create_compute_cluster.py

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

# Load the workspace from config.json
ws = Workspace.from_config()

# Now let's make sure we have a compute resource
cluster_name = "My-Cluster"

# 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...')
    max_nodes = 4
    sku = 'Standard_D2DS_V4'
    compute_config = AmlCompute.provisioning_configuration(vm_size=sku, min_nodes=0, 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)