Real-Time SigOpt Demo
SigOpt automates the optimization of your model’s feature, architecture, and hyper-parameters using an ensemble of Bayesian optimization methods.
We can walk you through the SigOpt cloud-based API in just 60 seconds.
Let’s get started!
In this tutorial, we’ll use Franke’s function as an example of a two-dimensional optimization problem. (This function stands in for whichever trading, ML, or banking model you’re looking to optimize.)
Franke’s function has two numeric parameters: x and y. So, we start by telling SigOpt to create a two-dimensional Experiment object (docs) containing two Parameter objects (docs): x and y. Both are continuous and take values from 0 to 1.
(Note: We don’t need to pass additional model data to SigOpt. Models and data stay within your system. Only parameter configuration information is ever sent to SigOpt.)
Next, we start the SigOpt feedback loop to find the maximum of our target metric.
Here’s the loop:
- Get a Suggestion object from our API (docs). This contains our suggested parameter values.
- Evaluate your function using the parameters from the Suggestion.
- Send your function’s output to SigOpt as an Observation object (docs).
- Repeat steps 1-3 until the function is optimized (we suggest 10-20x the number of parameters).
Let’s visualize this loop. For each step below, read the description then hit the blue button.
(Optionally, to follow along in your development environment, select a language first.)