main_train_1.py

# Create an experiment
from azureml.core import Experiment
# NOTE: New experiment is created if one with the following name is not found
experiment_name = config['train']['experiment_name']
exp = Experiment(workspace=ws, name=experiment_name)

# Create a target compute - VM
from azureml.core.compute import DsvmCompute
from azureml.core.compute_target import ComputeTargetException

# NOTE: New compute target is created is one with the following name is not found
compute_target_name = config['train']['compute_target_name']

try:
    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)
    print('found existing:', dsvm_compute.name)
except ComputeTargetException:
    print('creating new.')
    dsvm_config = DsvmCompute.provisioning_configuration(vm_size= config['train']['vm_size'])
    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)
    dsvm_compute.wait_for_completion(show_output=True)