Training a simple recommendation engine on GCP with custom environment containers

Complete guide using Tensorflow, Airflow scheduler and Docker

Google AI Platform allows advanced model training using various environments. So it is really easy to train your model with just one command like so:

gcloud ai-platform jobs submit training ${JOB_NAME} \
    --region $REGION \
    --scale-tier=CUSTOM \
    --job-dir ${BUCKET}/jobs/${JOB_NAME} \
    --module-name trainer.task \
    --package-path trainer \
    --config trainer/config/config_train.json \
    --master-machine-type complex_model_m_gpu \
    --runtime-version 1.15

This guide explains how to build a simple recommendation engine using WALS algorhitm:

• How to run model trainer locally

• Write a Dockerfile and Create a custom environment container

• Push it to Google Cloud Platform and create a custom container Google AItraining job

• Schedule model training with AirFlow

READ IT ON MEDIUM:

https://towardsdatascience.com/training-your-ml-model-using-google-ai-platform-and-custom-environment-containers-1531888cac3d