def test_serving():
ws = _get_ws()
exp = skil.Experiment(ws)
save_model()
model = skil.Model('model.h5', experiment=exp)
dep = skil.Deployment(ws.skil)
service = model.deploy(dep)
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_test = x_test.reshape(10000, 784)
x_test = x_test.astype('float32')
x_test /= 255
service.predict_single(x_test[0])
service.predict(x_test[:10])
service.stop()
time.sleep(4)
with pytest.raises(Exception):
service.predict(x_test[:10])
service.start()
time.sleep(4)
service.predict(x_test[:10])
service.delete()
os.remove('model.h5')
def test_deployment_serde():
dep = skil.Deployment()
dep.save('dep.json')
recov = skil.Deployment.load('dep.json')
assert recov.get_config() == dep.get_config()
def test_service_serde():
dep = skil.Deployment()
model = skil.Model('keras_mnist.h5', name='bar')
service = model.deploy(dep)
service.save('service.json')
recov = skil.Service.load('service.json')
assert recov.get_config() == service.get_config()
def deploy(self,
deployment=None,
start_server=True,
scale=1,
input_names=None,
output_names=None,
verbose=True):
"""Deploys the model
# Arguments:
deployment: `Deployment` instance.
start_server: boolean. If `True`, the service is immedietely started.
scale: integer. Scale-out for deployment.
input_names: list of strings. Input variable names of the model.
output_names: list of strings. Output variable names of the model.
verbose: boolean. If `True`, api response will be printed.
# Returns:
`Service` instance.
"""
if not deployment:
deployment = skil.Deployment(skil=self.skil, name=self.name)
uris = [
"{}/model/{}/default".format(deployment.name, self.name),
"{}/model/{}/v1".format(deployment.name, self.name)
]
if not self.service:
deploy_model_request = skil_client.ImportModelRequest(
name=self.name,
scale=scale,
file_location=self.model_path,
model_type="model",
uri=uris,
input_names=input_names,
output_names=output_names)
# TODO: why ".response"? Doesn't make sense.
self.deployment = deployment.response
models = self.skil.api.models(self.deployment.id)
deployed_model = [m for m in models if m.name == self.name]
if deployed_model:
self.model_deployment = deployed_model[0]
else:
self.model_deployment = self.skil.api.deploy_model(
self.deployment.id, deploy_model_request)
if verbose:
self.skil.printer.pprint(self.model_deployment)
self.service = Service(self.skil, self, self.deployment,
self.model_deployment)
if start_server:
self.service.start()
return self.service
def test_service_creation():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
exp = skil.Experiment(work_space)
model = skil.Model('keras_mnist.h5', experiment=exp)
model.add_evaluation(0.95)
dep = skil.Deployment(sk)
model.deploy(dep)
work_space.delete()
exp.delete()
model.delete()
dep.delete()
def deploy(self,
deployment=None,
start_server=True,
scale=1,
input_names=None,
output_names=None,
verbose=True):
if not deployment:
deployment = skil.Deployment(skil=self.skil, name=self.name)
uris = [
"{}/model/{}/default".format(deployment.name, self.name),
"{}/model/{}/v1".format(deployment.name, self.name)
]
deploy_model_request = skil_client.ImportModelRequest(
name=self.name,
scale=scale,
file_location=self.model_path,
model_type="model",
uri=uris,
input_names=input_names,
output_names=output_names)
self.deployment = deployment.response
self.model_deployment = self.skil.api.deploy_model(
self.deployment.id, deploy_model_request)
if verbose:
self.skil.printer.pprint(self.model_deployment)
service = Service(self.skil, self.name, self.deployment,
self.model_deployment)
if start_server:
service.start()
return service
def test_deployment_by_id():
sk = _get_sk()
dep = skil.Deployment(sk, name='test_dep' + str(uuid.uuid1())[:6])
dep_id = dep.id
dep2 = skil.get_deployment_by_id(sk, dep_id)
assert dep.name == dep2.name
def test_transform_service_creation():
ws = _get_ws()
exp = skil.Experiment(ws)
model = skil.Transform('iris_tp.json', experiment=exp)
dep = skil.Deployment(ws.skil)
model.deploy(dep)
import skil
import numpy as np
skil_server = skil.Skil()
work_space = skil.WorkSpace(skil_server)
experiment = skil.Experiment(work_space)
transform = skil.Transform(transform='iris_tp.json', experiment=experiment)
model = skil.Model(model='iris_model.h5', experiment=experiment)
deployment = skil.Deployment(skil_server)
pipeline = skil.Pipeline(deployment, model, transform)
with open('iris.data', 'r') as f:
data = np.array(f.readlines())
print(pipeline.predict(data))
import skil
import cv2
skil_server = skil.Skil()
model = skil.Model('yolo_v2.pb', name='yolo-tf', model_id='yolo-3493723')
deployment = skil.Deployment(skil_server, 'yolo')
service = model.deploy(deployment, input_names=['input'], output_names=['output'], scale=2)
cap = cv2.VideoCapture(0)
while True:
_, image = cap.read()
detection = service.detect_objects(image)
image = skil.utils.yolo.annotate_image(image, detection)
cv2.imshow('yolo', image)
def test_deployment_creation():
sk = skil.Skil()
dep = skil.Deployment(sk)
dep.delete()
import skil
import cv2
model = skil.Model('yolo.pb', model_id='yolo_42', name='yolo')
service = model.deploy(skil.Deployment(),
input_names=['input'],
output_names=['output'])
image = cv2.imread("say_yolo_again.jpg")
detection = service.detect_objects(image)
image = skil.utils.yolo.annotate_image(image, detection)
cv2.imwrite('annotated.jpg', image)
def test_deployment_deletion():
dep = skil.Deployment()
dep.delete()
def test_service_creation():
ws = _get_ws()
exp = skil.Experiment(ws)
model = skil.Model('keras_mnist.h5', experiment=exp)
dep = skil.Deployment(ws.skil)
model.deploy(dep)
def test_deployment_creation():
sk = _get_sk()
dep = skil.Deployment(sk)
