Skip to main content

Facilitates automated and reproducible experimental research

Project description

Every experiment is sacred
Every experiment is great
If an experiment is wasted
God gets quite irate

Current PyPi Version Supported Python Versions MIT licensed ReadTheDocs DOI for this release

Travis-CI Status appveyor-CI Status Coverage Report Code Scrutinizer Quality Codacity rating

Sacred is a tool to help you configure, organize, log and reproduce experiments. It is designed to do all the tedious overhead work that you need to do around your actual experiment in order to:

  • keep track of all the parameters of your experiment
  • easily run your experiment for different settings
  • save configurations for individual runs in a database
  • reproduce your results

Sacred achieves this through the following main mechanisms:

  • ConfigScopes A very convenient way of the local variables in a function to define the parameters your experiment uses.
  • Config Injection: You can access all parameters of your configuration from every function. They are automatically injected by name.
  • Command-line interface: You get a powerful command-line interface for each experiment that you can use to change parameters and run different variants.
  • Observers: Sacred provides Observers that log all kinds of information about your experiment, its dependencies, the configuration you used, the machine it is run on, and of course the result. These can be saved to a MongoDB, for easy access later.
  • Automatic seeding helps controlling the randomness in your experiments, such that the results remain reproducible.

Example

Script to train an SVM on the iris dataset The same script as a Sacred experiment
from numpy.random import permutation
from sklearn import svm, datasets





C = 1.0
gamma = 0.7



iris = datasets.load_iris()
perm = permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
clf = svm.SVC(C, 'rbf', gamma=gamma)
clf.fit(iris.data[:90],
        iris.target[:90])
print(clf.score(iris.data[90:],
                iris.target[90:]))
from numpy.random import permutation
from sklearn import svm, datasets
from sacred import Experiment
ex = Experiment('iris_rbf_svm')

@ex.config
def cfg():
  C = 1.0
  gamma = 0.7

@ex.automain
def run(C, gamma):
  iris = datasets.load_iris()
  per = permutation(iris.target.size)
  iris.data = iris.data[per]
  iris.target = iris.target[per]
  clf = svm.SVC(C, 'rbf', gamma=gamma)
  clf.fit(iris.data[:90],
          iris.target[:90])
  return clf.score(iris.data[90:],
                   iris.target[90:])

Documentation

The documentation is hosted at ReadTheDocs.

Installing

You can directly install it from the Python Package Index with pip:

pip install sacred

Or if you want to do it manually you can checkout the current version from git and install it yourself:

cd sacred
python setup.py install

You might want to also install the numpy and the pymongo packages. They are optional dependencies but they offer some cool features:

pip install numpy, pymongo

Tests

The tests for sacred use the py.test package. You can execute them by running py.test in the sacred directory like this:

py.test

There is also a config file for tox so you can automatically run the tests for various python versions like this:

tox

Frontends

At this point there are two frontends to the database entries created by sacred (that I’m aware of). They are developed externally as separate projects.

Sacredboard

docs/images/sacredboard.png

Sacredboard is a web-based dashboard interface to the sacred runs stored in a MongoDB.

SacredBrowser

docs/images/sacred_browser.png

SacredBrowser is a PyQt4 application to browse the MongoDB entries created by sacred experiments. Features include custom queries, sorting of the results, access to the stored source-code, and many more. No installation is required and it can connect to a local database or over the network.

Prophet

Prophet is an early prototype of a webinterface to the MongoDB entries created by sacred experiments, that is discontinued. It requires you to run RestHeart to access the database.

License

This project is released under the terms of the MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
sacred-0.7.4-py2.py3-none-any.whl (83.1 kB) Copy SHA256 hash SHA256 Wheel 3.6 Jun 12, 2018
sacred-0.7.4.tar.gz (67.0 kB) Copy SHA256 hash SHA256 Source None Jun 12, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page