Metadata-Version: 2.0
Name: azureml-mlflow
Version: 1.0.60
Summary: UNKNOWN
Home-page: https://docs.microsoft.com/en-us/azure/machine-learning/service/
Author: Microsoft Corp
License: Proprietary https://aka.ms/azureml-preview-sdk-license 
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/x-rst
Requires-Dist: azureml-core (==1.0.60.*)
Requires-Dist: jsonpickle
Requires-Dist: mlflow (>=1.0.0)

Microsoft Azure Machine Learning Tracking server plugin for Python
===================================================================
The azureml-mlflow package contains the integration code of AzureML with MLFlow.
MLFlow (https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models.
You can use MLFlow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.


With an AzureML Workspace (https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py) add the below lines before your MLflow code:

import mlflow
from azureml.core import Workspace

workspace = Workspace.from_config()

mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri())

# Examples can be found here (https://aka.ms/azureml-mlflow-examples)




