Building deep learning models typically involves complex data pipelines as well as a lot of trial and error, tweaking model architecture and parameters whose performance needs to be compared. It is often difficult to keep track of all the experiments, leading at best to confusion and at worst wrong conclusions. In 4 reasons why your ML code is bad we explored how to organize ML code as DAG workflows to solve that problem. In this guide we will go through a practical case study on turning an existing pytorch script into a scalable deep learning pipeline with d6tflow. The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the code into a scalable deep learning pipeline.

Building deep learning models typically involves complex data pipelines as well as a lot of trial and error, tweaking model architecture and parameters whose performance needs to be compared. It is often difficult to keep track of all the experiments, leading at best to confusion and at worst wrong conclusions. In 4 reasons why your ML code is bad we explored how to organize ML code as DAG workflows to solve that problem. In this guide we will go through a practical case study on turning an existing pytorch script into a scalable deep learning pipeline with d6tflow. The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the code into a scalable deep learning pipeline.