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Spearmint is a software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus the code name spearmint) in a manner that iteratively adjusts a number of parameters so as to minimize some objective in as few runs as possible.

IMPORTANT: Spearmint is under an Academic and Non-Commercial Research Use License. Before using spearmint please be aware of the license. If you do not qualify to use spearmint you can ask to obtain a license as detailed in the license or you can use the older open source code version (which is somewhat outdated) at

Relevant Publications

Spearmint implements a combination of the algorithms detailed in the following publications:

Practical Bayesian Optimization of Machine Learning Algorithms  
Jasper Snoek, Hugo Larochelle and Ryan Prescott Adams  
Advances in Neural Information Processing Systems, 2012  

Multi-Task Bayesian Optimization  
Kevin Swersky, Jasper Snoek and Ryan Prescott Adams  
Advances in Neural Information Processing Systems, 2013  

Input Warping for Bayesian Optimization of Non-stationary Functions  
Jasper Snoek, Kevin Swersky, Richard Zemel and Ryan Prescott Adams  
International Conference on Machine Learning, 2014  

Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology  
Jasper Snoek, PhD Thesis, University of Toronto, 2013  

Bayesian Optimization with Unknown Constraints
Michael Gelbart, Jasper Snoek and Ryan Prescott Adams
Uncertainty in Artificial Intelligence, 2014

Setting up Spearmint

STEP 1: Installation

  1. Install python, numpy, scipy, pymongo. For academic users, the anaconda distribution is great. Use numpy 1.8 or higher. We use python 2.7.
  2. Download/clone the spearmint code
  3. Install the spearmint package using pip: pip install -e \</path/to/spearmint/root\> (the -e means changes will be reflected automatically)
  4. Download and install MongoDB:
  5. Install the pymongo package using e.g., pip pip install pymongo or anaconda conda install pymongo

STEP 2: Setting up your experiment

  1. Create a callable objective function. See ./examples/simple/ as an example
  2. Create a config file. There are 3 example config files in the ../examples directory. Note 1: There are more parameters that can be set in the config files than what is shown in the examples, but these parameters all have default values. Note 2: By default Spearmint assumes your function is noisy (non-deterministic). If it is noise-free, you should set this explicitly as in the ../examples/simple/config.json file.

STEP 3: Running spearmint

  1. Start up a MongoDB daemon instance:
    mongod --fork --logpath <path/to/logfile\> --dbpath <path/to/dbfolder\>
  2. Run spearmint: python \</path/to/experiment/directory\>

STEP 4: Looking at your results
Spearmint will output results to standard out / standard err. You can also load the results from the database and manipulate them directly.


Spearmint Bayesian optimization codebase







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