Я сохранил модель keras .h5 в .pb, используя SavedModelBuilder. После того, как я использую образ докера тензорного потока / обслуживания: 1.14.0 разверните мою модель, когда я запустил процесс прогнозирования, я получил «request.exceptions.HTTPError: 501 Ошибка сервера: Не реализовано для URL: http://localhost:8501/v1/models/genre:predict"
Модель строительного кода выглядит следующим образом:
from keras import backend as K
import tensorflow as tf
from keras.models import load_model
model=load_model('/home/li/model.h5')
model_signature =
tf.saved_model.signature_def_utils.predict_signature_def(
inputs={'input': model.input}, outputs={'output': model.output})
#export_path = os.path.join(model_path,model_version)
export_path = "/home/li/genre/1"
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
sess=K.get_session(),
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict':
model_signature,
'serving_default':
model_signature
})
builder.save()
Тогда я получил .PB модель: 
Когда я запускаю saved_model_cli show --dir /home/li/genre/1 --all, Сохранено. Информация о модели pd выглядит следующим образом:
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['predict']:
The given SavedModel SignatureDef contains the following input(s):
inputs['input'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 1, 128, 1292)
name: conv2d_1_input_2:0
The given SavedModel SignatureDef contains the following output(s):
outputs['output'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 19)
name: dense_2_2/Softmax:0
Method name is: tensorflow/serving/predict
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['input'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 1, 128, 1292)
name: conv2d_1_input_2:0
The given SavedModel SignatureDef contains the following output(s):
outputs['output'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 19)
name: dense_2_2/Softmax:0
Method name is: tensorflow/serving/predict
Команда, которую я использую для развертывания в тензор потока / обслуживания образа Docker,
docker run -p 8501:8501 --name tfserving_genre --mount type=bind,source=/home/li/genre,target=/models/genre -e MODEL_NAME=genre -t tensorflow/serving:1.14.0 &
При открытии http://localhost:8501/v1/models/genre в браузере, Я получил сообщение
{
"model_version_status": [
{
"version": "1",
"state": "AVAILABLE",
"status": {
"error_code": "OK",
"error_message": ""
}
}
]
}
Код предсказания клиента следующим образом:
import requests
import numpy as np
import os
import sys
from audio_to_spectrum_v2 import split_song_to_frames
# Define a Base client class for Tensorflow Serving
class TFServingClient:
"""
This is a base class that implements a Tensorflow Serving client
"""
TF_SERVING_URL_FORMAT = '{protocol}://{hostname}: {port}/v1/models/{endpoint}:predict'
def __init__(self, hostname, port, endpoint, protocol="http"):
self.protocol = protocol
self.hostname = hostname
self.port = port
self.endpoint = endpoint
def _query_service(self, req_json):
"""
:param req_json: dict (as define in https://cloud.google.com/ml-engine/docs/v1/predict-request)
:return: dict
"""
server_url = self.TF_SERVING_URL_FORMAT.format(protocol=self.protocol,
hostname=self.hostname,
port=self.port,
endpoint=self.endpoint)
response = requests.post(server_url, json=req_json)
response.raise_for_status()
print(response.json())
return np.array(response.json()['output'])
# Define a specific client for our inception_v3 model
class GenreClient(TFServingClient):
# INPUT_NAME is the config value we used when saving the model (the only value in the `input_names` list)
INPUT_NAME = "input"
def load_song(self, song_path):
"""Load a song from path,slices to pieces, and extract features, returned as np.array format"""
song_pieces = split_song_to_frames(song_path,False,30)
return song_pieces
def predict(self, song_path):
song_pieces = self.load_song(song_path)
# Create a request json dict
req_json = {
"instances": song_pieces.tolist()
}
print(req_json)
return self._query_service(req_json)
def main():
song_path=sys.argv[1]
print("file name:{}".format(os.path.split(song_path)[-1]))
hostname = "localhost"
port = "8501"
endpoint="genre"
client = GenreClient(hostname=hostname, port=port, endpoint=endpoint)
prediction = client.predict(song_path)
print(prediction)
if __name__=='__main__':
main()
После запуска кода предсказания я получил информацию об ошибке следующим образом:
Traceback (most recent call last):
File "client_predict.py", line 90, in <module>
main()
File "client_predict.py", line 81, in main
prediction = client.predict(song_path)
File "client_predict.py", line 69, in predict
return self._query_service(req_json)
File "client_predict.py", line 40, in _query_service
response.raise_for_status()
File "/home/li/anaconda3/lib/python3.7/site-packages/requests/models.py", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 501 Server Error: Not Implemented for url: http://localhost:8501/v1/models/genre:predict
Интересно, в чем причина этой проблемы развертывания и как ее решить, спасибо за все.