Runs: An Easy Way to Track Training

Nick Payton and Tobias Andreasen
Deep Learning, Experiment Management, Machine Learning, Modeling Best Practices

Modeling is messy and it can be hard to keep track of everything. To solve this problem, SigOpt developed Runs, which allow you to track training runs, log modeling attributes, and organize your modeling workflow with just a few lines of code. 

SigOpt Runs are compatible with any combination of modeling library, coding environment, and infrastructure you use for your modeling. Getting started is as simple as pip installing SigOpt and adding a few lines of SigOpt code to your notebook or in the command line. Here is a simple example snapshot from a notebook that you can click into and use today:

If you are interested in learning more, check out this blog post – Keeping track of it all: recording and organizing model training runs. If you want to see how to use Runs before applying them to your own project, use this test notebook.

If you want to try out the product, sign up for free access, execute a run to track your training, and launch an experiment to automate hyperparameter optimization. If you want to learn more about our products, track industry news, and hear from our research team, follow our blog or subscribe to our youtube channel.

Nick Payton
Nick Payton Head of Marketing & Partnerships
Tobias Andreasen
Tobias Andreasen Machine Learning Specialist

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