def _post_model(files, push_api, auth_api, tries, max_tries, extra_headers,
options) -> Optional[str]:
'''Attempts to post the model to Acumos, returns the docker image URI'''
headers = {
'Authorization': get_jwt(auth_api),
'isCreateMicroservice':
'true' if options.create_microservice else 'false',
'deploy': 'true' if options.deploy else 'false'
}
if extra_headers is not None:
headers.update(extra_headers)
resp = requests.post(push_api, files=files, headers=headers)
if resp.ok:
logger.info("Model pushed successfully to {}".format(push_api))
if options.create_microservice:
try:
docker_image_uri = resp.json()["dockerImageUri"]
logger.info(
f"Acumos model docker image successfully created: {docker_image_uri}"
)
return docker_image_uri
except KeyError:
logger.warning(
"Docker image URI could not be found in server response, "
"on-boarding server is probably running a version prior to Demeter."
)
else:
clear_jwt()
if resp.status_code == 401 and tries != max_tries:
logger.warning(
'Model push failed due to an authorization failure. Clearing credentials and trying again'
)
_post_model(files, push_api, auth_api, tries + 1, max_tries,
extra_headers, options)
elif resp.status_code == 500 and tries != max_tries:
with ExitStack() as stack:
print(
"\x1b[31m Warning : Status code 500 received, Trying with the 0.4.0 Clio schema\x1b[39m \n"
)
dump_dir = str(files['model'][1]).split('name=')[1].split(
'model.zip')[0].split('\'')[1]
meta_clio = stack.enter_context(
open(path_join(dump_dir, 'metadata_clio.json')))
model = stack.enter_context(
open(path_join(dump_dir, 'model.zip'), 'rb'))
proto = stack.enter_context(
open(path_join(dump_dir, 'model.proto')))
files_Clio = {
'model': ('model.zip', model, 'application/zip'),
'metadata':
('metadata.json', meta_clio, 'application/json'),
'schema': ('model.proto', proto, 'text/plain')
}
_post_model(files_Clio, push_api, auth_api, tries + 1,
max_tries, extra_headers, options)
else:
raise AcumosError("Model push failed: {}".format(
_ServerResponse(resp.status_code, resp.reason, resp.text)))
def test_session_push_sklearn():
'''Tests basic model pushing functionality with sklearn'''
clear_jwt()
with _patch_auth():
with MockServer() as server:
iris = load_iris()
X = iris.data
y = iris.target
clf = RandomForestClassifier(random_state=0)
clf.fit(X, y)
columns = [
'sepallength', 'sepalwidth', 'petallength', 'petalwidth'
]
X_df = pd.DataFrame(X, columns=columns)
DataFrame = create_dataframe('DataFrame', X_df)
Predictions = create_namedtuple('Predictions',
[('predictions', List[int])])
def predict(df: DataFrame) -> Predictions:
'''Predicts the class of iris'''
X = np.column_stack(df)
yhat = clf.predict(X)
preds = Predictions(predictions=yhat)
return preds
model = Model(predict=predict)
model_url, auth_url, _, _ = server.config
s = AcumosSession(model_url, auth_url)
s.push(model, name='sklearn_iris_push')
def _patch_auth(clear_token=True):
'''Convenience CM to patch interactive prompts used for authentication'''
if clear_token:
clear_jwt()
with mock.patch('acumos.auth.gettoken', lambda x: _FAKE_TOKEN):
yield
def test_auth_envvar():
'''Tests that environment variables can be used in place of interactive input'''
clear_jwt()
with _patch_environ(**{
_USERNAME_VAR: _FAKE_USERNAME,
_PASSWORD_VAR: _FAKE_PASSWORD
}):
_push_dummy_model()
clear_jwt()
with _patch_environ(**{_TOKEN_VAR: _FAKE_TOKEN}):
_push_dummy_model()
def test_extra_header():
'''Tests that extra headers are correctly sent to the onboarding server'''
clear_jwt()
# in the mock onboarding server, this extra test header acts as another auth header
with _patch_environ(**{_TOKEN_VAR: 'wrongtoken'}):
extra_headers = {'X-Test-Header': _FAKE_TOKEN}
_push_dummy_model(extra_headers)
clear_jwt()
with pytest.raises(AcumosError):
with _patch_environ(**{_TOKEN_VAR: 'wrongtoken'}):
extra_headers = {'X-Test-Header': 'wrongtoken'}
_push_dummy_model(extra_headers)
def test_session_push():
'''Tests various session push scenarios'''
# allow users to push using username and password env vars
clear_jwt()
with _patch_environ(**{
_USERNAME_VAR: _FAKE_USERNAME,
_PASSWORD_VAR: _FAKE_PASSWORD
}):
_push_dummy_model(use_auth_url=True)
# verify auth url is required for username and password auth
clear_jwt()
with _patch_environ(**{
_USERNAME_VAR: _FAKE_USERNAME,
_PASSWORD_VAR: _FAKE_PASSWORD
}):
with pytest.raises(AcumosError):
_push_dummy_model(use_auth_url=False)
# allow users to push using a token env var
clear_jwt()
with _patch_environ(**{_TOKEN_VAR: _FAKE_TOKEN}):
_push_dummy_model()
# allow users to push by providing a token via interactive prompt
clear_jwt()
with _patch_auth():
_push_dummy_model()
# verify we can recover from a stale token
_set_jwt('invalidtoken')
with _patch_auth():
_push_dummy_model()
def _post_model(files, push_api, auth_api, tries, max_tries, extra_headers):
'''Attempts to post the model to Acumos'''
headers = {'Authorization': get_jwt(auth_api)}
if extra_headers is not None:
headers.update(extra_headers)
r = requests.post(push_api, files=files, headers=headers)
if r.status_code == 201:
logger.info("Model pushed successfully to {}".format(push_api))
else:
clear_jwt()
if r.status_code == 401 and tries != max_tries:
logger.warning('Model push failed due to an authorization failure. Clearing credentials and trying again')
_post_model(files, push_api, auth_api, tries + 1, max_tries, extra_headers)
else:
raise AcumosError("Model push failed: {}".format(_ServerResponse(r.status_code, r.reason, r.text)))
def test_session_push_keras():
'''Tests basic model pushing functionality with keras'''
clear_jwt()
with _patch_auth():
with MockServer() as server:
iris = load_iris()
X = iris.data
y = pd.get_dummies(iris.target).values
clf = Sequential()
clf.add(Dense(3, input_dim=4, activation='relu'))
clf.add(Dense(3, activation='softmax'))
clf.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
clf.fit(X, y)
columns = [
'sepallength', 'sepalwidth', 'petallength', 'petalwidth'
]
X_df = pd.DataFrame(X, columns=columns)
DataFrame = create_dataframe('DataFrame', X_df)
Predictions = create_namedtuple('Predictions',
[('predictions', List[int])])
def predict(df: DataFrame) -> Predictions:
'''Predicts the class of iris'''
X = np.column_stack(df)
yhat = clf.predict(X)
preds = Predictions(predictions=yhat)
return preds
model = Model(predict=predict)
model_url, auth_url, _, _ = server.config
s = AcumosSession(model_url, auth_url)
s.push(model, name='keras_iris_push')
