def consume_file_path(
number: int,
number_1a_path: str,
number_1b_file: str,
number_1c_file_path: str,
number_1d_path_file: str,
number_2a_path: InputPath(str),
number_2b_file: InputPath(str),
number_2c_file_path: InputPath(str),
number_2d_path_file: InputPath(str),
number_3a_path: InputTextFile(str),
number_3b_file: InputTextFile(str),
number_3c_file_path: InputTextFile(str),
number_3d_path_file: InputTextFile(str),
number_4a_path: InputBinaryFile(str),
number_4b_file: InputBinaryFile(str),
number_4c_file_path: InputBinaryFile(str),
number_4d_path_file: InputBinaryFile(str),
output_number_2a_path: OutputPath(str),
output_number_2b_file: OutputPath(str),
output_number_2c_file_path: OutputPath(str),
output_number_2d_path_file: OutputPath(str),
output_number_3a_path: OutputTextFile(str),
output_number_3b_file: OutputTextFile(str),
output_number_3c_file_path: OutputTextFile(str),
output_number_3d_path_file: OutputTextFile(str),
output_number_4a_path: OutputBinaryFile(str),
output_number_4b_file: OutputBinaryFile(str),
output_number_4c_file_path: OutputBinaryFile(str),
output_number_4d_path_file: OutputBinaryFile(str),
):
pass
def write_to_file_path(
file_input1_path: InputPath(str),
file_input2_file: InputTextFile(str),
file_output1_path: OutputPath(str),
file_output2_file: OutputTextFile(str),
value_input1: str = 'foo',
value_input2: str = 'foo',
) -> NamedTuple('Outputs', [
('return_output1', str),
('return_output2', str),
]):
with open(file_input1_path, 'r') as file_input1_file:
with open(file_output1_path, 'w') as file_output1_file:
file_output1_file.write(file_input1_file.read())
file_output2_file.write(file_input2_file.read())
return (value_input1, value_input2)
def write_to_file_path(number_file: OutputTextFile(int)):
number_file.write(str(42))
def test_task(
model_file: InputBinaryFile(str),
examples_file: InputBinaryFile(str),
confusion_matrix: OutputTextFile(str),
results: OutputTextFile(str),
):
"""Connects to served model and tests example MNIST images."""
import time
import json
import numpy as np
import requests
from tensorflow.python.keras.backend import get_session
from tensorflow.python.keras.saving import load_model
from tensorflow.python.saved_model.simple_save import simple_save
with get_session() as sess:
model = load_model(model_file)
simple_save(
sess,
'/output/mnist/1/',
inputs={'input_image': model.input},
outputs={t.name: t
for t in model.outputs},
)
model_url = 'http://localhost:9001/v1/models/mnist'
for _ in range(60):
try:
requests.get(f'{model_url}/versions/1').raise_for_status()
break
except requests.RequestException:
time.sleep(5)
else:
raise Exception("Waited too long for sidecar to come up!")
response = requests.get(f'{model_url}/metadata')
response.raise_for_status()
assert response.json() == {
'model_spec': {
'name': 'mnist',
'signature_name': '',
'version': '1'
},
'metadata': {
'signature_def': {
'signature_def': {
'serving_default': {
'inputs': {
'input_image': {
'dtype': 'DT_FLOAT',
'tensor_shape': {
'dim': [
{
'size': '-1',
'name': ''
},
{
'size': '28',
'name': ''
},
{
'size': '28',
'name': ''
},
{
'size': '1',
'name': ''
},
],
'unknown_rank':
False,
},
'name': 'conv2d_input:0',
}
},
'outputs': {
'dense_1/Softmax:0': {
'dtype': 'DT_FLOAT',
'tensor_shape': {
'dim': [{
'size': '-1',
'name': ''
}, {
'size': '10',
'name': ''
}],
'unknown_rank':
False,
},
'name': 'dense_1/Softmax:0',
}
},
'method_name': 'tensorflow/serving/predict',
}
}
}
},
}
examples = np.load(examples_file)
assert examples['val_x'].shape == (100, 28, 28, 1)
assert examples['val_y'].shape == (100, 10)
response = requests.post(f'{model_url}:predict',
json={'instances': examples['val_x'].tolist()})
response.raise_for_status()
predicted = np.argmax(response.json()['predictions'], axis=1).tolist()
actual = np.argmax(examples['val_y'], axis=1).tolist()
zipped = list(zip(predicted, actual))
accuracy = sum(1 for (p, a) in zipped if p == a) / len(predicted)
print(f"Accuracy: {accuracy:0.2f}")
