/train-tfrecords

Training pipeline using TFRecord files

Primary LanguagePython

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.