def test_auto_artifact_dependencies():
clf = svm.SVC(gamma='scale')
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)
# Create a iris classifier service
iris_classifier_service = IrisClassifier()
# Pack it with the newly trained model artifact
iris_classifier_service.pack('model', clf)
# Save the prediction service to a BentoService bundle
saved_path = iris_classifier_service.save()
with open(os.path.join(saved_path, 'requirements.txt')) as f:
requirements_txt_content = f.read()
dependencies = requirements_txt_content.split('\n')
dependencies = [dep.split('==')[0] for dep in dependencies]
assert 'scikit-learn' in dependencies
assert 'bentoml' in dependencies
# Test that dependencies also wrote to BentoServiceMetadat config file
bs_matadata = load_bento_service_metadata(saved_path)
dependencies = bs_matadata.env.pip_dependencies.split('\n')
dependencies = [dep.split('==')[0] for dep in dependencies]
assert 'scikit-learn' in dependencies
assert 'bentoml' in dependencies
delete_saved_bento_service(iris_classifier_service.name,
iris_classifier_service.version)
def run_test_with_bento_service_class(bento_service_class):
clf = svm.SVC(gamma='scale')
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)
# Create a bento service instance
bento_service = bento_service_class()
# Pack it with the newly trained model artifact
bento_service.pack('model', clf)
# Save the prediction service to a BentoService bundle
bento_service.save()
bento_name = f"{bento_service.name}:{bento_service.version}"
run_command = [
"bentoml",
"run",
bento_name,
"predict",
"--input",
"[[5.1, 3.5, 1.4, 0.2]]",
"-q",
]
print(f"running command {' '.join(run_command)}:")
with subprocess.Popen(run_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE) as proc:
output = proc.stdout.read().decode('utf-8')
assert output.replace('\r\n', '\n') == '[0]\n'
delete_saved_bento_service(bento_service.name, bento_service.version)
def run_predict(bento_service, input_data, is_file=False, options=[]):
bento_name = f"{bento_service.name}:{bento_service.version}"
run_predict_deployment_command = [
'bentoml',
'run',
bento_name,
'predict',
*options,
'--input-file' if is_file else '--input',
input_data,
]
logger.info(f'Run predict command: {run_predict_deployment_command}')
try:
with subprocess.Popen(
run_predict_deployment_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=os.environ,
) as proc:
stdout = proc.stdout.read().decode('utf-8')
stderr = proc.stderr.read().decode('utf-8')
logger.info(f'Got output: {stdout}')
return stdout, stderr
finally:
delete_saved_bento_service(bento_service.name, bento_service.version)
def test_auto_artifact_dependencies():
clf = _fit_clf()
# Create a iris classifier service
iris_classifier_service = IrisClassifier()
# Pack it with the newly trained model artifact
iris_classifier_service.pack('model', clf)
# Save the prediction service to a BentoService bundle
saved_path = iris_classifier_service.save()
# parse generated requirements.txt
dependencies = _dependencies_to_requirements(
_parse_dependencies(saved_path))
_assert_in_dependencies(['scikit-learn', 'bentoml'], dependencies)
# Test that dependencies also wrote to BentoServiceMetadata config file
bs_metadata = load_bento_service_metadata(saved_path)
dependencies = bs_metadata.env.pip_packages
dependencies = _dependencies_to_requirements(dependencies)
_assert_in_dependencies(['scikit-learn', 'bentoml'], dependencies)
# Clean up
delete_saved_bento_service(iris_classifier_service.name,
iris_classifier_service.version)
def test_requirements_txt_file():
clf = _fit_clf()
iris_classifier_service = IrisClassifierPipEnv()
iris_classifier_service.pack('model', clf)
saved_path = iris_classifier_service.save()
dependencies = _dependencies_to_requirements(
_parse_dependencies(saved_path))
_assert_in_dependencies(
['scikit-learn', 'azure-cli', 'psycopg2-binary', 'bentoml'],
dependencies)
bs_metadata = load_bento_service_metadata(saved_path)
requirements_txt = bs_metadata.env.requirements_txt
requirements_content = open("./tests/pipenv_requirements.txt", "r").read()
assert requirements_txt == requirements_content
delete_saved_bento_service(iris_classifier_service.name,
iris_classifier_service.version)
def test_save_duplicated_bento_exception_raised(example_bento_service_class):
test_model = TestModel()
svc = example_bento_service_class()
svc.pack("model", test_model)
saved_path = svc.save()
svc_metadata = load_bento_service_metadata(saved_path)
assert svc.version == svc_metadata.version
with pytest.raises(BentoMLException):
with patch.object(bentoml.BentoService, 'save_to_dir') as save_to_dir_method:
# attempt to save again
svc.save()
save_to_dir_method.assert_not_called()
# reset svc version
svc.set_version()
saved_path = svc.save()
svc_metadata_new = load_bento_service_metadata(saved_path)
assert svc.version == svc_metadata_new.version
delete_saved_bento_service(svc_metadata.name, svc_metadata.version)
delete_saved_bento_service(svc_metadata_new.name, svc_metadata_new.version)
def test_bundle_local_dependencies():
clf = svm.SVC(gamma='scale')
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)
# Create a iris classifier service
iris_classifier_service = IrisClassifier()
# Pack it with the newly trained model artifact
iris_classifier_service.pack('model', clf)
# Save the prediction service to a BentoService bundle
iris_classifier_service.save()
bento_name = f"{iris_classifier_service.name}:{iris_classifier_service.version}"
run_command = [
"bentoml",
"run",
bento_name,
"predict",
"--input",
"[[5.1, 3.5, 1.4, 0.2]]",
"-q",
]
print(f"running command {' '.join(run_command)}:")
with subprocess.Popen(run_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE) as proc:
output = proc.stdout.read().decode('utf-8')
assert output == '[0]\n'
delete_saved_bento_service(iris_classifier_service.name,
iris_classifier_service.version)