Tracking Your Training Runs

SigOpt Runs record everything you might need to understand how a model was built, reconstitute the model in the future, or explain the process to a colleague.

Common attributes of a Run include:

• the model type,

• dataset identifier,

• evaluation metrics,

• hyperparameters,

• logs, and

• a code reference

When you create and execute a Run, SigOpt will keep track of all the attributes you've logged, providing you with a permanent record that you can analyze.

You can analyze, filter, and compare your training behavior and results across a set of Runs in a SigOpt Project.

And when you are ready to optimize your model, you can make minor changes to your already instrumented code to enable SigOpt Experiments.

Overview of SigOpt Run Methods and CLI Commands

SigOpt enables you to execute Runs in 3 different ways:

• SigOpt Jupyter Integration

• SigOpt Python Client + SigOpt CLI

• SigOpt Python Client

An overview of all commands can be found in the API Reference section here.