Compute.py

from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

# Now let's make sure we have a compute resource created
cluster_name = "My-Cluster"  # The name of the cluster
vm_size = 'Standard_D2DS_V4' # There are many different specs available for CPU or GPU tasks.
min_nodes = 0                # This is important to prevent billing while idle
max_nodes = 4                # Azure does limit you to a certain quota, but you can get that extended

# Fetch or create the compute resource
try:
    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name) # This will throw a ComputeTargetException if this doesn't exist
    print('Using existing compute: ' + cluster_name)

except ComputeTargetException:
    # Create the cluster
    print('Provisioning cluster...')
    compute_config = AmlCompute.provisioning_configuration(vm_size=vm_size, min_nodes=min_nodes, max_nodes=max_nodes)
    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)

# Ensure the cluster is ready to go
cpu_cluster.wait_for_completion(show_output=True)