Machine learning with the maximum of free GPU currently available plus the ability to keep a neat log of your data science experiments. Interested? My article shows a deep dive solution.
Quick summary
Colab, MLflow and papermill are individually great. Together they form a dream team.
Colab is great for running notebooks, MLflow keeps records of your results and papermill can parametrise a notebook, run it and save a copy.
All three are backed by top tier American companies, Colab by Google, MLflow by Databricks and papermill by Netflix.
Outline
Background and Motivation
Let me ask a few rhetorical questions (and give you my answers):
What’s your most comfortable way of running machine learning notebooks for experiments: Colab.
What’s your easiest way of running machine learning experiments where you need a decent size GPU: Colab.
What’s an elegant way of keeping track of ML experiments: MLfLow.
What’s a nice way of turning Jupyter-style ML notebooks into reproducible and loggable code : Papermill.
Code
My code is published on github, as usual:
https://github.com/hfwittmann/colab-mlflow-papermill
My code owes thanks to this repository (but without the Colab part):