def download_model(ws, experiment_name, run_id, input_location, output_location):
"""Download the pretrained model
Args:
ws: workspace to access the experiment
experiment_name: Name of the experiment in which model is saved
run_id: Run Id of the experiment in which model is pre-trained
input_location: Input location in a RUN Id
output_location: Location for saving the model
"""
experiment = Experiment(workspace=ws, name=experiment_name)
# Download the model on which evaluation need to be done
run = Run(experiment, run_id=run_id)
if input_location.endswith(".h5"):
run.download_file(input_location, output_location)
elif input_location.endswith(".ckpt"):
run.download_files(prefix=input_location,
output_directory=output_location)
else:
raise NameError(f"{input_location}'s path extension not supported")
print("Successfully downloaded model")
def deploy_model(ws, aci_service_name, experiment_name, asset_name,
asset_label, run_id, cpu_cores, memory_gb, entry_script):
env = create_env_from_requirements(endpoint=True)
inference_config = InferenceConfig(source_directory=os.getcwd(),
entry_script=entry_script,
environment=env)
deployment_config = AciWebservice.deploy_configuration(cpu_cores=cpu_cores,
memory_gb=memory_gb)
# model name
model_name = get_model_register_name(run_id)
try:
model = Model(ws, name=model_name)
except:
# creating directory for download Model files for Model register
tmp_path = create_tempdir(name='download_tmp')
register_path = create_directory(AML_MLAPP_FOLDER, path=tmp_path)
# getting RUN context
experiment = Experiment(workspace=ws, name=experiment_name)
tags = {"run_id": run_id, "asset_name": asset_name}
if asset_label is not None:
tags["asset_label"] = asset_label
selected_run_id = None
for run in Run.list(experiment,
tags=tags,
include_children=True,
status='Completed'):
run_metrics = run.get_metrics()
exp_saved_run_id = run_metrics.get("run_id")
if exp_saved_run_id == run_id:
selected_run_id = run.id
break
if selected_run_id is None:
raise Exception(
'ERROR: there is no matching Run object that associated with the run id %s in this experiment.'
% str(run_id))
current_run = Run(experiment=experiment, run_id=selected_run_id)
# download files from run object
current_run.download_files(output_directory=register_path)
# register model
model = Model.register(ws,
model_path=register_path,
model_name=model_name,
tags=tags,
description=asset_name)
# deletes tmp dir and all content
delete_directory_with_all_contents(tmp_path)
# deploy model
service = None
try:
service = Webservice(ws, name=aci_service_name)
service.update(models=[model], inference_config=inference_config)
except WebserviceException as e:
if service:
service.delete()
service = Model.deploy(ws, aci_service_name, [model], inference_config,
deployment_config)
service.wait_for_deployment(True)
print(f"Last Run ID: {run_id}")
# register last run_id as environment variable so it can be used in %sh commands
os.environ['RUN_ID_LAST'] = run_id
# print(os.getenv('RUN_ID_LAST'))
# COMMAND ----------
import mlflow.sklearn
# run_id = "434c0e56-307a-4e40-a170-343530c77386"
run = Run(experiment, run_id=run_id)
run.get_file_names()
# COMMAND ----------
run.download_files(output_directory=f"/tmp/{run_id}")
# COMMAND ----------
# MAGIC %sh
# MAGIC ls -l /tmp/$RUN_ID_LAST/random_forest_model
# COMMAND ----------
model_uri_aml = f"/tmp/{run_id}/random_forest_model"
model = mlflow.sklearn.load_model(model_uri_aml)
# COMMAND ----------
model
