def main(name, vm_size, nodes): ws = Workspace.from_config() try: compute_cluster = ComputeTarget(ws, name) except: compute_config = AmlCompute.provisioning_configuration(vm_size=vm_size, min_nodes=1, max_nodes=nodes) compute_cluster = ComputeTarget.create(ws, name, compute_config) compute_cluster.wait_for_completion(show_output=True)
try: compute_target = ComputeTarget(workspace=workspace, name=cluster_name) print('Found existing compute target') except ComputeTargetException: print('Creating a new compute target...') compute_config = AmlCompute.provisioning_configuration( vm_size='STANDARD_D3_V2', max_nodes=1) # create the cluster compute_target = ComputeTarget.create(workspace, 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) # store the connection string in AML workspace Key Vault # (secret name is 'debugrelay-' + MD5(azrelay_connection_string) ) #hybrid_connection_string_secret =\ # f"debugrelay-{hashlib.md5(azrelay_connection_string.encode('utf-8')).hexdigest()}" #workspace.get_default_keyvault().set_secret(hybrid_connection_string_secret, azrelay_connection_string) # Configuring a PythonScriptStep with a RunConfiguration # that includes debugpy and azure-debug-relay run_config = RunConfiguration() conda_dependencies = run_config.environment.python.conda_dependencies conda_dependencies.add_conda_package("pip") conda_dependencies.add_conda_package("scikit-learn")