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].
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Metadata
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Release files for mlflow-observer 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| mlflow-observer-0.0.1.tar.gz | 3.0 kB | Details |
Release files / mlflow-observer-0.0.1.tar.gz
| Download URL | mlflow-observer-0.0.1.tar.gz |
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| Size | 3.0 kB |
| Tags | Source |
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twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200604 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.6.10
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