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

azureml_mlflow-1.43.0.post1-py3-none-any.whl (810.0 kB view details)

Uploaded Python 3

File details

Details for the file azureml_mlflow-1.43.0.post1-py3-none-any.whl.

File metadata

File hashes

Hashes for azureml_mlflow-1.43.0.post1-py3-none-any.whl
Algorithm Hash digest
SHA256 5e5b6fe3923c5dbdfccab247c338e26777c9c9e32a3c82934ab1329779a06470
MD5 93fbe2a9574bdb0f64c5fdee635fcdc8
BLAKE2b-256 6c9f19b8bec8d5d7779d18f6eaa80781a62ea257f69c9477b96f52458826a345

See more details on using hashes here.

Supported by

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