def _update_nodes_information(params, image, vm_size, min_nodes, max_nodes): """Updates cluster's nodes information. :param models.ClusterCreateParameters params: cluster create parameters. :param str or None image: image. :param str or None vm_size: VM size. :param int min_nodes: min number of nodes. :param int or None max_nodes: max number of nodes. :return models.ClusterCreateParameters: updated parameters. """ result = copy.deepcopy(params) if vm_size: result.vm_size = vm_size if not result.vm_size: raise CLIError('Please provide VM size') if image: result.virtual_machine_configuration = models.VirtualMachineConfiguration(_get_image_reference_or_die(image)) if min_nodes == max_nodes: result.scale_settings = models.ScaleSettings(manual=models.ManualScaleSettings(min_nodes)) elif max_nodes is not None: result.scale_settings = models.ScaleSettings(auto_scale=models.AutoScaleSettings(min_nodes, max_nodes)) if not result.scale_settings or (not result.scale_settings.manual and not result.scale_settings.auto_scale): raise CLIError('Please provide scale setting for the cluster via configuration file or via --min and --max ' 'parameters.') return result
def set_cluster_auto_scale_parameters(client, resource_group, cluster_name, min_nodes, max_nodes): return client.update( resource_group, cluster_name, scale_settings=models.ScaleSettings( auto_scale=models.AutoScaleSettings(min_nodes, max_nodes)))
def test_auto_scaling(self, resource_group, location, storage_account, storage_account_key): """Tests auto-scaling""" # Create the cluster with no nodes. cluster = helpers.create_cluster(self.client, location, resource_group.name, self.cluster_name, 'STANDARD_D1', 0, storage_account.name, storage_account_key) # Switch the cluster into auto-scale mode self.client.clusters.update( resource_group.name, self.cluster_name, scale_settings=models.ScaleSettings( auto_scale=models.AutoScaleSettings(minimum_node_count=0, maximum_node_count=1))) # Submit a task. BatchAI must increase the number of nodes to execute the task. self.assertCanRunJobOnHost(resource_group, location, cluster.id, timeout_sec=helpers.AUTO_SCALE_TIMEOUT_SEC) # Verify that cluster downsized to zero since there are no more jobs for it self.assertEqual( helpers.wait_for_nodes(self.is_live, self.client, resource_group.name, self.cluster_name, 0, helpers.NODE_STARTUP_TIMEOUT_SEC), 0) self.client.clusters.delete(resource_group.name, self.cluster_name).result()
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 assertCanResizeCluster(self, resource_group, target): self.client.clusters.update(resource_group.name, Helpers.DEFAULT_WORKSPACE_NAME, self.cluster_name, scale_settings=models.ScaleSettings( manual=models.ManualScaleSettings(target_node_count=target))) self.assertEqual( Helpers.wait_for_nodes(self.is_live, self.client, resource_group.name, self.cluster_name, target, Helpers.NODE_STARTUP_TIMEOUT_SEC), target) Helpers.assert_remote_login_info_reported_for_nodes(self, self.client, resource_group.name, self.cluster_name, target)
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 _get_scale_settings(initial_count, min_count, max_count): """Returns scale settings for a cluster with gine parameters""" if not initial_count and not min_count and not max_count: # Get from the config file return None if sum([1 if v is not None else 0 for v in (min_count, max_count)]) == 1: raise CLIError('You need to either provide both min and max node counts or not provide any of them') if min_count is not None and max_count is not None and min_count > max_count: raise CLIError('Maximum nodes count must be greater or equal to minimum nodes count') if min_count == max_count: if min_count is None or initial_count == min_count: return models.ScaleSettings( manual=models.ManualScaleSettings(target_node_count=initial_count)) if initial_count is None: return models.ScaleSettings( manual=models.ManualScaleSettings(target_node_count=min_count) ) return models.ScaleSettings( auto_scale=models.AutoScaleSettings( minimum_node_count=min_count, maximum_node_count=max_count, initial_node_count=initial_count or 0))
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 resize_cluster(client, resource_group, cluster_name, target): return client.update(resource_group, cluster_name, scale_settings=models.ScaleSettings( manual=models.ManualScaleSettings(target_node_count=target)))
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()
for f in ['Train-28x28_cntk_text.txt', 'Test-28x28_cntk_text.txt', 'ConvNet_MNIST.py']: filesystem.create_file_from_path(fileshare, "data", f, "z:/script/"+f) ## Create Cluster 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)], ), ), )