Question

我正在尝试调用TensorFlow模型，该模型使用TensorFlow Serving和gRPC部署在具有Python 2.7 API的云铸造服务器上。该模型期望输入200暗淡的向量，此刻我已对其进行了硬编码。连接变量存储在virtualenv中，并检查了两次。

代码：

import os
from grpc.beta import implementations
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from grpc._cython import cygrpc

MODEL_NAME = str(os.getenv('MODEL_NAME', ''))
MODEL_SERVER_HOST = str(os.getenv('MODEL_SERVER_HOST', ''))
MODEL_SERVER_PORT = int(os.getenv('MODEL_SERVER_PORT', ''))
ROOT_CERT = str(os.getenv('ROOT_CERT', '')).replace('\\n', '\n')

def metadata_transformer(metadata):
    additions = []
    token = 'Bearer <my access token>'
    additions.append(('authorization', token))
    return tuple(metadata) + tuple(additions)

credentials = implementations.ssl_channel_credentials(root_certificates=ROOT_CERT)
channel = implementations.secure_channel(MODEL_SERVER_HOST, MODEL_SERVER_PORT, credentials)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel, metadata_transformer=metadata_transformer)

import numpy as np
data = np.matrix([0.06222425773739815, 0.08211926370859146, -0.060986146330833435, 0.13920938968658447, 0.10515272617340088, -0.06220443174242973, -0.05927170068025589, -0.054189786314964294, -0.0986655130982399, 0.013334010727703571, -0.05667420104146004, 0.059366412460803986, -0.03483295068144798, -0.05382293462753296, 0.02721281163394451, -0.1428503543138504, 0.029297124594449997, 0.07006879895925522, 0.06501731276512146, 0.028620243072509766, 0.07128454744815826, 0.029960375279188156, 0.0710490494966507, -0.04619687795639038, -0.03106304071843624, -0.04266272485256195, 0.004348727408796549, 0.03099834732711315, 0.09248803555965424, -0.036939311772584915, 0.00017547572497278452, 0.03521900251507759, 0.10932505130767822, -0.019729139283299446, 0.12315405160188675, 0.10092845559120178, -0.12633951008319855, -0.022320391610264778, 0.0870826318860054, -0.06696301698684692, -0.016253307461738586, -0.0413096621632576, -0.040929097682237625, 0.09338817000389099, -0.08800378441810608, 0.015543102286756039, 0.018787918612360954, 0.07351260632276535, 0.038140904158353806, 0.019255049526691437, 0.0875692293047905, -0.07542476058006287, -0.04116508364677429, 0.04507743567228317, -0.06986603885889053, -0.24688798189163208, -0.035459864884614944, 0.06200174242258072, -0.06932217627763748, 0.06320516765117645, -0.023999478667974472, -0.04712359234690666, 0.03672196343541145, -0.02999514900147915, 0.04105519875884056, 0.08891177922487259, 0.15175248682498932, -0.0021488466300070286, 0.04398706927895546, -0.04429445043206215, 0.04708605632185936, 0.043234940618276596, -0.043555982410907745, 0.017381751909852028, 0.048889972269535065, -0.016929129138588905, 0.01731136068701744, -0.04694319888949394, 0.20381565392017365, 0.009074307978153229, 0.004490611143410206, -0.08525945991277695, -0.03385556861758232, 0.017475442960858345, -0.040392760187387466, 0.14970248937606812, 0.042721331119537354, -0.1257765144109726, -0.07097769528627396, -0.10943038016557693, 0.015442096628248692, -0.06519876420497894, -0.07588690519332886, -0.07620779424905777, 0.04572996124625206, -0.058589719235897064, -0.04492143541574478, -0.01922304928302765, -0.008066931739449501, 0.04317406192421913, 0.020763304084539413, -0.025430725887417793, 0.04271349683403969, 0.07393930852413177, 0.0020402593072503805, 0.0783640518784523, 0.047386448830366135, 0.010610940866172314, 0.022059153765439987, 0.034980181604623795, -0.006882485933601856, -0.08911270648241043, -0.001243607490323484, -0.06307544559240341, -0.01352659147232771, -0.24622271955013275, 0.07930449396371841, 0.03659113869071007, -0.05077377334237099, 0.08726480603218079, -0.09274136275053024, -0.05766649544239044, -0.12269984930753708, 0.056026071310043335, -0.0048304214142262936, -0.05568183213472366, -0.08890420943498611, -0.02911136858165264, -0.0944124087691307, 0.0011820291401818395, -0.08908636122941971, -0.008728212676942348, -0.014545259065926075, -0.008866528049111366, 0.02728298306465149, -0.020994992926716805, 0.031155599281191826, 0.036098793148994446, 0.06911332905292511, -0.06691643595695496, -0.00014896543871145695, -0.007080242037773132, 0.0031992685981094837, 0.043563224375247955, 0.02550852671265602, -0.015397937037050724, 0.06041031703352928, -0.08981014788150787, -0.10881254076957703, 0.03226703032851219, -0.02039985917508602, -0.05354547128081322, -0.026514282450079918, 0.09616094827651978, -0.04160488396883011, -0.06793050467967987, -0.17060619592666626, -0.08044841140508652, 0.042605575174093246, 0.08186516910791397, 0.026051705703139305, 0.1254323273897171, 0.09807661175727844, 0.04692094400525093, 0.05536479875445366, 0.004592049401253462, 0.01953544095158577, -0.02827763929963112, 0.11051501333713531, -0.05077047273516655, -0.09987067431211472, 0.025186538696289062, -0.24119670689105988, -0.054666098207235336, 0.03561021387577057, -0.006030901800841093, 0.14740994572639465, 0.09515859931707382, 0.0628485381603241, 0.020558597519993782, -0.04458167776465416, -0.04740617796778679, 0.024550801143050194, -0.09533495455980301, 0.057229768484830856, -0.08855120837688446, 0.027864644303917885, -0.07248448580503464, 0.0647491067647934, 0.09660986065864563, 0.038834456354379654, -0.030274877324700356, -0.024261653423309326, 0.05457066744565964, -0.00860705878585577, 0.04901411384344101, 0.017157232388854027, -0.02722001262009144, 0.012187148444354534, 0.05596058815717697])

request = predict_pb2.PredictRequest()
request.model_spec.name = MODEL_NAME
request.model_spec.signature_name = 'ticketCatFeature2'
request.inputs['input'].CopyFrom(
    tf.contrib.util.make_tensor_proto(data, shape=[200]))

print stub.Classify(request, 10)

运行应用程序时出现以下错误消息：

回溯（最近通话最近）：在第36行的文件“ app.py”中打印存根分类（请求，10）在第 call 行中的第309行中的文件“ /home/vagrant/Desktop/Masterarbeit/appDir/venv/local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py” self._request_serializer，self._response_deserializer）文件“ /home/vagrant/Desktop/Masterarbeit/appDir/venv/local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py”，第195行，位于_blocking_unary_unary 引发_abortion_error（rpc_error_call） grpc.framework.interfaces.face.face.AbortionError：AbortionError（code = StatusCode.INTERNAL，details =“未读取全部消息”）

grpc调试日志：https://ufile.io/owk76

TensorFlow Serving + gRPC“未读取全部消息”

0 个答案: