def test_json_minimal_loading():
file_path = "yaml/konduit_minimal.json"
try:
server = server_from_file(file_path, use_yaml=False, start_server=True)
client = client_from_file(file_path, use_yaml=False)
finally:
server.stop()
del server, client
def test_yaml_server_python_prediction():
try:
konduit_yaml_path = "yaml/konduit_tf_inference.yaml"
img = np.load("../data/input_layer.npy")
server = server_from_file(konduit_yaml_path, start_server=True)
client = client_from_file(konduit_yaml_path)
predicted = client.predict(data_input={"input_layer": img})
result = dict(zip(np.arange(10), predicted[0].round(3)))
# {0: 0.0, 1: 0.0, 2: 0.001, 3: 0.001, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.998, 8: 0.0, 9: 0.0}
assert round(result.get(7) * 1000) == 998
server.stop()
finally:
server.stop()
def predict_numpy(config, numpy_data, input_names):
"""Get predictions for your pipeline from numpy data and input names"""
from konduit.load import client_from_file
numpy_files = numpy_data.split(",")
input_names = input_names.split(",")
assert len(numpy_files) == len(input_names)
client = client_from_file(file_path=config)
input_dict = {}
for i in range(len(numpy_files)):
input_dict[input_names[i]] = np.load(numpy_files[i])
print(client.predict(input_dict))
def test_model():
client = client_from_file("konduit.yml")
image = open("1902_airplane.png", "rb").read()
responses = []
start = time.time()
for i in range(10):
response = client.predict({"default": image})
print(response)
responses.append(response)
end = time.time()
print("%f seconds elapsed for %d requests (%d RPS)" % (end - start, len(responses), (10.0 / (end - start))))
def test_model():
client = client_from_file("konduit.yml")
testset = torchvision.datasets.CIFAR10(root='./model/data',
train=False,
download=True)
image, _ = testset[0]
responses = []
start = time.time()
for i in range(10):
byte_buf = io.BytesIO()
image.save(byte_buf, format='PNG')
byte_array = byte_buf.getvalue()
responses.append(client.predict({"default": byte_array}))
print(len(responses))
end = time.time()
print("%f seconds elapsed for 1000 requests (%d RPS)" %
(end - start, (10.0 / (end - start))))
# Copyright (c) 2020, codenamewei
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import time
import numpy as np
from konduit.client import Client
from konduit.load import client_from_file
current_milli_time = lambda: int(round(time.time() * 1000))
client = client_from_file("config.yaml")
prediction = client.predict({client.input_names[0]: np.array([5])})
print("Output: {}".format(prediction))
def test_yaml_minimal_loading():
file_path = "yaml/konduit_minimal.yaml"
server = server_from_file(file_path, use_yaml=True, start_server=True)
client = client_from_file(file_path, use_yaml=True)
del server, client
def test_yaml_client_loading():
file_path = "yaml/konduit.yaml"
client = client_from_file(file_path)
