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Popbots-Flask-API-Stressor

The purpose of this API endpoint is to route queries to a controller which converts a string sentence into a correctly tokenized BERT 32 tokens array and then query a BERT model served with Tensorflow Serving Docker container.

1. Deploy

1.1. Create a python3.7 venv (see online tutorials) with the name flaskapi ( you can configure a different name but you'll need to change the venv name in app.py at line 1 and configure the gitignore)

  • Activate the venv by doing:

    source "venv_name"/bin/activate

  • install all the required packages by navigating into the popbots folder and running

    pip3 install -r pip_requirements.txt

  • run via the bash command

    python app.py

Note: if you are using pm2 to manage the services you will need to specify the python interpreter

pm2 start app.py --interpreter ./flaskapi/bin/python3 --name flask_classifiers_api

2. Routes

The is only 2 routes in this API:

/classifier/stressor which given the param string 'stressor' will return the category0 to 8 sorted by probability from higher to lower

/classifier/covid which given the param string 'stressor' will return if it is covid related or not.

A typical query looks like http://{CLASSIFIER_IP_ADDRESS}/classifier/stressor?stressor="this upcoming deadline"

5. The docker container for Tensorflow serving

The command to execute the docker container is as follow:

Here are the instructions to install the tensorflow docker serving container https://www.tensorflow.org/tfx/serving/docker

sudo docker run -p 8501:8501 --name tfserving_bertstressor --mount type=bind,source=//home/ec2-user/models/,target=/models/bertstressor -e NVIDIA_VISIBLE_DEVICES=none -e MODEL_NAME=bertstressor -t tensorflow/serving:latest-gpu --enable_batching=false &

Make sure that this runs with :

curl -X POST   http://localhost:8501/v1/models/bertstressor:predict   -H 'Content-Type: application/json'   -H 'Postman-Token: bd0bb5d4-6409-4de5-9bc6-8ee45d69108a'   -H 'cache-control: no-cache'   -d '{"signature_name": "serving_default", "instances": [{"input_ids": [101, 2147, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}]}'

To execute the covid model :

sudo docker run -p 8502:8501 --name tfserving_bertcovidstressor --mount type=bind,source=//home/ec2-user/covid_model/,target=/models/bertcovidstressor -e NVIDIA_VISIBLE_DEVICES=none -e MODEL_NAME=bertcovidstressor -t tensorflow/serving:latest-gpu --enable_batching=false &

# might be 8502:8502 not sure

You can make sure that this runs with the following query

curl -X POST   http://localhost:8502/v1/models/bertcovidstressor:predict   -H 'Content-Type: application/json'   -H 'Postman-Token: bd0bb5d4-6409-4de5-9bc6-8ee45d69108a'   -H 'cache-control: no-cache'   -d '{"signature_name": "serving_default", "instances": [{"input_ids": [101, 2147, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}]}'

The both of these models files host a pb format Tensorflow estimator:

/home/ec2-user/models/ # for the stressor classifier

/home/ec2-user/covid_model/ # for the covid classifier

these are obtained via the Popbots-mturk-HITS/bert-pipeline jupyter notebook by the function export_model