Tensorflow Serving

from kashgari.tasks.classification import BiGRU_Model
from kashgari.corpus import SMP2018ECDTCorpus
from kashgari import utils
train_x, train_y = SMP2018ECDTCorpus.load_data()
model = BiGRU_Model()
model.fit(train_x, train_y)
# Save model
utils.convert_to_saved_model(model, 
                             model_path='saved_model/bgru', 
                             version=1)

Then run tensorflow-serving.

docker run -t --rm -p 8501:8501 -v "path_to/saved_model:/models/" -e MODEL_NAME=bgru tensorflow/serving

Load processor from model, then predict with serving.

import requests
from kashgari import utils
import numpy as np
x = ['Hello', 'World']
# Pre-processor data
processor = utils.load_processor(model_path='saved_model/bgru/1')
tensor = processor.process_x_dataset([x])
# array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)
# if you using BERT, you need to reformat tensor first
# ------ Only for BERT Embedding Start --------
tensor = [{
   "Input-Token:0": i.tolist(),
   "Input-Segment:0": np.zeros(i.shape).tolist()
} for i in tensor]
# ------ Only for BERT Embedding End ----------
# predict
r = requests.post("http://localhost:8501/v1/models/bgru:predict", json={"instances": tensor.tolist()})
preds = r.json()['predictions']
# Convert result back to labels
labels = processor.reverse_numerize_label_sequences(np.array(preds).argmax(-1))
# labels = ['video']