Customized training using TFRecords data format

This project uses TensorFlow and some TFRecords data for training. The models and training pipeline for click-through rate prediction are customized.

Flask is used to create a service that can (re)train a model.

Dataset

The data comes from the text files of the standardized data format of iPinYou RTB dataset. Using this project, text files are transformed into TFRecords file.

Data information:

feature = click,          shape = (10000, 1),  Unique count = 2,   min = 0,   max = 1
feature = weekday,        shape = (10000, 1),  Unique count = 1,   min = 4,   max = 4
feature = region,         shape = (10000, 1),  Unique count = 35,  min = 0,   max = 395
feature = city,           shape = (10000, 1),  Unique count = 359, min = 0,   max = 399
feature = adexchange,     shape = (10000, 1),  Unique count = 3,   min = 1,   max = 3
feature = slotformat,     shape = (10000, 1),  Unique count = 2,   min = 0,   max = 1
feature = hour,           shape = (10000, 1),  Unique count = 1,   min = 0,   max = 0
feature = slotwidth,      shape = (10000, 1),  Unique count = 6,   min = 160, max = 1000
feature = slotheight,     shape = (10000, 1),  Unique count = 4,   min = 90,  max = 600
feature = slotvisibility, shape = (10000, 1),  Unique count = 4,   min = 0,   max = 255
feature = slotprice,      shape = (10000, 1),  Unique count = 46,  min = 0,   max = 280
feature = usertag,        shape = (10000, 39), Unique count = 45,  min = -1,  max = 16706

How to create the environment

1. Using pipenv

• To create or activate a virtual env: pipenv shell

  • Install all required packages:
    • install packages exactly as specified in Pipfile.lock: pipenv sync
    • install using the Pipfile, including the dev packages: pipenv install --dev

2. Using docker

• To build the image: make build
• To create the container: docker-compose up -d
  • Note that this will already trigger the Flask service app.
  • To get the exact IP address, try running docker-machine inspect default | grep IPAddress and then use this address instead of localhost.
• To get into the container: docker exec -it <container_name> bash
• To check the logs, especially when training the model: docker logs -f <container_name>
• To force stop the container: docker rm -f <container_name>

How to run the program

• Testing the code: make tests
• Training and Testing on data:
  • For easy configurations, edit: src/initial_configurations/default
  • Run training: make train
• Tuning the model:
  • Go inside the folder cd tune/.
  • Edit the configurations in search_space.json and tune_config.yml.
  • Run the tuner: nnictl create --config tune_config.yml --port <PORT_NUMBER>
• How to use the Flask service:
  • To check if it is working, GET http://localhost:7777/, should result to:

  Hello World! <3,Flask
  

  • To (re)train a model, POST on http://localhost:7777/train/<start_date>/<training_period>.
  • To check the performance metrics of the trained models, GET http://localhost:7777/monitor/get_result_csv.