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-1.58.0.post3-py3-none-any.whl (1.0 MB view details)

Uploaded Python 3

File details

Details for the file azureml_mlflow-1.58.0.post3-py3-none-any.whl.

File metadata

File hashes

Hashes for azureml_mlflow-1.58.0.post3-py3-none-any.whl
Algorithm Hash digest
SHA256 2b5b80d54cad001b07b2f2108f10e8f6b83576f4159c2f3824c7d38b8c814c11
MD5 1b02a04381cc12267dc07f65b2a29e27
BLAKE2b-256 be8a490caa4f0be4b4cd8225c67977314b613fa56d8fcce2a79c8fc2667b3480

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