How to turn a typical pytorch script into a scalable d6tflow DAG for faster research & development
5 Step Guide to Scalable Deep Learning Pipelines with pytorch and d6tflow
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.
The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the pytorch code into a scalable deep learning pipeline.
Read the blog post on Towards Data Science and the code is available on Github.
