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osprey is an easy-to-use tool for hyperparameter optimization for machine learning algorithms in python using scikit-learn (or using scikit-learn compatible APIs).

Each osprey experiment combines an dataset, an estimator, a search space (and engine), cross validation and asynchronous serialization for distributed parallel optimization of model hyperparameters.


For full documentation, please visit the Osprey homepage.


If you have an Anaconda Python distribution, installation is as easy as:

$ conda install -c omnia osprey

You can also install with pip:

$ pip install git+git://

Alternatively, you can install directly from this GitHub repo:

$ git clone
$ cd osprey && python install

Example using MSMBuilder

Below is an example of an osprey config file to cross validate Markov state models based on varying the number of clusters and dihedral angles used in a model:

  eval_scope: msmbuilder
  eval: |
        ('featurizer', DihedralFeaturizer(types=['phi', 'psi'])),
        ('cluster', MiniBatchKMeans()),
        ('msm', MarkovStateModel(n_timescales=5, verbose=False)),

    min: 10
    max: 100
    type: int
      - ['phi', 'psi']
      - ['phi', 'psi', 'chi1']
   type: enum

cv: 5

  name: mdtraj
    trajectories: ~/local/msmbuilder/Tutorial/XTC/*/*.xtc
    topology: ~/local/msmbuilder/Tutorial/native.pdb
    stride: 1

    uri: sqlite:///osprey-trials.db

Then run osprey worker. You can run multiple parallel instances of osprey worker simultaneously on a cluster too.

$ osprey worker config.yaml


Beginning iteration                                              1 / 1
History contains: 0 trials
Choosing next hyperparameters with random...
  {'cluster__n_clusters': 20, 'featurizer__types': ['phi', 'psi']}

Fitting 5 folds for each of 1 candidates, totalling 5 fits
[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    0.3s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.8s finished
Success! Model score = 4.080646
(best score so far   = 4.080646)

1/1 models fit successfully.
time:         October 27, 2014 10:44 PM
elapsed:      4 seconds.
osprey worker exiting.

You can dump the database to JSON or CSV with osprey dump.


  • six

  • pyyaml

  • numpy

  • scikit-learn

  • sqlalchemy

  • GPy (optional, required for gp strategy)

  • scipy (optional, required for gp strategy)

  • hyperopt (optional, required for hyperopt_tpe strategy)

  • nose (optional, for testing)

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