def test_create_update_and_delete_service():
override_token_funcs()
test_config = get_test_config()
deployment_client = DeploymentClient(
test_config['test_subscription_id'],
test_config['test_resource_group'],
test_config['test_model_management_account'])
cleanup_old_test_services(deployment_client)
id = uuid.uuid4().hex[:5]
model_name = "int-test-model-" + id
service_name = "int-test-service-" + id
service_def_path = "/tmp/model"
in_images = tf.placeholder(tf.string)
image_tensors = preprocess_array(in_images)
model = LocalQuantizedResNet50(os.path.expanduser("~/models"))
model.import_graph_def(include_featurizer=False)
service_def = ServiceDefinition()
service_def.pipeline.append(
TensorflowStage(tf.Session(), in_images, image_tensors))
service_def.pipeline.append(BrainWaveStage(model))
service_def.pipeline.append(
TensorflowStage(tf.Session(), model.classifier_input,
model.classifier_output))
service_def.save(service_def_path)
# create service
first_model_id = deployment_client.register_model(model_name,
service_def_path)
service = deployment_client.create_service(service_name, first_model_id)
service_list = deployment_client.list_services()
assert any(x.name == service_name for x in service_list)
prediction_client = PredictionClient(service.ipAddress, service.port)
top_result = sorted(enumerate(
prediction_client.score_image("/tmp/share1/shark.jpg")),
key=lambda x: x[1],
reverse=True)[:1]
# 'tiger shark' is class 3
assert top_result[0][0] == 3
# update service, remove classifier
service_def = ServiceDefinition()
service_def.pipeline.append(
TensorflowStage(tf.Session(), in_images, image_tensors))
service_def.pipeline.append(BrainWaveStage(model))
service_def.save(service_def_path)
second_model_id = deployment_client.register_model(model_name,
service_def_path)
deployment_client.update_service(service.id, second_model_id)
result = prediction_client.score_image("/tmp/share1/shark.jpg")
assert all([x == y for x, y in zip(np.array(result).shape, [1, 1, 2048])])
# wait for timeout of Azure LB
time.sleep(4 * 60 + 10)
result = prediction_client.score_image("/tmp/share1/shark.jpg")
assert all([x == y for x, y in zip(np.array(result).shape, [1, 1, 2048])])
deployment_client.delete_service(service.id)
deployment_client.delete_model(first_model_id)
deployment_client.delete_model(second_model_id)
def test_create_client_raises_if_port_is_none():
with pytest.raises(ValueError):
PredictionClient("localhost", None)
def test_create_client_raises_if_host_is_none():
with pytest.raises(ValueError):
PredictionClient(None, 50051)
def test_create_client_with_auth():
client = PredictionClient("localhost", 50051, True, "key1")
assert client is not None
def test_create_client():
client = PredictionClient("localhost", 50051)
assert client is not None
import requests
from amlrealtimeai.client import PredictionClient
import argparse
parse = argparse.ArgumentParser(description='AML inferencing client')
parse.add_argument('IP', type=str, help='IP of the FPGA runtime node')
parse.add_argument('path',
type=str,
help='Path to image that need to inferenced')
arg = parse.parse_args()
IP = arg.IP
path = arg.path
client = PredictionClient(IP, 80, False, '')
results = client.score_image(path)
#print(results)
trained_ds = requests.get(
"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt"
).text.splitlines()
#for line in trained_ds:
# print(line)
# map results [class_id] => [confidence]
results = enumerate(results)
#for line in results:
# print(line)
# sort results by confidence
sorted_results = sorted(results, key=lambda x: x[1], reverse=True)
#for line in sorted_results:
# print(line)