def get_prediction(coords, path_to_img, host):
# account for papaya recieved image
coords = change_coordinate_system(coords, path_to_img)
# pre-process image so that it matches input of model
processed_image = pre_process_image(coords, path_to_img)
# specify where the client should look to make requests
client = ProdClient(host + ':9200', 'crohns', 1)
# query tensorflow seriving model for predictions and attention layer
prob_values, max_prob_indx, attentions = query_client(
processed_image, client)
# proccess the feature map to get the average and resize it
feature_maps_arr = process_feature_maps(attentions,
processed_image[0].shape)
# make the attention layer into a nifit file
make_feature_image(coords, path_to_img, feature_maps_arr)
# produce an output string to display on front-end
classes = {0: 'healthy', 1: 'abnormal (Crohn\'s)'}
predictions = classes[max_prob_indx]
output_str = f'{predictions} with probability {round(prob_values[0][max_prob_indx], 3)}'
return output_str
def main(user_text):
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s')
# In each file/module, do this to get the module name in the logs
logger = logging.getLogger(__name__)
# Make sure you have a model running on localhost:9000
host = 'localhost:8500'
model_name = 'bert'
model_version = 1
#-------------------------------------------------------------------#
#----------------INPUT STRING----------------------------
#user_text = 'i hate all twitter users'
request_id = np.zeros((128), dtype=int).tolist()
content = {
'user_text': user_text,
}
label_list = [0, 0, 0, 0, 0, 0]
VOCAB_FILE = 'vocab.txt'
tokenizer = tokenization.FullTokenizer(vocab_file=VOCAB_FILE,
do_lower_case=True)
processor = MultiLabelTextProcessor()
inputExample = processor.serving_create_example(
[request_id, content['user_text']], 'test')
feature = convert_single_example(0, inputExample, label_list, 128,
tokenizer)
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["is_real_example"] = create_int_feature(
[int(feature.is_real_example)])
if isinstance(feature.label_id, list):
label_ids = feature.label_id
else:
label_ids = [feature.label_id]
features["label_ids"] = create_int_feature(label_ids)
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
#----------------------------------------------------------------------------#
tf_example = tf_example.SerializeToString()
print('test')
client = ProdClient(host, model_name, model_version)
prediction = client.predict(tf_example, request_timeout=10)
logger.info('Prediction: {}'.format(prediction))
return prediction
def __init__(self, max_input_len: int, host: str, port: int,
model_name: str, model_version: int):
self.max_input_len = max_input_len
self.client = ProdClient('{host}:{port}'.format(host=host, port=port),
model_name, model_version)
_, self.char_to_id, self.id_to_char, self.vocabulary_size = get_vocabulary_and_dictionaries(
)
def test(request):
client = ProdClient('localhost:9000', 'simple', 2)
req_data = [{
'in_tensor_name': 'inputs',
'in_tensor_dtype': 'DT_INT32',
'data': 6
}]
value = client.predict(req_data)
print(value)
return render(request, 'tf_model_test/test.html', {'value': value})
#!/usr/bin/env python
#
# https://medium.com/epigramai/tensorflow-serving-101-pt-2-682eaf7469e7
#
# Example gRPC client of TFServing for hellworld model. Note the "predict_client"
# directory contains wrapper for tensorflow serving API, thus we don't have to
# depend on "tensorflow_serving.apis".
import argparse
import logging
from predict_client.prod_client import ProdClient
# Make logging work.
logging.basicConfig(level=logging.DEBUG, format='%(message)s')
# Parse arguments.
parser = argparse.ArgumentParser(description='Process arguments.')
parser.add_argument('--host', type=str, default='localhost:9000')
parser.add_argument('--model_name', type=str, default='simple')
parser.add_argument('--model_version', type=int, default=1)
args = parser.parse_args()
client = ProdClient(args.host, args.model_name, args.model_version)
# Note in_tensor_name ‘a’ is the same ‘a’ that we used in the signature
# definition in our model. The input tensor’s data type must also match
# the one of the placeholder a in our model.
req_data = [{'in_tensor_name': 'a', 'in_tensor_dtype': 'DT_INT32', 'data': 2}]
print(client.predict(req_data))
import utils
import numpy as np
images = utils.images
images = utils.images
labels = utils.labels
import numpy as np
from predict_client.prod_client import ProdClient
HOST = '0.0.0.0:8500'
MODEL_NAME = 'mnistpb'
MODEL_VERSION = 1
client = ProdClient(
HOST, MODEL_NAME,
MODEL_VERSION) #connect to the tf-serving running through docker
#parameters to request
req_data = [{
'in_tensor_name': 'inputs',
'in_tensor_dtype': 'DT_FLOAT',
'data': images[0:4]
}]
prediction = client.predict(req_data, request_timeout=10) #get the output
#axis 1 because of batch request
print(np.argmax(np.asarray(prediction['outputs']), axis=1))
img = np.expand_dims(img, axis=0)
print(img.shape)
return img
import numpy as np
from predict_client.prod_client import ProdClient
from flask import Flask
from flask import request
from flask import jsonify
HOST = 'localhost:9000'
MODEL_NAME = 'test'
MODEL_VERSION = 1
app = Flask(__name__)
client = ProdClient(HOST, MODEL_NAME, MODEL_VERSION)
def convert_data(raw_data):
return np.array(raw_data, dtype=np.float32)
def get_prediction_from_model(data):
req_data = [{'in_tensor_name': 'inputs', 'in_tensor_dtype': 'DT_FLOAT', 'data': data}]
prediction = client.predict(req_data, request_timeout=10)
return prediction
@app.route("/prediction", methods=['POST'])
def get_prediction():
##req_data = request.get_json()
import logging
from predict_client.prod_client import ProdClient
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s')
# In each file/module, do this to get the module name in the logs
logger = logging.getLogger(__name__)
# Make sure you have a model running on localhost:9000
host = 'localhost:9000'
model_name = 'simple'
model_version = 1
client = ProdClient(host, model_name, model_version)
req_data = [{'in_tensor_name': 'a', 'in_tensor_dtype': 'DT_INT32', 'data': 2}]
prediction = client.predict(req_data, request_timeout=10)
logger.info('Prediction: {}'.format(prediction))
import tensorflow as tf
import numpy as np
import pickle
import config as cfg
from predict_client.prod_client import ProdClient
from inference import simple_inference
#import Flask dependencies
from flask import Flask, request, render_template, send_from_directory
#Set root dir
APP_ROOT = os.path.dirname(os.path.abspath(__file__))
#Define client
client = ProdClient('localhost:9000', 'simple', 2)
#Load training set vectors
with open('hamming_train_vectors.pickle', 'rb') as f:
train_vectors = pickle.load(f)
#Load training set paths
with open('train_images_pickle.pickle', 'rb') as f:
train_images_paths = pickle.load(f)
#Define Flask app
app = Flask(__name__, static_url_path='/static')
#Define apps home page
@app.route("/") #www.image-search.com/
from predict_client.prod_client import ProdClient
# Used by Docker 1
client = ProdClient('localhost:9001', 'simple', 1)
req_data = [{
'in_tensor_name': 'inputs',
'in_tensor_dtype': 'DT_INT32',
'data': 4
}]
client.predict(req_data)
# Used by Docker 2
client = ProdClient('localhost:9000', 'simple_1', 2)
req_data = [{
'in_tensor_name': 'inputs',
'in_tensor_dtype': 'DT_INT32',
'data': 4
}]
client.predict(req_data)
# Used by Docker 3
client = ProdClient('localhost:9002', 'subtract_model', 1)
req_data = [{
'in_tensor_name': 'inputs',
'in_tensor_dtype': 'DT_INT32',
'data': 22
}]
client.predict(req_data)