Neptune-Sacred Integration

The integration lets you log Sacred experiments to Neptune.

Requirements

Integration with the Sacred framework is enabled as part of the logging module, so all you need is neptune-client to get started.

pip install neptune-client

Create an Experiment

Note

The code here uses a shared, anonymous token but you can change to your private NEPTUNE_API_TOKEN.

from numpy.random import permutation
from sklearn import svm, datasets
from sacred import Experiment

ex = Experiment('iris_rbf_svm')

Append NeptuneObserver

Pass the experiment object as the first argument.

Note

The code uses the API token for the anonymous user neptuner. You can change it to your own or test it out on this public project.

from neptunecontrib.monitoring.sacred import NeptuneObserver
ex.observers.append(NeptuneObserver(api_token='ANONYMOUS',
                                    project_name='shared/sacred-integration'))

Run normally

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

@ex.automain
def run(C, gamma, _run):
    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:])

Monitor your Sacred experiments in Neptune

Now you can watch your Sacred experiments in Neptune!

Check out this example experiment dashboard.

Full Sacred monitoring script

Simply copy and paste the script to sacred_example.py and run. Remember to change your credentials in NeptuneObserver():

ex.observers.append(NeptuneObserver(api_token='YOUR_API_TOKEN',
                                    project_name='USER_NAME/PROJECT_NAME'))

from numpy.random import permutation
from sklearn import svm, datasets
from sacred import Experiment

from neptunecontrib.monitoring.sacred import NeptuneObserver

ex = Experiment('iris_rbf_svm')
ex.observers.append(NeptuneObserver(api_token='ANONYMOUS',
                                    project_name='shared/sacred-integration'))

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

@ex.automain
def run(C, gamma, _run):

    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:])