def create_cluster(cmd, client, # pylint: disable=too-many-locals resource_group, cluster_name, json_file=None, location=None, user_name=None, ssh_key=None, password=None, image='UbuntuLTS', vm_size=None, min_nodes=0, max_nodes=None, nfs_name=None, nfs_resource_group=None, nfs_mount_path='nfs', azure_file_share=None, afs_mount_path='afs', container_name=None, container_mount_path='bfs', account_name=None, account_key=None, raw=False): if json_file: with open(json_file) as f: json_obj = json.load(f) params = _get_deserializer()('ClusterCreateParameters', json_obj) else: params = models.ClusterCreateParameters(None, None, None) params = _update_cluster_create_parameters_with_env_variables(cmd.cli_ctx, params, account_name, account_key) params = _update_user_account_settings(params, user_name, ssh_key, password) if location: params.location = location if not params.location: raise CLIError('Please provide location for cluster creation.') params = _update_nodes_information(params, image, vm_size, min_nodes, max_nodes) if nfs_name: file_server = client.file_servers.get(nfs_resource_group if nfs_resource_group else resource_group, nfs_name) params = _add_nfs_to_cluster_create_parameters(params, file_server.id, nfs_mount_path) if azure_file_share: params = _add_azure_file_share_to_cluster_create_parameters(cmd.cli_ctx, params, azure_file_share, afs_mount_path, account_name, account_key) if container_name: params = _add_azure_container_to_cluster_create_parameters(cmd.cli_ctx, params, container_name, container_mount_path, account_name, account_key) return client.clusters.create(resource_group, cluster_name, params, raw=raw)
def prepare_batch_ai_workspace(client, service, config): # Create Batch AI workspace client.workspaces.create(config.workspace_resource_group, config.workspace, config.location) # Create GPU cluster parameters = models.ClusterCreateParameters( # VM size. Use N-series for GPU vm_size=config.workspace_vm_size, # Configure the ssh users user_account_settings=models.UserAccountSettings( admin_user_name=config.admin, admin_user_password=config.admin_password), # Number of VMs in the cluster scale_settings=models.ScaleSettings( manual=models.ManualScaleSettings(target_node_count=config.workspace_node_count) ), # Configure each node in the cluster node_setup=models.NodeSetup( # Mount shared volumes to the host mount_volumes=models.MountVolumes( azure_file_shares=[ models.AzureFileShareReference( account_name=config.storage_account_name, credentials=models.AzureStorageCredentialsInfo( account_key=config.storage_account_key), azure_file_url='https://{0}/{1}'.format( service.primary_endpoint, config.workspace_file_share), relative_mount_path=config.workspace_relative_mount_path)], ), ), ) client.clusters.create(config.workspace_resource_group, config.workspace, config.workspace_cluster, parameters).result()
def create_cluster(cmd, client, # pylint: disable=too-many-locals resource_group, cluster_name, json_file=None, location=None, user_name=None, ssh_key=None, password=None, generate_ssh_keys=None, image=None, custom_image=None, use_auto_storage=False, vm_size=None, vm_priority='dedicated', target=None, min_nodes=None, max_nodes=None, subnet=None, nfs_name=None, nfs_resource_group=None, nfs_mount_path='nfs', azure_file_share=None, afs_mount_path='afs', container_name=None, container_mount_path='bfs', account_name=None, account_key=None, setup_task=None, setup_task_output=None): if generate_ssh_keys: _generate_ssh_keys() if ssh_key is None: ssh_key = _get_default_ssh_public_key_location() _ensure_resource_not_exist(client.clusters, resource_group, cluster_name) _verify_subnet(client, subnet, nfs_name, nfs_resource_group or resource_group) if json_file: with open(json_file) as f: json_obj = json.load(f) params = _get_deserializer()('ClusterCreateParameters', json_obj) else: # noinspection PyTypeChecker params = models.ClusterCreateParameters() if params.node_setup: params.node_setup.mount_volumes = _patch_mount_volumes( cmd.cli_ctx, params.node_setup.mount_volumes, account_name, account_key) params = _update_user_account_settings(params, user_name, ssh_key, password) params.location = location or _get_resource_group_location(cmd.cli_ctx, resource_group) params = _update_nodes_information(params, image, custom_image, vm_size, vm_priority, target, min_nodes, max_nodes) if nfs_name or azure_file_share or container_name: params.node_setup = params.node_setup or models.NodeSetup() mount_volumes = params.node_setup.mount_volumes if params.node_setup else None if nfs_name: file_server = client.file_servers.get(nfs_resource_group or resource_group, nfs_name) mount_volumes = _add_nfs_to_mount_volumes(mount_volumes, file_server.id, nfs_mount_path) if azure_file_share: mount_volumes = _add_azure_file_share_to_mount_volumes(cmd.cli_ctx, mount_volumes, azure_file_share, afs_mount_path, account_name, account_key) if container_name: mount_volumes = _add_azure_container_to_mount_volumes(cmd.cli_ctx, mount_volumes, container_name, container_mount_path, account_name, account_key) if use_auto_storage: auto_storage_account, auto_storage_key = _configure_auto_storage(cmd.cli_ctx, params.location) mount_volumes = _add_azure_file_share_to_mount_volumes( cmd.cli_ctx, mount_volumes, AUTO_STORAGE_SHARE_NAME, AUTO_STORAGE_SHARE_PATH, auto_storage_account, auto_storage_key) mount_volumes = _add_azure_container_to_mount_volumes( cmd.cli_ctx, mount_volumes, AUTO_STORAGE_CONTAINER_NAME, AUTO_STORAGE_CONTAINER_PATH, auto_storage_account, auto_storage_key) if mount_volumes: if params.node_setup is None: params.node_setup = models.NodeSetup() params.node_setup.mount_volumes = mount_volumes if subnet: params.subnet = models.ResourceId(id=subnet) if setup_task: params = _add_setup_task(setup_task, setup_task_output, params) return client.clusters.create(resource_group, cluster_name, params)
def test_experiments_isolation(self, resource_group, location): self.client.workspaces.create(resource_group.name, 'first', location).result() self.client.workspaces.create(resource_group.name, 'second', location).result() # Create a cluster, two experiments and a job in each experiment for workspace in ['first', 'second']: cluster = self.client.clusters.create( resource_group.name, workspace, 'cluster', parameters=models.ClusterCreateParameters( vm_size='STANDARD_D1', scale_settings=models.ScaleSettings( manual=models.ManualScaleSettings( target_node_count=0)), user_account_settings=models.UserAccountSettings( admin_user_name=helpers.ADMIN_USER_NAME, admin_user_password=helpers.ADMIN_USER_PASSWORD), vm_priority='lowpriority')).result() for experiment in ['exp1', 'exp2']: self.client.experiments.create(resource_group.name, workspace, experiment).result() self.client.jobs.create( resource_group.name, workspace, experiment, 'job', parameters=models.JobCreateParameters( cluster=models.ResourceId(id=cluster.id), node_count=1, std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT', custom_toolkit_settings=models.CustomToolkitSettings( command_line='true'))).result() # Delete exp1 in the first workspace self.client.experiments.delete(resource_group.name, 'first', 'exp1').result() # Ensure the experiment was actually deleted self.assertRaises( CloudError, lambda: self.client.experiments.get( resource_group.name, 'first', 'exp1')) for workspace in ['first', 'second']: # Ensure the clusters are not affected self.client.clusters.get(resource_group.name, workspace, 'cluster') # Ensure the other experiments are not affected for experiment in ['exp1', 'exp2']: if workspace == 'first' and experiment == 'exp1': continue self.client.experiments.get(resource_group.name, workspace, experiment) job = self.client.jobs.get(resource_group.name, workspace, experiment, 'job') # And check the job are not terminated self.assertEqual(job.execution_state, models.ExecutionState.queued)
def cluster_parameters_for(config, container_settings, volumes): return models.ClusterCreateParameters( virtual_machine_configuration=models.VirtualMachineConfiguration( image_reference=models.ImageReference(offer='UbuntuServer', publisher='Canonical', sku='16.04-LTS', version='16.04.201708151')), location=config.location, vm_size=config.vm_type, user_account_settings=models.UserAccountSettings( admin_user_name=config.admin_user['name'], admin_user_password=config.admin_user['password']), scale_settings=models.ScaleSettings(manual=models.ManualScaleSettings( target_node_count=config.node_count)), node_setup=models.NodeSetup(mount_volumes=volumes))
def create_cluster(client, location, resource_group, cluster_name, vm_size, target_nodes, storage_account, storage_account_key, file_servers=None, file_systems=None, subnet_id=None, setup_task_cmd=None, setup_task_env=None, setup_task_secrets=None): """Creates a cluster with given parameters and mounted Azure Files :param BatchAIManagementClient client: client instance. :param str location: location. :param str resource_group: resource group name. :param str cluster_name: name of the cluster. :param str vm_size: vm size. :param int target_nodes: number of nodes. :param str storage_account: name of the storage account. :param str storage_account_key: storage account key. :param list(models.FileServerReference) file_servers: file servers. :param list(models.UnmanagedFileServerReference) file_systems: file systems. :param str setup_task_cmd: start task cmd line. :param dict[str, str] setup_task_env: environment variables for start task. :param dict[str, str] setup_task_secrets: environment variables with secret values for start task, server doesn't return values for these environment variables in get cluster responses. :param str subnet_id: virtual network subnet id. :return models.Cluster: the created cluster """ Helpers._create_file_share(storage_account, storage_account_key) setup_task = None if setup_task_cmd: setup_task = models.SetupTask( command_line=setup_task_cmd, environment_variables=[ models.EnvironmentVariable(name=k, value=v) for k, v in setup_task_env.items() ], secrets=[ models.EnvironmentVariableWithSecretValue(name=k, value=v) for k, v in setup_task_secrets.items() ], std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format( Helpers.AZURE_FILES_MOUNTING_PATH)) client.workspaces.create(resource_group, Helpers.DEFAULT_WORKSPACE_NAME, location).result() return client.clusters.create( resource_group, Helpers.DEFAULT_WORKSPACE_NAME, cluster_name, parameters=models.ClusterCreateParameters( vm_size=vm_size, scale_settings=models.ScaleSettings( manual=models.ManualScaleSettings( target_node_count=target_nodes)), node_setup=models.NodeSetup( mount_volumes=models.MountVolumes( azure_file_shares=[ models.AzureFileShareReference( azure_file_url= 'https://{0}.file.core.windows.net/{1}'.format( storage_account, Helpers.AZURE_FILES_NAME), relative_mount_path=Helpers. AZURE_FILES_MOUNTING_PATH, account_name=storage_account, credentials=models.AzureStorageCredentialsInfo( account_key=storage_account_key), ) ], file_servers=file_servers, unmanaged_file_systems=file_systems), setup_task=setup_task), subnet=subnet_id, user_account_settings=models.UserAccountSettings( admin_user_name=Helpers.ADMIN_USER_NAME, admin_user_password=Helpers.ADMIN_USER_PASSWORD), vm_priority='lowpriority')).result()
cluster_name = 'shwarscluster' relative_mount_point = 'azurefileshare' parameters = models.ClusterCreateParameters( location='northeurope', vm_size='STANDARD_NC6', user_account_settings=models.UserAccountSettings( admin_user_name="shwars", admin_user_password="******"), scale_settings=models.ScaleSettings( manual=models.ManualScaleSettings(target_node_count=1) ), node_setup=models.NodeSetup( # Mount shared volumes to the host mount_volumes=models.MountVolumes( azure_file_shares=[ models.AzureFileShareReference( account_name=storage_account_name, credentials=models.AzureStorageCredentialsInfo( account_key=storage_account_key), azure_file_url='https://{0}.file.core.windows.net/{1}'.format( storage_account_name, fileshare), relative_mount_path = relative_mount_point)], ), ), ) client.clusters.create(resource_group_name, cluster_name, parameters).result() cluster = client.clusters.get(resource_group_name, cluster_name)