def test_server_start():
port = random.randint(1000, 65535)
serving_config = ServingConfig(http_port=port)
python_config = PythonConfig(
python_code="first += 2",
python_inputs={"first": "NDARRAY"},
python_outputs={"first": "NDARRAY"},
)
step = PythonStep().step(python_config)
server = Server(steps=step, serving_config=serving_config)
server.start()
client = server.get_client()
data_input = {"default": np.load("../data/input-0.npy")}
assert is_port_in_use(port)
try:
predicted = client.predict(data_input)
print(predicted)
server.stop()
except Exception as e:
print(e)
server.stop()
def test_setup_and_run_start():
port = random.randint(1000, 65535)
serving_config = ServingConfig(http_port=port)
python_config = PythonConfig(
python_code=
"def setup(): pass\ndef run(input): {'output': np.array(input + 2)}",
python_inputs={"input": "NDARRAY"},
python_outputs={"output": "NDARRAY"},
setup_and_run=True,
)
step = PythonStep().step(python_config)
server = Server(steps=step, serving_config=serving_config)
server.start()
client = server.get_client()
data_input = {"default": np.asarray([42.0, 1.0])}
assert is_port_in_use(port)
try:
predicted = client.predict(data_input)
print(predicted)
server.stop()
except Exception as e:
print(e)
server.stop()
def test_build_tp(output_format):
schema = pydatavec.Schema()
schema.add_string_column("first")
tp = pydatavec.TransformProcess(schema)
tp.append_string("first", "two")
java_tp = tp.to_java()
tp_json = java_tp.toJson()
load_java_tp(tp_json)
_ = json.dumps(tp_json)
as_python_json = json.loads(tp_json)
transform_process = (TransformProcessStep().set_input(
column_names=["first"],
types=["String"
]).set_output(column_names=["first"],
types=["String"
]).transform_process(as_python_json))
port = random.randint(1000, 65535)
serving_config = ServingConfig(
http_port=port,
input_data_format="JSON",
output_data_format=output_format,
log_timings=True,
)
inference_config = InferenceConfiguration(serving_config=serving_config,
steps=[transform_process])
as_json = config_to_dict_with_type(inference_config)
inference_from_json(as_json)
server = Server(
inference_config=inference_config,
extra_start_args="-Xmx8g",
jar_path="konduit.jar",
)
server.start()
client = server.get_client()
assert is_port_in_use(port)
try:
predicted = client.predict({"first": "value"})
print(predicted)
server.stop()
except Exception as e:
print(e)
server.stop()
from konduit.server import Server
from konduit.utils import default_python_path
import os
import cv2
work_dir = os.path.abspath(".")
python_config = PythonConfig(
python_path=default_python_path(work_dir),
python_code_path=os.path.join(work_dir, "detect_image.py"),
python_inputs={"image": "NDARRAY"},
python_outputs={"num_boxes": "NDARRAY"},
)
server = Server(
serving_config=ServingConfig(http_port=1337), steps=PythonStep().step(python_config)
)
server.start()
# client = Client(port=1337)
client = server.get_client()
img_path = "./Ultra-Light-Fast-Generic-Face-Detector-1MB/imgs/1.jpg"
img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB).astype("int16")
try:
print(client.predict(img))
finally:
server.stop()
