def submit_experiment(path_to_dataset_in_datastore, compute_target_name,
experiment_name, env_name, model_name, model_save_path):
try:
ws = _establish_connection_to_aml_workspace()
except Exception as e:
raise e
try:
experiment = Experiment(workspace=ws, name=experiment_name)
running_config = _create_running_config(ws,
path_to_dataset_in_datastore,
compute_target_name, env_name,
model_save_path)
############### submit experiment and get url ################
run = experiment.submit(running_config)
url = run.get_portal_url()
print("The details for this experiment is here:\n {}".format(url))
run.wait_for_completion()
print(
"Experiment run has completed, Note:This message does NOT suggetst your experiment is successful, go to {} to find out logs and status for your experiment"
.format(url))
############### submit experiment and get url ################
except Exception as e:
print("Submit Experiment failed!")
raise e
try:
############### register model ######################
run.register_model(model_name=model_name,
model_path=os.path.join(model_save_path,
'saved_model.pt'))
except Exception as e:
print("Failed to register due to {}!".format(e))
def get_custom_env(env_name):
try:
ws = _establish_connection_to_aml_workspace()
except Exception as e:
raise e
try:
new_env = Environment.get(ws, env_name)
return new_env
except Exception as e:
raise e
def show_list_of_all_environments():
try:
ws = _establish_connection_to_aml_workspace()
except Exception as e:
raise e
try:
env_list = Environment.list(ws)
for key in env_list:
print(key)
except Exception as e:
raise e
def register_new_conda_environment(env_name, yml_file_path):
try:
ws = _establish_connection_to_aml_workspace()
except Exception as e:
raise e
try:
new_env = Environment.from_conda_specification(name=env_name,
file_path=yml_file_path)
new_env.register(ws)
print("New environment {} has been registerd".format(env_name))
except Exception as e:
raise e
def _create_inference_config(inference_env_name):
try:
ws=_establish_connection_to_aml_workspace()
except Exception as e:
print("failed to connect to workspce")
raise e
try:
environment=Environment.get(workspace=ws,name=inference_env_name)
inference_config=InferenceConfig(entry_script="score.py",
environment=environment,
source_directory=r'deployment')
return inference_config
except Exception as e:
print("failed to create inference config")
raise e
def create_compute_instance():
try:
ws=_establish_connection_to_aml_workspace()
except Exception as e:
raise e
try:
compute_target_name='testConfDSVM'
attach_config=RemoteCompute.attach_configuration(
# resource_id="/subscriptions/e4da59ca-11b0-454e-a89a-cd711dea9094/resourceGroups/Barrys-ConfidentialML-Test/providers/Microsoft.Compute/virtualMachines/Barrys-ConfidentialML-TestComputeTarget",
resource_id="/subscriptions/e4da59ca-11b0-454e-a89a-cd711dea9094/resourcegroups/Barrys-ConfidentialML-Test/providers/Microsoft.Compute/virtualMachines/BarrysTestConfidentialDSVM",
ssh_port=22,
username="******",
password="******"
)
compute=ComputeTarget.attach(ws,compute_target_name,attach_config)
compute.wait_for_completion(show_output=True)
except Exception as e:
raise e
def create_compute_cluster():
try:
print("Start searching for user's compute target...")
ws=_establish_connection_to_aml_workspace()
except Exception as e:
raise e
try:
compute_target = _get_compute_target(ws,"StandardCompute")
print("User already has a compute target!")
except ComputeTargetException:
print("User does not have a compute target, starts creating...")
compute_config = AmlCompute.provisioning_configuration(vm_size="Standard_F4s_v2",
max_nodes=4,
idle_seconds_before_scaledown=2400)
cpu_cluster = ComputeTarget.create(ws, "StandardCompute", compute_config)
cpu_cluster.wait_for_completion(show_output=True)
print("compute target craeted!")
except Exception as e:
raise e
def _start_deploy_model(inference_config,deployment_config,model_name,model_version,deployment_name):
try:
ws=_establish_connection_to_aml_workspace()
except Exception as e:
print("failed to connect to workspce")
raise e
try:
model=Model(workspace=ws,name=model_name,version=model_version)
service=model.deploy(workspace=ws,
name=deployment_name,
models=[model],
inference_config=inference_config,
deployment_config=deployment_config,
overwrite=True)
service.wait_for_deployment()
print(service.state)
print("Deployed at {}".format(service.scoring_uri))
except Exception as e:
raise e