Python Server.get_client Examples

Example #1

File: test_python_serving.py Project: wangfeng-skymind/konduit-serving

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()

Example #2

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()

Example #3

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()

Example #4

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()