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 test_rcp_functions(self):
c = Client()
with MockServer(c.address.port) as s:
self.assertTrue(c.call('foo'))
with self.assertRaises(RPCError):
c.call('not_existing')
c.add_functions()
self.assertTrue(c.call(Client.FUNCNAME_PING))
self.assertTrue(c.call('sum', 1, 2) == c.sum(1, 2))
def test_reconnect_to_new(self):
c = Client(server_port=None, reconnect_limit=100)
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
# calling rpc function before the server is launched. The call should just hang in there waiting for the
# server to come on line (up until set limit of reconnect attempts)
future = executor.submit(c.call, 'sum', 1, 2)
with MockServer(c.address.port) as s:
return_value = future.result()
self.assertEqual(return_value, 3)
def _push_dummy_model(extra_headers=None):
'''Generic dummy model push routine'''
def my_transform(x: int, y: int) -> int:
return x + y
model = Model(transform=my_transform)
with MockServer() as server:
model_url, auth_url, _, _ = server.config
s = AcumosSession(model_url, auth_url)
s.push(model, name='my-model', extra_headers=extra_headers)
def setUp(self):
self.lldb_debugger = lldb.SBDebugger.Create()
self.lldb_debugger.CreateTargetWithFileAndArch(
self.__class__.test_executable.executable_path,
lldb.LLDB_ARCH_DEFAULT)
self.server = MockServer()
file_manager = FileManager(self.server)
self.remote_object_manager = RemoteObjectManager()
self.debugger = DebuggerDomain(None,
file_manager,
self.remote_object_manager,
debugger=self.lldb_debugger,
socket=self.server)
def setUp(self):
self.lldb_debugger = lldb.SBDebugger.Create()
self.lldb_debugger.CreateTargetWithFileAndArch(
self.__class__.test_executable.executable_path,
lldb.LLDB_ARCH_DEFAULT)
self.server = MockServer()
file_manager = FileManager(self.server)
self.location_serializer = LocationSerializer(file_manager, '.')
self.remote_object_manager = RemoteObjectManager()
self.thread_manager = ThreadManager(
self.server,
self.location_serializer,
self.remote_object_manager)
def _push_dummy_model(extra_headers=None,
use_model_url=True,
use_auth_url=False,
options=None):
'''Generic dummy model push routine'''
def my_transform(x: int, y: int) -> int:
return x + y
model = Model(transform=my_transform)
with MockServer() as server:
_model_url, _auth_url, _, _ = server.config
model_url = _model_url if use_model_url else None
auth_url = _auth_url if use_auth_url else None
session = AcumosSession(model_url, auth_url)
session.push(model,
name='my-model',
extra_headers=extra_headers,
options=options)
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')
def setUp(self):
self.server = MockServer()
self.console = ConsoleDomain(socket=self.server, debugger=None)