def main():
run = Run.get_context()
if (run.id.startswith('OfflineRun')):
from dotenv import load_dotenv
sys.path.append(os.path.abspath("./code/util")) # NOQA: E402
from model_helper import get_model_by_tag
# For local development, set values in this section
load_dotenv()
workspace_name = os.environ.get("WORKSPACE_NAME")
experiment_name = os.environ.get("EXPERIMENT_NAME")
resource_group = os.environ.get("RESOURCE_GROUP")
subscription_id = os.environ.get("SUBSCRIPTION_ID")
build_id = os.environ.get('BUILD_BUILDID')
aml_workspace = Workspace.get(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group)
ws = aml_workspace
exp = Experiment(ws, experiment_name)
else:
sys.path.append(os.path.abspath("./util")) # NOQA: E402
from model_helper import get_model_by_tag
ws = run.experiment.workspace
exp = run.experiment
e = Env()
parser = argparse.ArgumentParser("register")
parser.add_argument(
"--build_id",
type=str,
help="The Build ID of the build triggering this pipeline run",
)
parser.add_argument("--output_model_version_file",
type=str,
default="model_version.txt",
help="Name of a file to write model version to")
args = parser.parse_args()
if (args.build_id is not None):
build_id = args.build_id
model_name = e.model_name
try:
tag_name = 'BuildId'
model = get_model_by_tag(model_name, tag_name, build_id, exp.workspace)
if (model is not None):
print("Model was registered for this build.")
if (model is None):
print("Model was not registered for this run.")
sys.exit(1)
except Exception as e:
print(e)
print("Model was not registered for this run.")
sys.exit(1)
# Save the Model Version for other AzDO jobs after script is complete
if args.output_model_version_file is not None:
with open(args.output_model_version_file, "w") as out_file:
out_file.write(str(model.version))
def main():
run = Run.get_context()
if (run.id.startswith('OfflineRun')):
from dotenv import load_dotenv
sys.path.append(os.path.abspath("./code/util")) # NOQA: E402
from model_helper import get_model_by_tag
# For local development, set values in this section
load_dotenv()
workspace_name = os.environ.get("WORKSPACE_NAME")
experiment_name = os.environ.get("EXPERIMENT_NAME")
resource_group = os.environ.get("RESOURCE_GROUP")
subscription_id = os.environ.get("SUBSCRIPTION_ID")
tenant_id = os.environ.get("TENANT_ID")
model_name = os.environ.get("MODEL_NAME")
app_id = os.environ.get('SP_APP_ID')
app_secret = os.environ.get('SP_APP_SECRET')
build_id = os.environ.get('BUILD_BUILDID')
# run_id useful to query previous runs
run_id = "bd184a18-2ac8-4951-8e78-e290bef3b012"
service_principal = ServicePrincipalAuthentication(
tenant_id=tenant_id,
service_principal_id=app_id,
service_principal_password=app_secret)
aml_workspace = Workspace.get(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group,
auth=service_principal)
ws = aml_workspace
exp = Experiment(ws, experiment_name)
else:
sys.path.append(os.path.abspath("./util")) # NOQA: E402
from model_helper import get_model_by_tag
ws = run.experiment.workspace
exp = run.experiment
run_id = 'amlcompute'
parser = argparse.ArgumentParser("register")
parser.add_argument(
"--build_id",
type=str,
help="The Build ID of the build triggering this pipeline run",
)
parser.add_argument(
"--run_id",
type=str,
help="Training run ID",
)
parser.add_argument(
"--model_name",
type=str,
help="Name of the Model",
default="sklearn_regression_model.pkl",
)
parser.add_argument(
"--validate",
type=str,
help="Set to true to only validate if model is registered for run",
default=False,
)
args = parser.parse_args()
if (args.build_id is not None):
build_id = args.build_id
if (args.run_id is not None):
run_id = args.run_id
if (run_id == 'amlcompute'):
run_id = run.parent.id
if (args.validate is not None):
validate = args.validate
model_name = args.model_name
if (validate):
try:
tag_name = 'BuildId'
model = get_model_by_tag(model_name, tag_name, build_id,
exp.workspace)
if (model is not None):
print("Model was registered for this build.")
if (model is None):
print("Model was not registered for this run.")
sys.exit(1)
except Exception as e:
print(e)
print("Model was not registered for this run.")
sys.exit(1)
else:
if (build_id is None):
register_aml_model(model_name, exp, run_id)
else:
run.tag("BuildId", value=build_id)
register_aml_model(model_name, exp, run_id, build_id)
build_id = args.build_id
if (args.run_id is not None):
run_id = args.run_id
if (run_id == 'amlcompute'):
run_id = run.parent.id
model_name = args.model_name
metric_eval = "mse"
run.tag("BuildId", value=build_id)
# Paramaterize the matrices on which the models should be compared
# Add golden data set on which all the model performance can be evaluated
try:
firstRegistration = False
tag_name = 'experiment_name'
model = get_model_by_tag(model_name, tag_name, exp.name, ws)
if (model is not None):
production_model_run_id = model.run_id
# Get the run history for both production model and
# newly trained model and compare mse
production_model_run = Run(exp, run_id=production_model_run_id)
new_model_run = run.parent
print("Production model run is", production_model_run)
production_model_mse = \
production_model_run.get_metrics().get(metric_eval)
new_model_mse = new_model_run.get_metrics().get(metric_eval)
if (production_model_mse is None or new_model_mse is None):