def test_publish_sklearn(self):
from sasctl.tasks import publish_model
from sasctl.services.model_repository import get_model
model = get_model(SCIKIT_MODEL_NAME, PROJECT_NAME)
p = publish_model(model, 'maslocal', max_retries=100)
assert 'score' in p.stepIds
def test_model_publish(self):
pytest.xfail(
'Import job pends forever. Waiting on Support Track #7612766673')
module = publish_model(self.model_name, 'maslocal', max_retries=60)
self.module_name = module.name
print('done')
def test_publish_sklearn_again(self):
from sasctl.tasks import publish_model
from sasctl.services import model_repository as mr
model = mr.get_model(SCIKIT_MODEL_NAME, PROJECT_NAME)
# Should not be able to republish the model by default
with pytest.raises(RuntimeError):
publish_model(model, 'maslocal', max_retries=100)
# Publish should succeed with replace flag
p = publish_model(model, 'maslocal', max_retries=100, replace=True)
# Score step should have been defined in the module
assert 'score' in p.stepIds
# MAS module should automatically have methods bound
assert callable(p.score)
def test_publish_sklearn(self):
from sasctl.tasks import publish_model
from sasctl.services import model_repository as mr
model = mr.get_model(SCIKIT_MODEL_NAME, PROJECT_NAME)
p = publish_model(model, 'maslocal', max_retries=100)
# Score step should have been defined in the module
assert 'score' in p.stepIds
# MAS module should automatically have methods bound
assert callable(p.score)
def test_model_publish(self, request):
"""Publish the imported model to MAS"""
module = publish_model(self.MODEL_NAME,
'maslocal',
max_retries=60,
replace=True)
# SAS module should have a "score" method
assert 'score' in module.stepIds
# Store module name so we can retrieve it in later tests
request.config.cache.set('CAS_MODULE_NAME', module.name)
lr=0.001)
# Export the model as an ASTORE and get a reference to the new ASTORE table
s.deeplearn.dlexportmodel(modelTable=model.model_table,
initWeights=model.model_weights,
casout='astore_table')
astore = s.CASTable('astore_table')
# Connect to the SAS environment
with Session('dsasspre', 'robinswu', 'password'):
# Register the trained model by providing:
# - the ASTORE containing the model
# - a name for the model
# - a name for the project
#
# NOTE: the force=True option will create the project if it does not exist.
model = register_model(astore, 'Deep Learning', 'PGV', force=True)
# Publish the model to SAS® Micro Analytic Service (MAS). Specifically to
# the default MAS service "maslocal".
module = publish_model(model, 'maslocal')
# sasctl wraps the published module with Python methods corresponding to
# the various steps defined in the module (like "predict").
response = module.score(SepalLength=5.1,
SepalWidth=3.5,
PetalLength=1.4,
PetalWidth=0.2)
s.terminate()
project=project, # Register in "Iris" project
force=True) # Create project if it doesn't exist
# Update project properties. Target variable must be set before performance
# definitions can be created.
project = mr.get_project(project)
project['targetVariable'] = 'Price'
project = mr.update_project(project)
# Instruct the project to look for tables in the "Public" CAS library with
# names starting with "boston_" and use these tables to track model
# performance over time.
mm.create_performance_definition(model_name, 'Public', 'boston')
# Publish the model to the real-time scoring engine
module_lm = publish_model(model_name, 'maslocal')
# Select the first row of testing data
x = X_test.iloc[0, :]
# Call the published module and score the record
result = module_lm.score(**x)
print(result)
# Build a second model
dt = DecisionTreeRegressor()
dt.fit(X_train, y_train)
# Register the second model in Model Manager
model_dt = register_model(dt, 'Decision Tree', project, input=X)
