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 _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 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 _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))
