Let's allocate the training computation cluster to train our model. The following code is pretty standard and has been listed here for convenience. The basic idea is to allocate a CPU-based, two-node cluster:

# compute target
from azureml.core.compute import ComputeTarget, BatchAiCompute
from azureml.core.compute_target import ComputeTargetException

# choose a name for your cluster
batchai_cluster_name = "traincluster-w3"

try:
    # look for the existing cluster by name
    compute_target = ComputeTarget(workspace=ws,   
                     name=batchai_cluster_name)
    if type(compute_target) is BatchAiCompute:
        print('found compute target {}, just use   
              it.'.format(batchai_cluster_name))
    else:
        print('{} exists but it is not a Batch AI cluster. Please choose a different name.'.format(batchai_cluster_name))
except ComputeTargetException:
    print('creating a new compute target...')
    compute_config = 
BatchAiCompute.provisioning_configuration(vm_size="STANDARD_D2_V2", # 
                                                  small CPU-based VM
                               #vm_priority='lowpriority', # optional
                               autoscale_enabled=True,
                               cluster_min_nodes=0, 
                               cluster_max_nodes=2)
    # create the cluster
    compute_target = ComputeTarget.create(ws, batchai_cluster_name, 
    compute_config)

    # can poll for a minimum number of nodes and for a specific 
    timeout. 
    # if no min node count is provided it uses the scale settings for 
    the cluster
    compute_target.wait_for_completion(show_output=True, 
    min_node_count=None, timeout_in_minutes=20)

    # Use the 'status' property to get a detailed status for the 
    current cluster 
    print(compute_target.status.serialize())