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Bert as service

(简体中文|English)

In the example, a BERT model is used for semantic understanding prediction, and the text is represented as a vector, which can be used for further analysis and prediction. If your python version is 3.X, replace the 'pip' field in the following command with 'pip3',replace 'python' with 'python3'.

Getting Model

method 1: This example use model BERT Chinese Model from Paddlehub.

Install paddlehub first

pip3 install paddlehub

run

python3 prepare_model.py 128

PaddleHub only support Python 3.5+

the 128 in the command above means max_seq_len in BERT model, which is the length of sample after preprocessing. the config file and model file for server side are saved in the folder bert_seq128_model. the config file generated for client side is saved in the folder bert_seq128_client.

method 2: You can also download the above model from BOS(max_seq_len=128). After decompression, the config file and model file for server side are stored in the bert_chinese_L-12_H-768_A-12_model folder, and the config file generated for client side is stored in the bert_chinese_L-12_H-768_A-12_client folder:

wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticModel/bert_chinese_L-12_H-768_A-12.tar.gz
tar -xzf bert_chinese_L-12_H-768_A-12.tar.gz
mv bert_chinese_L-12_H-768_A-12_model bert_seq128_model
mv bert_chinese_L-12_H-768_A-12_client bert_seq128_client

if your model is bert_chinese_L-12_H-768_A-12_model, replace the 'bert_seq128_model' field in the following command with 'bert_chinese_L-12_H-768_A-12_model',replace 'bert_seq128_client' with 'bert_chinese_L-12_H-768_A-12_client'.

Getting Dict and Sample Dataset

sh get_data.sh

this script will download Chinese Dictionary File vocab.txt and Chinese Sample Data data-c.txt

Inference Service(Support BRPC-Client、GRPC-Client、Http-Client)

start cpu inference service,Run

python3 -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292  #cpu inference service

Or,start gpu inference service,Run

python3 -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0

BRPC-Client Inference

before prediction we should install paddle_serving_app. This module provides data preprocessing for BERT model.

pip3 install paddle_serving_app

Run

head data-c.txt | python3 bert_client.py --model bert_seq128_client/serving_client_conf.prototxt

the client reads data from data-c.txt and send prediction request, the prediction is given by word vector. (Due to massive data in the word vector, we do not print it).

GRPC-Client/HTTP-Client

Run

head data-c.txt | python3 bert_httpclient.py --model bert_seq128_client/serving_client_conf.prototxt

Benchmark

bash benchmark.sh bert_seq128_model bert_seq128_client

The output log file of benchmark named profile_log_bert_seq128_model