Skip to main content

Contains the integration code of AzureML with Mlflow.

Project description

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 so that metrics and artifacts are logged to your Azure machine learning workspace.

Usage

Within an AzureML Workspace, add the code below to use MLflow.

import mlflow
from azureml.core import Workspace

workspace = Workspace.from_config()

mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri())

More examples can be found at https://aka.ms/azureml-mlflow-examples.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

azureml_mlflow-1.62.0.post1-py3-none-any.whl (1.0 MB view details)

Uploaded Python 3

File details

Details for the file azureml_mlflow-1.62.0.post1-py3-none-any.whl.

File metadata

File hashes

Hashes for azureml_mlflow-1.62.0.post1-py3-none-any.whl
Algorithm Hash digest
SHA256 f3e6bfa737fe2841bbfa099d5ab49f75429b24cc91ec19e3c8cb6dd96a9863ad
MD5 7e86017f29f336c005d224d3614d2b58
BLAKE2b-256 82ba46001358ddceb4b358ff62939a7e1fce7d0cb8a493b22f63fb73f2c5712a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page