mlflow-observer 0.0.1

Experiment tracking with sacred and mlflow

Observe your sacred experiments with mlflow.

Writing experiments with sacred is great.

mlflow provides a nice UI that can be used to get a quick overview of your runs and analyze the results.

Usage

In your code, add the observer:

from sacred import Experiment
from mlflow_observer import MlflowObserver

from _paths import MY_TRACKING_URI

ex = Experiment('MyExperiment')
ex.observers.append(MlflowObserver(MY_TRACKING_URI))

In the commandline, you can pass a run name through sacred’s comment flag:

python train.py -c "My sacred run"

Otherwise the run name will be of the form run_[datetime].