Take a Tour of an Example Deep Learning Model
Step through how to integrate SigOpt Experiment Management in a Jupyter Notebook and how to use the Web interface.
Track & Organize Modeling Attributes
Modeling is messy and it can be hard to keep track of everything. With just a few lines of code, SigOpt tracks and organizes your training and tuning cycles, including: architectures, metrics, parameters, hyperparameters, code snapshots and the results of feature analysis, training runs or tuning jobs. Consider us your intern, sidekick, advisor, or all of the above.
Visualize & Compare Runs
Modeling is often about tradeoffs, but it’s hard to gather insights to properly evaluate these. Quickly gain intuition on your models and their performance with an API that automatically populates your dashboard with customizable visualizations and in-depth hyperparameter, metric and run insights as you train and tune.
Seamless Training & Tuning
Transitioning between training and tuning can be expensive in time and resources, so many modelers leave tuning until the last mile. Our solution fully integrates automated hyperparameter tuning with training run tracking to make this process easy and accessible. And features like automated early stopping, highly customizable search spaces, multimetric optimization and multitask optimization make tuning useful for any model you are building.
Compatible with any ML Library
Our solution is fully agnostic to modeling framework, compute stack, orchestration setup, or coding environment. And our back-end is designed to work equally well for startups with a few modelers and a few tuning jobs a month to modeling leaders with thousands of modelers and millions of concurrent tuning jobs per month.
Explore, Understand, Advance
We design software that sets modelers up for success. Use SigOpt to:
- Efficiently explore your problem space by comparing results from your model development process.
- Understand model performance with easy ways to visualize patterns in learning curves, assess parameter importance, and evaluate metric comparisons.
- Once you’re on to something promising, advance your model with easy hyperparameter tuning before taking it into production.
Review the docs
Learn how to use Runs API through our Python client in either command line interface or notebook environments, and how to view history, visualizations, and comparisons in our dashboard.