desired number of compute nodes in the Cluster. Default is 0. If autoScaleSettings are not specified, then the Cluster starts with this target. Default value: 0 .
  • node_deallocation_option (str or DeallocationOption) – Determines what to do with the job(s) running on compute node if the Cluster size is decreasing. The default value is requeue. Possible values include: ‘requeue’, ‘terminate’, ‘waitforjobcompletion’. Default value: “requeue” .
class azure.mgmt.batchai.models.AutoScaleSettings(*, minimum_node_count: int, maximum_node_count: int, initial_node_count: int = 0, **kwargs)[source]

Bases: msrest.serialization.Model

The system automatically scales the cluster up and down (within minimumNodeCount and maximumNodeCount) based on the pending and running jobs on the cluster.

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

Parameters:
  • minimum_node_count (int) – Required. Specifies the minimum number of compute nodes the cluster can have.
  • maximum_node_count (int) – Required. Specifies the maximum number of compute nodes the cluster can have.
  • initial_node_count (int) – Specifies the number of compute nodes to allocate on cluster creation. Note that this value is used only during cluster creation. Default value: 0 .
class azure.mgmt.batchai.models.ScaleSettings(*, manual=None, auto_scale=None, **kwargs)[source]

Bases: msrest.serialization.Model

At least one of manual or autoScale settings must be specified. Only one of manual or autoScale settings can be specified. If autoScale settings are specified, the system automatically scales the cluster up and down (within the supplied limits) based on the pending jobs on the cluster.

Parameters:
class azure.mgmt.batchai.models.ImageReference(*, publisher: str, offer: str, sku: str, version: str = None, virtual_machine_image_id: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

The image reference.

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

Parameters:
  • publisher (str) – Required. Publisher of the image.
  • offer (str) – Required. Offer of the image.
  • sku (str) – Required. SKU of the image.
  • version (str) – Version of the image.
  • virtual_machine_image_id (str) – The ARM resource identifier of the virtual machine image. Computes nodes of the cluster will be created using this custom image. This is of the form /subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.Compute/images/{imageName}. The virtual machine image must be in the same region and subscription as the cluster. For information about the firewall settings for the Batch node agent to communicate with the Batch service see https://docs.microsoft.com/en-us/azure/batch/batch-api-basics#virtual-network-vnet-and-firewall-configuration. Note, you need to provide publisher, offer and sku of the base OS image of which the custom image has been derived from.
class azure.mgmt.batchai.models.VirtualMachineConfiguration(*, image_reference=None, **kwargs)[source]

Bases: msrest.serialization.Model

Settings for OS image.

Parameters:image_reference (ImageReference) – Reference to OS image.
class azure.mgmt.batchai.models.EnvironmentVariable(*, name: str, value: str, **kwargs)[source]

Bases: msrest.serialization.Model

A collection of environment variables to set.

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

Parameters:
  • name (str) – Required. The name of the environment variable.
  • value (str) – Required. The value of the environment variable.
class azure.mgmt.batchai.models.EnvironmentVariableWithSecretValue(*, name: str, value: str = None, value_secret_reference=None, **kwargs)[source]

Bases: msrest.serialization.Model

A collection of environment variables with secret values to set.

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

Parameters:
  • name (str) – Required. The name of the environment variable to store the secret value.
  • value (str) – The value of the environment variable. This value will never be reported back by Batch AI.
  • value_secret_reference (KeyVaultSecretReference) – Specifies the location of the Azure KeyVault secret which will be used as the environment variable value. Specifies KeyVault Store and Secret which contains the value for the environment variable. One of value or valueSecretReference must be provided.
class azure.mgmt.batchai.models.SetupTask(*, command_line: str, std_out_err_path_prefix: str, environment_variables=None, secrets=None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies a setup task which can be used to customize the compute nodes of the cluster.

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:
  • command_line (str) – Required. Command line to be executed on each cluster’s node after it being allocated or rebooted. Command line to be executed on each cluster’s node after it being allocated or rebooted. The command is executed in a bash subshell as a root.
  • environment_variables (list[EnvironmentVariable]) – Collection of environment variables to be set for setup task.
  • secrets (list[EnvironmentVariableWithSecretValue]) – Collection of environment variables with secret values to be set for setup task. Server will never report values of these variables back.
  • std_out_err_path_prefix (str) – Required. The prefix of a path where the Batch AI service will upload the stdout and stderr of the setup task.
Variables:

std_out_err_path_suffix (str) – A path segment appended by Batch AI to stdOutErrPathPrefix to form a path where stdout and stderr of the setup task will be uploaded. Batch AI creates the setup task output directories under an unique path to avoid conflicts between different clusters. You can concatinate stdOutErrPathPrefix and stdOutErrPathSuffix to get the full path to the output directory.

class azure.mgmt.batchai.models.AzureStorageCredentialsInfo(*, account_key: str = None, account_key_secret_reference=None, **kwargs)[source]

Bases: msrest.serialization.Model

Credentials to access Azure File Share.

Parameters:
  • account_key (str) – Storage account key. One of accountKey or accountKeySecretReference must be specified.
  • account_key_secret_reference (KeyVaultSecretReference) – Specifies the location of the storage account key, which is a Key Vault Secret. Users can store their secrets in Azure KeyVault and pass it to the Batch AI Service to integrate with KeyVault. One of accountKey or accountKeySecretReference must be specified.
class azure.mgmt.batchai.models.AzureFileShareReference(*, account_name: str, azure_file_url: str, credentials, relative_mount_path: str, file_mode: str = '0777', directory_mode: str = '0777', **kwargs)[source]

Bases: msrest.serialization.Model

Details of the Azure File Share to mount on the cluster.

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

Parameters:
  • account_name (str) – Required. Name of the storage account.
  • azure_file_url (str) – Required. URL to access the Azure File.
  • credentials (AzureStorageCredentialsInfo) – Required. Information of the Azure File credentials.
  • relative_mount_path (str) – Required. Specifies the relative path on the compute node where the Azure file share will be mounted. Note that all cluster level file shares will be mounted under $AZ_BATCHAI_MOUNT_ROOT location and all job level file shares will be mounted under $AZ_BATCHAI_JOB_MOUNT_ROOT.
  • file_mode (str) – Specifies the file mode. Default value is 0777. Valid only if OS is linux. Default value: “0777” .
  • directory_mode (str) – Specifies the directory Mode. Default value is 0777. Valid only if OS is linux. Default value: “0777” .
class azure.mgmt.batchai.models.AzureBlobFileSystemReference(*, account_name: str, container_name: str, credentials, relative_mount_path: str, mount_options: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Provides required information, for the service to be able to mount Azure Blob Storage container on the cluster nodes.

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

Parameters:
  • account_name (str) – Required. Name of the Azure Blob Storage account.
  • container_name (str) – Required. Name of the Azure Blob Storage container to mount on the cluster.
  • credentials (AzureStorageCredentialsInfo) – Required. Information of the Azure Blob Storage account credentials.
  • relative_mount_path (str) – Required. Specifies the relative path on the compute node where the Azure Blob file system will be mounted. Note that all cluster level blob file systems will be mounted under $AZ_BATCHAI_MOUNT_ROOT location and all job level blob file systems will be mounted under $AZ_BATCHAI_JOB_MOUNT_ROOT.
  • mount_options (str) – Specifies the various mount options that can be used to configure Blob file system.
class azure.mgmt.batchai.models.FileServerReference(*, file_server, relative_mount_path: str, source_directory: str = None, mount_options: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Provides required information, for the service to be able to mount Azure FileShare on the cluster nodes.

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

Parameters:
  • file_server (ResourceId) – Required. Reference to the file server resource.
  • source_directory (str) – Specifies the source directory in File Server that needs to be mounted. If this property is not specified, the entire File Server will be mounted.
  • relative_mount_path (str) – Required. Specifies the relative path on the compute node where the File Server will be mounted. Note that all cluster level file servers will be mounted under $AZ_BATCHAI_MOUNT_ROOT location and job level file servers will be mouted under $AZ_BATCHAI_JOB_MOUNT_ROOT.
  • mount_options (str) – Specifies the mount options for File Server.
class azure.mgmt.batchai.models.UnmanagedFileSystemReference(*, mount_command: str, relative_mount_path: str, **kwargs)[source]

Bases: msrest.serialization.Model

Details of the file system to mount on the compute cluster nodes.

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

Parameters:
  • mount_command (str) – Required. Command used to mount the unmanaged file system.
  • relative_mount_path (str) – Required. Specifies the relative path on the compute cluster node where the file system will be mounted. Note that all cluster level unmanaged file system will be mounted under $AZ_BATCHAI_MOUNT_ROOT location and job level unmanaged file system will be mounted under $AZ_BATCHAI_JOB_MOUNT_ROOT.
class azure.mgmt.batchai.models.MountVolumes(*, azure_file_shares=None, azure_blob_file_systems=None, file_servers=None, unmanaged_file_systems=None, **kwargs)[source]

Bases: msrest.serialization.Model

Details of volumes to mount on the cluster.

Parameters:
  • azure_file_shares (list[AzureFileShareReference]) – Azure File Share setup configuration. References to Azure File Shares that are to be mounted to the cluster nodes.
  • azure_blob_file_systems (list[AzureBlobFileSystemReference]) – Azure Blob FileSystem setup configuration. References to Azure Blob FUSE that are to be mounted to the cluster nodes.
  • file_servers (list[FileServerReference]) – References to a list of file servers that are mounted to the cluster node.
  • unmanaged_file_systems (list[UnmanagedFileSystemReference]) – References to a list of file servers that are mounted to the cluster node.
class azure.mgmt.batchai.models.AppInsightsReference(*, component, instrumentation_key: str = None, instrumentation_key_secret_reference=None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies Azure Application Insights information for performance counters reporting.

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

Parameters:
  • component (ResourceId) – Required. Specifies the Azure Application Insights component resource id.
  • instrumentation_key (str) – Value of the Azure Application Insights instrumentation key.
  • instrumentation_key_secret_reference (KeyVaultSecretReference) – Specifies a KeyVault Secret containing Azure Application Insights instrumentation key. Specifies KeyVault Store and Secret which contains Azure Application Insights instrumentation key. One of instumentationKey or instrumentationKeySecretReference must be specified.
class azure.mgmt.batchai.models.PerformanceCountersSettings(*, app_insights_reference, **kwargs)[source]

Bases: msrest.serialization.Model

Performance counters reporting settings.

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

Parameters:app_insights_reference (AppInsightsReference) – Required. Specifies Azure Application Insights information for performance counters reporting. If provided, Batch AI will upload node performance counters to the corresponding Azure Application Insights account.
class azure.mgmt.batchai.models.NodeSetup(*, setup_task=None, mount_volumes=None, performance_counters_settings=None, **kwargs)[source]

Bases: msrest.serialization.Model

Use this to prepare the VM. NOTE: The volumes specified in mountVolumes are mounted first and then the setupTask is run. Therefore the setup task can use local mountPaths in its execution.

Parameters:
  • setup_task (SetupTask) – Specifies a setup task which can be used to customize the compute nodes of the cluster. The NodeSetup task runs everytime a VM is rebooted. For that reason the task code needs to be idempotent. Generally it is used to either download static data that is required for all jobs that run on the cluster VMs or to download/install software.
  • mount_volumes (MountVolumes) – Information on shared volumes to be used by jobs. Specified mount volumes will be available to all jobs executing on the cluster. The volumes will be mounted at location specified by $AZ_BATCHAI_MOUNT_ROOT environment variable.
  • performance_counters_settings (PerformanceCountersSettings) – Specifies settings for performance counters collecting and uploading.
class azure.mgmt.batchai.models.NodeStateCounts(**kwargs)[source]

Bases: msrest.serialization.Model

Counts of various compute node states on the cluster.

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

Variables:
  • idle_node_count (int) – Number of compute nodes in idle state.
  • running_node_count (int) – Number of compute nodes which are running jobs.
  • preparing_node_count (int) – Number of compute nodes which are being prepared.
  • unusable_node_count (int) – Number of compute nodes which are unusable.
  • leaving_node_count (int) – Number of compute nodes which are leaving the cluster.
class azure.mgmt.batchai.models.ClusterCreateParameters(*, vm_size: str, user_account_settings, vm_priority='dedicated', scale_settings=None, virtual_machine_configuration=None, node_setup=None, subnet=None, **kwargs)[source]

Bases: msrest.serialization.Model

Parameters supplied to the Create operation.

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

Parameters:
  • vm_size (str) – Required. The size of the virtual machines in the cluster. All virtual machines in a cluster are the same size. For information about available VM sizes for clusters using images from the Virtual Machines Marketplace (see Sizes for Virtual Machines (Linux) or Sizes for Virtual Machines (Windows). Batch AI service supports all Azure VM sizes except STANDARD_A0 and those with premium storage (STANDARD_GS, STANDARD_DS, and STANDARD_DSV2 series).
  • vm_priority (str or VmPriority) – dedicated or lowpriority. Default is dedicated. Possible values include: ‘dedicated’, ‘lowpriority’. Default value: “dedicated” .
  • scale_settings (ScaleSettings) – Desired scale for the cluster.
  • virtual_machine_configuration (VirtualMachineConfiguration) – Settings for OS image and mounted data volumes.
  • node_setup (NodeSetup) – Setup to be done on all compute nodes in the cluster.
  • user_account_settings (UserAccountSettings) – Required. Settings for user account that will be created on all compute nodes of the cluster.
  • subnet (ResourceId) – Specifies the identifier of the subnet. .
class azure.mgmt.batchai.models.ClusterUpdateParameters(*, scale_settings=None, **kwargs)[source]

Bases: msrest.serialization.Model

Parameters supplied to the Update operation.

Parameters:scale_settings (ScaleSettings) – Desired scale for the cluster.
class azure.mgmt.batchai.models.NameValuePair(*, name: str = None, value: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Represents a name-value pair.

Parameters:
  • name (str) – The name in the name-value pair.
  • value (str) – The value in the name-value pair.
class azure.mgmt.batchai.models.BatchAIError(**kwargs)[source]

Bases: msrest.serialization.Model

An error response from the Batch AI service.

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

Variables:
  • code (str) – An identifier for the error. Codes are invariant and are intended to be consumed programmatically.
  • message (str) – A message describing the error, intended to be suitable for display in a user interface.
  • details (list[NameValuePair]) – A list of additional details about the error.
class azure.mgmt.batchai.models.Cluster(*, vm_size: str = None, vm_priority='dedicated', scale_settings=None, virtual_machine_configuration=None, node_setup=None, user_account_settings=None, subnet=None, **kwargs)[source]

Bases: azure.mgmt.batchai.models.proxy_resource_py3.ProxyResource

Contains information about a Cluster.

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

Variables:
  • id (str) – The ID of the resource.
  • name (str) – The name of the resource.
  • type (str) – The type of the resource.
  • creation_time (datetime) – The creation time of the cluster.
  • provisioning_state (str or ProvisioningState) – Specifies the provisioning state of the cluster. Possible value are: creating - Specifies that the cluster is being created. succeeded - Specifies that the cluster has been created successfully. failed - Specifies that the cluster creation has failed. deleting - Specifies that the cluster is being deleted. Possible values include: ‘creating’, ‘succeeded’, ‘failed’, ‘deleting’
  • provisioning_state_transition_time (datetime) – The provisioning state transition time of the cluster.
  • allocation_state (str or AllocationState) – Indicates whether the cluster is resizing. Possible values are: steady and resizing. steady state indicates that the cluster is not resizing. There are no changes to the number of compute nodes in the cluster in progress. A cluster enters this state when it is created and when no operations are being performed on the cluster to change the number of compute nodes. resizing state indicates that the cluster is resizing; that is, compute nodes are being added to or removed from the cluster. Possible values include: ‘steady’, ‘resizing’
  • allocation_state_transition_time (datetime) – The time at which the cluster entered its current allocation state.
  • errors (list[BatchAIError]) – Contains details of various errors on the cluster including resize and node setup task. This element contains all the errors encountered by various compute nodes during node setup.
  • current_node_count (int) – The number of compute nodes currently assigned to the cluster.
  • node_state_counts (NodeStateCounts) – Counts of various node states on the cluster.
Parameters:
  • vm_size (str) – The size of the virtual machines in the cluster. All virtual machines in a cluster are the same size. For information about available VM sizes for clusters using images from the Virtual Machines Marketplace (see Sizes for Virtual Machines (Linux) or Sizes for Virtual Machines (Windows). Batch AI service supports all Azure VM sizes except STANDARD_A0 and those with premium storage (STANDARD_GS, STANDARD_DS, and STANDARD_DSV2 series).
  • vm_priority (str or VmPriority) – dedicated or lowpriority. The default value is dedicated. The node can get preempted while the task is running if lowpriority is choosen. This is best suited if the workload is checkpointing and can be restarted. Possible values include: ‘dedicated’, ‘lowpriority’. Default value: “dedicated” .
  • scale_settings (ScaleSettings) – Desired scale for the Cluster.
  • virtual_machine_configuration (VirtualMachineConfiguration) – Settings for OS image and mounted data volumes.
  • node_setup (NodeSetup) – Setup to be done on all compute nodes in the Cluster.
  • user_account_settings (UserAccountSettings) – Settings for user account of compute nodes.
  • subnet (ResourceId) – Specifies the identifier of the subnet.
class azure.mgmt.batchai.models.PrivateRegistryCredentials(*, username: str, password: str = None, password_secret_reference=None, **kwargs)[source]

Bases: msrest.serialization.Model

Credentials to access a container image in a private repository.

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

Parameters:
  • username (str) – Required. User name to login.
  • password (str) – Password to login. One of password or passwordSecretReference must be specified.
  • password_secret_reference (KeyVaultSecretReference) – Specifies the location of the password, which is a Key Vault Secret. Users can store their secrets in Azure KeyVault and pass it to the Batch AI Service to integrate with KeyVault. One of password or passwordSecretReference must be specified.
class azure.mgmt.batchai.models.ImageSourceRegistry(*, image: str, server_url: str = None, credentials=None, **kwargs)[source]

Bases: msrest.serialization.Model

Details of the container image such as name, URL and credentials.

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

Parameters:
  • server_url (str) – URL for image repository.
  • image (str) – Required. The name of the image in image repository.
  • credentials (PrivateRegistryCredentials) – Information to access the private Docker repository.
class azure.mgmt.batchai.models.ContainerSettings(*, image_source_registry, shm_size: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Settings for the container to be downloaded.

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

Parameters:
  • image_source_registry (ImageSourceRegistry) – Required. Registry to download the container from.
  • shm_size (str) – Size of /dev/shm. Please refer to docker documentation for supported argument formats.
class azure.mgmt.batchai.models.CNTKsettings(*, language_type: str = None, config_file_path: str = None, python_script_file_path: str = None, python_interpreter_path: str = None, command_line_args: str = None, process_count: int = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for CNTK (aka Microsoft Cognitive Toolkit) job.

Parameters:
  • language_type (str) – Specifies the language type to use for launching CNTK (aka Microsoft Cognitive Toolkit) job. Valid values are ‘BrainScript’ or ‘Python’.
  • config_file_path (str) – Specifies the path of the config file. This property can be specified only if the languageType is ‘BrainScript’.
  • python_script_file_path (str) – The path and file name of the python script to execute the job. This property can be specified only if the languageType is ‘Python’.
  • python_interpreter_path (str) – The path to python interpreter. This property can be specified only if the languageType is ‘Python’.
  • command_line_args (str) – Command line arguments that needs to be passed to the python script or CNTK.exe.
  • process_count (int) – Number of processes parameter that is passed to MPI runtime. The default value for this property is equal to nodeCount property
class azure.mgmt.batchai.models.PyTorchSettings(*, python_script_file_path: str, python_interpreter_path: str = None, command_line_args: str = None, process_count: int = None, communication_backend: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for pyTorch job.

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

Parameters:
  • python_script_file_path (str) – Required. The path and file name of the python script to execute the job.
  • python_interpreter_path (str) – The path to python interpreter.
  • command_line_args (str) – Specifies the command line arguments for the master task.
  • process_count (int) – Number of processes to launch for the job execution. The default value for this property is equal to nodeCount property.
  • communication_backend (str) – Type of the communication backend for distributed jobs. Valid values are ‘TCP’, ‘Gloo’ or ‘MPI’. Not required for non-distributed jobs.
class azure.mgmt.batchai.models.TensorFlowSettings(*, python_script_file_path: str, python_interpreter_path: str = None, master_command_line_args: str = None, worker_command_line_args: str = None, parameter_server_command_line_args: str = None, worker_count: int = None, parameter_server_count: int = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for TensorFlow job.

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

Parameters:
  • python_script_file_path (str) – Required. The path and file name of the python script to execute the job.
  • python_interpreter_path (str) – The path to python interpreter.
  • master_command_line_args (str) – Specifies the command line arguments for the master task.
  • worker_command_line_args (str) – Specifies the command line arguments for the worker task. This property is optional for single machine training.
  • parameter_server_command_line_args (str) – Specifies the command line arguments for the parameter server task. This property is optional for single machine training.
  • worker_count (int) – The number of worker tasks. If specified, the value must be less than or equal to (nodeCount * numberOfGPUs per VM). If not specified, the default value is equal to nodeCount. This property can be specified only for distributed TensorFlow training
  • parameter_server_count (int) – The number of parmeter server tasks. If specified, the value must be less than or equal to nodeCount. If not specified, the default value is equal to 1 for distributed TensorFlow training (This property is not applicable for single machine training). This property can be specified only for distributed TensorFlow training.
class azure.mgmt.batchai.models.CaffeSettings(*, config_file_path: str = None, python_script_file_path: str = None, python_interpreter_path: str = None, command_line_args: str = None, process_count: int = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for Caffe job.

Parameters:
  • config_file_path (str) – Specifies the path of the config file. This property cannot be specified if pythonScriptFilePath is specified.
  • python_script_file_path (str) – The path and file name of the python script to execute the job. This property cannot be specified if configFilePath is specified.
  • python_interpreter_path (str) – The path to python interpreter. This property can be specified only if the pythonScriptFilePath is specified.
  • command_line_args (str) – Command line arguments that needs to be passed to the Caffe job.
  • process_count (int) – Number of processes parameter that is passed to MPI runtime. The default value for this property is equal to nodeCount property
class azure.mgmt.batchai.models.Caffe2Settings(*, python_script_file_path: str, python_interpreter_path: str = None, command_line_args: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for Caffe2 job.

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

Parameters:
  • python_script_file_path (str) – Required. The path and file name of the python script to execute the job.
  • python_interpreter_path (str) – The path to python interpreter.
  • command_line_args (str) – Command line arguments that needs to be passed to the python script.
class azure.mgmt.batchai.models.ChainerSettings(*, python_script_file_path: str, python_interpreter_path: str = None, command_line_args: str = None, process_count: int = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for Chainer job.

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

Parameters:
  • python_script_file_path (str) – Required. The path and file name of the python script to execute the job.
  • python_interpreter_path (str) – The path to python interpreter.
  • command_line_args (str) – Command line arguments that needs to be passed to the python script.
  • process_count (int) – Number of processes parameter that is passed to MPI runtime. The default value for this property is equal to nodeCount property
class azure.mgmt.batchai.models.CustomToolkitSettings(*, command_line: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for a custom tool kit job.

Parameters:command_line (str) – The command line to execute the custom toolkit Job.
class azure.mgmt.batchai.models.CustomMpiSettings(*, command_line: str, process_count: int = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for a custom MPI job.

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

Parameters:
  • command_line (str) – Required. The program and program command line parameters to be executed by mpi runtime.
  • process_count (int) – Number of processes parameter that is passed to MPI runtime. The default value for this property is equal to nodeCount property
class azure.mgmt.batchai.models.HorovodSettings(*, python_script_file_path: str, python_interpreter_path: str = None, command_line_args: str = None, process_count: int = None, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for Horovod job.

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

Parameters:
  • python_script_file_path (str) – Required. The path and file name of the python script to execute the job.
  • python_interpreter_path (str) – The path to python interpreter.
  • command_line_args (str) – Command line arguments that needs to be passed to the python script.
  • process_count (int) – Number of processes parameter that is passed to MPI runtime. The default value for this property is equal to nodeCount property
class azure.mgmt.batchai.models.JobPreparation(*, command_line: str, **kwargs)[source]

Bases: msrest.serialization.Model

Specifies the settings for job preparation.

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

Parameters:command_line (str) – Required. The command line to execute. If containerSettings is specified on the job, this commandLine will be executed in the same container as job. Otherwise it will be executed on the node.
class azure.mgmt.batchai.models.InputDirectory(*, id: str, path: str, **kwargs)[source]

Bases: msrest.serialization.Model

Input directory for the job.

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

Parameters:
  • id (str) – Required. The id for the input directory. The path of the input directory will be available as a value of an environment variable with AZ_BATCHAI_INPUT_<id> name, where <id> is the value of id attribute.
  • path (str) – Required. The path to the input directory.
class azure.mgmt.batchai.models.OutputDirectory(*, id: str, path_prefix: str, path_suffix: str = None, **kwargs)[source]

Bases: msrest.serialization.Model

Output directory for the job.

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

Parameters:
  • id (str) – Required. The name for the output directory. The path of the output directory will be available as a value of an environment variable with AZ_BATCHAI_OUTPUT_<id> name, where <id> is the value of id attribute.
  • path_prefix (str) – Required. The prefix path where the output directory will be created. NOTE: This is an absolute path to prefix. E.g. $AZ_BATCHAI_MOUNT_ROOT/MyNFS/MyLogs. You can find the full path to the output directory by combining pathPrefix, jobOutputDirectoryPathSegment (reported by get job) and pathSuffix.
  • path_suffix (str) – The suffix path where the output directory will be created. The suffix path where the output directory will be created. E.g. models. You can find the full path to the output directory by combining pathPrefix, jobOutputDirectoryPathSegment (reported by get job) and pathSuffix.
class azure.mgmt.batchai.models.JobBasePropertiesConstraints(*, max_wall_clock_time='7.00:00:00', **kwargs)[source]

Bases: msrest.serialization.Model

Constraints associated with the Job.

Parameters:max_wall_clock_time (timedelta) – Max time the job can run. Default Value = 1 week. Default value: “7.00:00:00” .
class azure.mgmt.batchai.models.JobCreateParameters(*, cluster, node_count: int, std_out_err_path_prefix: str, scheduling_priority='normal', mount_volumes=None, container_settings=None, cntk_settings=None, py_torch_settings=None, tensor_flow_settings=None, caffe_settings=None, caffe2_settings=None, chainer_settings=None, custom_toolkit_settings=None, custom_mpi_settings=None, horovod_settings=None, job_preparation=None, input_directories=None, output_directories=None, environment_variables=None, secrets=None, constraints=None, **kwargs)[source]

Bases: msrest.serialization.Model

Parameters supplied to the Create operation.

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

Parameters:
  • scheduling_priority (str or JobPriority) – Scheduling priority associated with the job. Scheduling priority associated with the job. Possible values include: ‘low’, ‘normal’, ‘high’. Default value: “normal” .
  • cluster (ResourceId) – Required. Specifies the Id of the cluster on which this job will run.
  • mount_volumes (MountVolumes) – Information on mount volumes to be used by the job. These volumes will be mounted before the job execution and will be unmouted after the job completion. The volumes will be mounted at location specified by $AZ_BATCHAI_JOB_MOUNT_ROOT environment variable.
  • node_count (int) – Required. Number of compute nodes to run the job on. The job will be gang scheduled on that many compute nodes
  • container_settings (ContainerSettings) – If provided the job will run in the specified container. If the container was downloaded as part of cluster setup then the same container image will be used. If not provided, the job will run on the VM.
  • cntk_settings (CNTKsettings) – Specifies the settings for CNTK (aka Microsoft Cognitive Toolkit) job.
  • py_torch_settings (PyTorchSettings) – Specifies the settings for pyTorch job.
  • tensor_flow_settings (TensorFlowSettings) – Specifies the settings for Tensor Flow job.
  • caffe_settings (CaffeSettings) – Specifies the settings for Caffe job.
  • caffe2_settings (Caffe2Settings) – Specifies the settings for Caffe2 job.
  • chainer_settings (ChainerSettings) – Specifies the settings for Chainer job.
  • custom_toolkit_settings (CustomToolkitSettings) – Specifies the settings for custom tool kit job.
  • custom_mpi_settings (CustomMpiSettings) – Specifies the settings for custom MPI job.
  • horovod_settings (HorovodSettings) – Specifies the settings for Horovod job.
  • job_preparation (JobPreparation) – Specifies the command line to be executed before tool kit is launched. The specified actions will run on all the nodes that are part of the job
  • std_out_err_path_prefix (str) – Required. The path where the Batch AI service will upload stdout and stderror of the job.
  • input_directories (list[InputDirectory]) – Specifies the list of input directories for the Job.
  • output_directories (list[OutputDirectory]) – Specifies the list of output directories.
  • environment_variables (list[EnvironmentVariable]) – Additional environment variables to set on the job. Batch AI will setup these additional environment variables for the job.
  • secrets (list[EnvironmentVariableWithSecretValue]) – Additional environment variables with secret values to set on the job. Batch AI will setup these additional environment variables for the job. Server will never report values of these variables back.
  • constraints (JobBasePropertiesConstraints) – Constraints associated with the Job.
class azure.mgmt.batchai.models.JobPropertiesConstraints(*, max_wall_clock_time='7.00:00:00', **kwargs)[source]

Bases: msrest.serialization.Model

Constraints associated with the Job.

Parameters:max_wall_clock_time (timedelta) – Max time the job can run. Default Value = 1 week. Default value: “7.00:00:00” .
class azure.mgmt.batchai.models.JobPropertiesExecutionInfo(**kwargs)[source]

Bases: msrest.serialization.Model

Contains information about the execution of a job in the Azure Batch service.

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

Variables:
  • start_time (datetime) – The time at which the job started running. ‘Running’ corresponds to the running state. If the job has been restarted or retried, this is the most recent time at which the job started running. This property is present only for job that are in the running or completed state.
  • end_time (datetime) – The time at which the job completed. This property is only returned if the job is in completed state.
  • exit_code (int) – The exit code of the job. This property is only returned if the job is in completed state.
  • errors (list[BatchAIError]) – Contains details of various errors encountered by the service during job execution.
class azure.mgmt.batchai.models.Job(*, scheduling_priority='normal', cluster=None, mount_volumes=None, node_count: int = None, container_settings=None, tool_type=None, cntk_settings=None, py_torch_settings=None, tensor_flow_settings=None, caffe_settings=None, caffe2_settings=None, chainer_settings=None, custom_toolkit_settings=None, custom_mpi_settings=None, horovod_settings=None, job_preparation=None, std_out_err_path_prefix: str = None, input_directories=None, output_directories=None, environment_variables=None, secrets=None, constraints=None, execution_info=None, **kwargs)[source]

Bases: azure.mgmt.batchai.models.proxy_resource_py3.ProxyResource

Contains information about a Job.

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

Variables:
  • id (str) – The ID of the resource.
  • name (str) – The name of the resource.
  • type (str) – The type of the resource.
  • job_output_directory_path_segment (str) – A segment of job’s output directories path created by BatchAI. Batch AI creates job’s output directories under an unique path to avoid conflicts between jobs. This value contains a path segment generated by Batch AI to make the path unique and can be used to find the output directory on the node or mounted filesystem.
  • creation_time (datetime) – The job creation time. The creation time of the job.
  • provisioning_state (str or ProvisioningState) – The provisioned state of the Batch AI job. Possible values include: ‘creating’, ‘succeeded’, ‘failed’, ‘deleting’
  • provisioning_state_transition_time (datetime) – The time at which the job entered its current provisioning state. The time at which the job entered its current provisioning state.
  • execution_state (str or ExecutionState) – The current state of the job. The current state of the job. Possible values are: queued - The job is queued and able to run. A job enters this state when it is created, or when it is awaiting a retry after a failed run. running - The job is running on a compute cluster. This includes job-level preparation such as downloading resource files or set up container specified on the job - it does not necessarily mean that the job command line has started executing. terminating - The job is terminated by the user, the terminate operation is in progress. succeeded - The job has completed running succesfully and exited with exit code 0. failed - The job has finished unsuccessfully (failed with a non-zero exit code) and has exhausted its retry limit. A job is also marked as failed if an error occurred launching the job. Possible values include: ‘queued’, ‘running’, ‘terminating’, ‘succeeded’, ‘failed’
  • execution_state_transition_time (datetime) – The time at which the job entered its current execution state. The time at which the job entered its current execution state.
Parameters:
  • scheduling_priority (str or JobPriority) – Scheduling priority associated with the job. Scheduling priority associated with the job. Possible values include: ‘low’, ‘normal’, ‘high’. Default value: “normal” .
  • cluster (ResourceId) – Specifies the Id of the cluster on which this job will run.
  • mount_volumes (MountVolumes) – Information on mount volumes to be used by the job. These volumes will be mounted before the job execution and will be unmouted after the job completion. The volumes will be mounted at location specified by $AZ_BATCHAI_JOB_MOUNT_ROOT environment variable.
  • node_count (int) – Number of compute nodes to run the job on. The job will be gang scheduled on that many compute nodes
  • container_settings (ContainerSettings) – If provided the job will run in the specified container. If the container was downloaded as part of cluster setup then the same container image will be used. If not provided, the job will run on the VM.
  • tool_type (str or ToolType) – The toolkit type of this job. Possible values are: cntk, tensorflow, caffe, caffe2, chainer, pytorch, custom, mpi, horovod. Possible values include: ‘cntk’, ‘tensorflow’, ‘caffe’, ‘caffe2’, ‘chainer’, ‘horovod’, ‘mpi’, ‘custom’
  • cntk_settings (CNTKsettings) – Specifies the settings for CNTK (aka Microsoft Cognitive Toolkit) job.
  • py_torch_settings (PyTorchSettings) – Specifies the settings for pyTorch job.
  • tensor_flow_settings (TensorFlowSettings) – Specifies the settings for Tensor Flow job.
  • caffe_settings (CaffeSettings) – Specifies the settings for Caffe job.
  • caffe2_settings (Caffe2Settings) – Specifies the settings for Caffe2 job.
  • chainer_settings (ChainerSettings) – Specifies the settings for Chainer job.
  • custom_toolkit_settings (CustomToolkitSettings) – Specifies the settings for custom tool kit job.
  • custom_mpi_settings (CustomMpiSettings) – Specifies the settings for custom MPI job.
  • horovod_settings (HorovodSettings) – Specifies the settings for Horovod job.
  • job_preparation (JobPreparation) – Specifies the actions to be performed before tool kit is launched. The specified actions will run on all the nodes that are part of the job
  • std_out_err_path_prefix (str) – The path where the Batch AI service will upload stdout and stderror of the job.
  • input_directories (list[InputDirectory]) – Specifies the list of input directories for the Job.
  • output_directories (list[OutputDirectory]) – Specifies the list of output directories where the models will be created.
  • environment_variables (list[EnvironmentVariable]) – Additional environment variables to set on the job. Batch AI will setup these additional environment variables for the job.
  • secrets (list[EnvironmentVariableWithSecretValue]) – Additional environment variables with secret values to set on the job. Batch AI will setup these additional environment variables for the job. Server will never report values of these variables back.
  • constraints (JobPropertiesConstraints) – Constraints associated with the Job.
  • execution_info (JobPropertiesExecutionInfo) – Contains information about the execution of a job in the Azure Batch service.
class azure.mgmt.batchai.models.RemoteLoginInformation(**kwargs)[source]

Bases: msrest.serialization.Model

Contains remote login details to SSH/RDP to a compute node in cluster.

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

Variables:
  • node_id (str) – Id of the compute node
  • ip_address (str) – ip address
  • port (float) – port number.
class azure.mgmt.batchai.models.File(**kwargs)[source]

Bases: msrest.serialization.Model

Properties of the file or directory.

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

Variables:
  • name (str) – Name of the file.
  • file_type (str or FileType) – Contains information about file type. Possible values include: ‘file’, ‘directory’
  • download_url (str) – Will contain an URL to download the corresponding file. The downloadUrl is not returned for directories.
  • last_modified (datetime) – The time at which the file was last modified. The time at which the file was last modified.
  • content_length (long) – The file size. The file size.
class azure.mgmt.batchai.models.Resource(**kwargs)[source]

Bases: msrest.serialization.Model

A definition of an Azure resource.

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

Variables:
  • id (str) – The ID of the resource
  • name (str) – The name of the resource
  • type (str) – The type of the resource
  • location (str) – The location of the resource
  • tags (dict[str, str]) – The tags of the resource
class azure.mgmt.batchai.models.ProxyResource(**kwargs)[source]

Bases: msrest.serialization.Model

A definition of an Azure proxy resource.

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

Variables:
  • id (str) – The ID of the resource.
  • name (str) – The name of the resource.
  • type (str) – The type of the resource.
class azure.mgmt.batchai.models.OperationDisplay(**kwargs)[source]

Bases: msrest.serialization.Model

The object that describes the operation.

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

Variables:
  • provider (str) – Friendly name of the resource provider.
  • operation (str) – The operation type. For example: read, write, delete, or listKeys/action
  • resource (str) – The resource type on which the operation is performed.
  • description (str) – The friendly name of the operation.
class azure.mgmt.batchai.models.Operation(*, display=None, properties=None, **kwargs)[source]

Bases: msrest.serialization.Model

A REST API operation.

Details of a REST API operation.

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

Variables:
  • name (str) – The operation name. This is of the format {provider}/{resource}/{operation}
  • origin (str) – The intended executor of the operation.
Parameters:
  • display (OperationDisplay) – The object that describes the operation.
  • properties (object) – Properties of the operation.
class azure.mgmt.batchai.models.Workspace(**kwargs)[source]

Bases: azure.mgmt.batchai.models.resource_py3.Resource

Describes Batch AI Workspace.

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

Variables:
  • id (str) – The ID of the resource
  • name (str) – The name of the resource
  • type (str) – The type of the resource
  • location (str) – The location of the resource
  • tags (dict[str, str]) – The tags of the resource
  • creation_time (datetime) – Time when the Workspace was created.
  • provisioning_state (str or ProvisioningState) – The provisioned state of the workspace. Possible values include: ‘creating’, ‘succeeded’, ‘failed’, ‘deleting’
  • provisioning_state_transition_time (datetime) – The time at which the workspace entered its current provisioning state. The time at which the workspace entered its current provisioning state.
class azure.mgmt.batchai.models.WorkspaceCreateParameters(*, location: str, tags=None, **kwargs)[source]

Bases: msrest.serialization.Model

Parameters supplied to the Create operation.

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

Parameters:
  • location (str) – Required. The region in which to create the Workspace.
  • tags (dict[str, str]) – The user specified tags associated with the Workspace.
class azure.mgmt.batchai.models.WorkspaceUpdateParameters(*, tags=None, **kwargs)[source]

Bases: msrest.serialization.Model

Parameters supplied to the Update operation.

Parameters:tags (dict[str, str]) – The user specified tags associated with the Workspace.
class azure.mgmt.batchai.models.Experiment(**kwargs)[source]

Bases: azure.mgmt.batchai.models.proxy_resource_py3.ProxyResource

Contains information about the experiment.

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

Variables:
  • id (str) – The ID of the resource.
  • name (str) – The name of the resource.
  • type (str) – The type of the resource.
  • creation_time (datetime) – Time when the Experiment was created.
  • provisioning_state (str or ProvisioningState) – The provisioned state of the experiment. Possible values include: ‘creating’, ‘succeeded’, ‘failed’, ‘deleting’
  • provisioning_state_transition_time (datetime) – The time at which the experiment entered its current provisioning state. The time at which the experiment entered its current provisioning state.
class azure.mgmt.batchai.models.WorkspacesListOptions(*, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list operation.

Parameters:max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.WorkspacesListByResourceGroupOptions(*, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list_by_resource_group operation.

Parameters:max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.ExperimentsListByWorkspaceOptions(*, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list_by_workspace operation.

Parameters:max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.JobsListByExperimentOptions(*, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list_by_experiment operation.

Parameters:max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.JobsListOutputFilesOptions(*, outputdirectoryid: str, directory: str = '.', linkexpiryinminutes: int = 60, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list_output_files operation.

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

Parameters:
  • outputdirectoryid (str) – Required. Id of the job output directory. This is the OutputDirectory–>id parameter that is given by the user during Create Job.
  • directory (str) – The path to the directory. Default value: “.” .
  • linkexpiryinminutes (int) – The number of minutes after which the download link will expire. Default value: 60 .
  • max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.FileServersListByWorkspaceOptions(*, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list_by_workspace operation.

Parameters:max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.ClustersListByWorkspaceOptions(*, max_results: int = 1000, **kwargs)[source]

Bases: msrest.serialization.Model

Additional parameters for list_by_workspace operation.

Parameters:max_results (int) – The maximum number of items to return in the response. A maximum of 1000 files can be returned. Default value: 1000 .
class azure.mgmt.batchai.models.OperationPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Operation object

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

Bases: msrest.paging.Paged

A paging container for iterating over a list of Usage object

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

Bases: msrest.paging.Paged

A paging container for iterating over a list of Workspace object

class azure.mgmt.batchai.models.ExperimentPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Experiment object

class azure.mgmt.batchai.models.JobPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Job object

class azure.mgmt.batchai.models.FilePaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of File object

class azure.mgmt.batchai.models.RemoteLoginInformationPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of RemoteLoginInformation object

class azure.mgmt.batchai.models.FileServerPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of FileServer object

class azure.mgmt.batchai.models.ClusterPaged(*args, **kwargs)[source]

Bases: msrest.paging.Paged

A paging container for iterating over a list of Cluster object

class azure.mgmt.batchai.models.UsageUnit[source]

Bases: str, enum.Enum

An enumeration.

count = 'Count'
class azure.mgmt.batchai.models.CachingType[source]

Bases: str, enum.Enum

An enumeration.

none = 'none'
readonly = 'readonly'
readwrite = 'readwrite'
class azure.mgmt.batchai.models.StorageAccountType[source]

Bases: str, enum.Enum

An enumeration.

premium_lrs = 'Premium_LRS'
standard_lrs = 'Standard_LRS'
class azure.mgmt.batchai.models.FileServerProvisioningState[source]

Bases: str, enum.Enum

An enumeration.

creating = 'creating'
deleting = 'deleting'
failed = 'failed'
succeeded = 'succeeded'
updating = 'updating'
class azure.mgmt.batchai.models.VmPriority[source]

Bases: str, enum.Enum

An enumeration.

dedicated = 'dedicated'
lowpriority = 'lowpriority'
class azure.mgmt.batchai.models.DeallocationOption[source]

Bases: str, enum.Enum

An enumeration.

requeue = 'requeue'
terminate = 'terminate'
waitforjobcompletion = 'waitforjobcompletion'
class azure.mgmt.batchai.models.ProvisioningState[source]

Bases: str, enum.Enum

An enumeration.

creating = 'creating'
deleting = 'deleting'
failed = 'failed'
succeeded = 'succeeded'
class azure.mgmt.batchai.models.AllocationState[source]

Bases: str, enum.Enum

An enumeration.

resizing = 'resizing'
steady = 'steady'
class azure.mgmt.batchai.models.JobPriority[source]

Bases: str, enum.Enum

An enumeration.

high = 'high'
low = 'low'
normal = 'normal'
class azure.mgmt.batchai.models.ToolType[source]

Bases: str, enum.Enum

An enumeration.

caffe = 'caffe'
caffe2 = 'caffe2'
chainer = 'chainer'
cntk = 'cntk'
custom = 'custom'
horovod = 'horovod'
mpi = 'mpi'
tensorflow = 'tensorflow'
class azure.mgmt.batchai.models.ExecutionState[source]

Bases: str, enum.Enum

An enumeration.

failed = 'failed'
queued = 'queued'
running = 'running'
succeeded = 'succeeded'
terminating = 'terminating'
class azure.mgmt.batchai.models.FileType[source]

Bases: str, enum.Enum

An enumeration.

directory = 'directory'
file = 'file'