def ComputeDelete():
subscription_id = request.json['subscription_id']
resource_group = request.json['resource_group']
workspace_name = request.json['workspace_name']
location = request.json['location']
Cluster_type = request.json['Cluster_type']
cluster_name = request.json['cluster_name']
ws = Workspace(subscription_id=subscription_id,
resource_group=resource_group,
workspace_name=workspace_name)
print("Found workspace {} at location {}".format(ws.name, ws.location))
try:
if Cluster_type == 'Training':
aml_compute = AmlCompute(ws, cluster_name)
print('Found existing AML compute context.')
aml_compute.delete()
else:
aks_target = AksCompute(ws, cluster_name)
print('Found existing AKS compute context.')
aks_target.delete()
print('compute deleted')
return "compute deleted"
except Exception as e:
error_statement = str(e)
print("Error statement: ", error_statement)
return error_statement
run = experiment.submit(config=run_config) #%% run.wait_for_completion(show_output=True) #%% [markdown] # ## View logs #%% [markdown] # You can view logs using [Azure Portal](https://portal.azure.com/), but you can also view using AML run history widget in your notebook. #%% from azureml.widgets import RunDetails RunDetails(run_instance=run).show() #%% [markdown] # You can also explorer metrics with your python code. #%% allmetrics = run.get_metrics() print(allmetrics) #%% [markdown] # ## Remove AML compute #%% # Delete cluster (nbodes) and remove from AML workspace mycompute = AmlCompute(workspace=ws, name='hypertest01') mycompute.delete() #%%
# access the inference_output
data = inference_pipeline_run.find_step_run('inference')[0].get_output_data('inference_output')
# download the predictions to local path
data.download('.', show_progress=True)
# print the predictions
predictions = np.loadtxt(os.path.join('./', data.path_on_datastore, 'results.txt'))
print(predictions)
# ### Cleanup Resources
#
# If you are done experimenting with this quickstart, run the following two cell to clean up resources.
# In[ ]:
schedule.disable()
# In[ ]:
aml_compute.delete()
# In[ ]:
cli_auth = AzureCliAuthentication()
ws = Workspace.from_config(path=workspace_config_settings["path"],
auth=cli_auth,
_file_name=workspace_config_settings["file_name"])
print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\n')
try:
# Loading AMLCompute
print("Loading existing AML Compute")
cluster = AmlCompute(workspace=ws, name=aml_settings["name"])
# Check settings and redeploy if required settings have changed
print("Found existing cluster")
if cluster.vm_size.lower() != aml_settings["vm_size"].lower(
) or cluster.vm_priority.lower() != aml_settings["vm_priority"].lower():
cluster.delete()
cluster.wait_for_completion(show_output=True)
raise ComputeTargetException(
"Cluster is of incorrect size or has incorrect priority. Deleting cluster and provisioning a new one."
)
# Update AMLCompute
#if cluster.provisioning_configuration.min_nodes != aml_settings["min_nodes"] or cluster.provisioning_configuration.max_nodes != aml_settings["max_nodes"] or cluster.provisioning_configuration.idle_seconds_before_scaledown != aml_settings["idle_seconds_before_scaledown"]:
print("Updating settings of Cluster")
cluster.update(min_nodes=aml_settings["min_nodes"],
max_nodes=aml_settings["max_nodes"],
idle_seconds_before_scaledown=aml_settings[
"idle_seconds_before_scaledown"])
# Wait until the operation has completed
cluster.wait_for_completion(show_output=True)
from azureml.core import Workspace
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
ws = Workspace.get(name='akws',
subscription_id='8b3748c0-bb0b-4913-ab5b-c462062118fe',
resource_group='akrg')
cpu_cluster_name = 'tdsp-cluster'
#verify that cluster does not exist
try:
cpu_cluster = AmlCompute(workspace=ws, name=cpu_cluster_name)
cpu_cluster.delete()
print('Deleting cluster...')
except ComputeTargetException:
print('Cluster does not exist in workspace.')
def main():
# Loading input values
print("::debug::Loading input values")
parameters_file = os.environ.get("INPUT_PARAMETERSFILE",
default="workspace.json")
azure_credentials = os.environ.get("INPUT_AZURECREDENTIALS", default="{}")
azure_credentials = json.loads(azure_credentials)
# Loading parameters file
print("::debug::Loading parameters file")
parameters_file_path = os.path.join(".aml", parameters_file)
try:
with open(parameters_file_path) as f:
parameters = json.load(f)
except FileNotFoundError:
print(
f"::error::Could not find parameter file in {parameters_file_path}. Please provide a parameter file in your repository (e.g. .aml/workspace.json)."
)
return
# Loading Workspace
sp_auth = ServicePrincipalAuthentication(
tenant_id=azure_credentials.get("tenantId", ""),
service_principal_id=azure_credentials.get("clientId", ""),
service_principal_password=azure_credentials.get("clientSecret", ""))
try:
print("::debug::Loading existing Workspace")
ws = Workspace.get(
name=parameters.get("name", None),
subscription_id=azure_credentials.get("subscriptionId", ""),
resource_group=parameters.get("resourceGroup", None),
auth=sp_auth)
print("::debug::Successfully loaded existing Workspace")
except AuthenticationException as exception:
print(
f"::error::Could not retrieve user token. Please paste output of `az ad sp create-for-rbac --name <your-sp-name> --role contributor --scopes /subscriptions/<your-subscriptionId>/resourceGroups/<your-rg> --sdk-auth` as value of secret variable: AZURE_CREDENTIALS: {exception}"
)
return
except AuthenticationError as exception:
print(f"::error::Microsoft REST Authentication Error: {exception}")
return
except AdalError as exception:
print(
f"::error::Active Directory Authentication Library Error: {exception}"
)
return
except ProjectSystemException as exception:
print(f"::error::Workspace authorizationfailed: {exception}")
return
# TODO: Create compute if not existing.
try:
# Loading AMLCompute
print("::debug::Loading existing AML Compute")
cluster = AmlCompute(workspace=ws, name=parameters["name"])
# Check settings and redeploy if required settings have changed
print("::debug::Found existing cluster")
if cluster.vm_size.lower() != parameters["vm_size"].lower(
) or cluster.vm_priority.lower() != parameters["vm_priority"].lower():
cluster.delete()
cluster.wait_for_completion(show_output=True)
raise ComputeTargetException(
"Cluster is of incorrect size or has incorrect priority. Deleting cluster and provisioning a new one."
)
# Update AMLCompute
#if cluster.provisioning_configuration.min_nodes != aml_settings["min_nodes"] or cluster.provisioning_configuration.max_nodes != aml_settings["max_nodes"] or cluster.provisioning_configuration.idle_seconds_before_scaledown != aml_settings["idle_seconds_before_scaledown"]:
print("::debug::Updating settings of Cluster")
cluster.update(min_nodes=parameters["min_nodes"],
max_nodes=parameters["max_nodes"],
idle_seconds_before_scaledown=parameters[
"idle_seconds_before_scaledown"])
# Wait until the operation has completed
cluster.wait_for_completion(show_output=True)
print("::debug::Successfully updated Cluster definition")
except ComputeTargetException:
print("::debug::Loading failed")
print("::debug::Creating new AML Compute resource")
compute_config = AmlCompute.provisioning_configuration(
vm_size=parameters["vm_size"],
vm_priority=parameters["vm_priority"],
min_nodes=parameters["min_nodes"],
max_nodes=parameters["max_nodes"],
idle_seconds_before_scaledown=parameters[
"idle_seconds_before_scaledown"],
tags=parameters["tags"],
description=parameters["description"])
# Deploy to VNET if provided
if parameters["vnet_resource_group_name"] and parameters[
"vnet_name"] and parameters["subnet_name"]:
compute_config.vnet_resourcegroup_name = parameters[
"vnet_resource_group_name"]
compute_config.vnet_name = parameters["vnet_name"]
compute_config.subnet_name = parameters["subnet_name"]
# Set Credentials if provided
if parameters["admin_username"] and parameters["admin_user_password"]:
compute_config.admin_username = parameters["admin_username"]
compute_config.admin_user_password = parameters[
"admin_user_password"]
elif parameters["admin_username"] and parameters["admin_user_ssh_key"]:
compute_config.admin_username = parameters["admin_username"]
compute_config.admin_user_ssh_key = parameters[
"admin_user_ssh_key"]
# Create Compute Target
cluster = ComputeTarget.create(
workspace=ws,
name=parameters["name"],
provisioning_configuration=compute_config)
# Wait until the cluster is attached
cluster.wait_for_completion(show_output=True)
# Checking status of AMLCompute Cluster
print("::debug::Checking status of AMLCompute Cluster")
if cluster.provisioning_state == "Failed":
cluster.delete()
raise Exception(
"::debug::Deployment of AMLCompute Cluster failed with the following status: {} and logs: \n{}"
.format(cluster.provisioning_state, cluster.provisioning_errors))
print(parameters)
print(
"::debug::Successfully finished Azure Machine Learning Compute Action")
