Track ML models with MLflow and Azure Machine Learning

APPLIES TO: Python SDK azureml v1

In this article, learn how to enable MLflow Tracking to connect Azure Machine Learning as the backend of your MLflow experiments.

MLflow is an open-source library for managing the lifecycle of your machine learning experiments. MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an Azure Databricks cluster.

See MLflow and Azure Machine Learning for all supported MLflow and Azure Machine Learning functionality including MLflow Project support (preview) and model deployment.

Prerequisites

• Install the mlflow package.

  • You can use the MLflow Skinny which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This is recommended for users who primarily need the tracking and logging capabilities without importing the full suite of MLflow features including deployments.

• Install the azureml-mlflow package.

• Create an Azure Machine Learning Workspace.

  • See which access permissions you need to perform your MLflow operations with your workspace.

• Install and set up Azure Machine Learning CLI (v1) and make sure you install the ml extension.

  Important

  Some of the Azure CLI commands in this article use the azure-cli-ml, or v1, extension for Azure Machine Learning. Support for the v1 extension will end on September 30, 2025. You will be able to install and use the v1 extension until that date.

  We recommend that you transition to the ml, or v2, extension before September 30, 2025. For more information on the v2 extension, see Azure ML CLI extension and Python SDK v2.

• Install and set up Azure Machine Learning SDK for Python.

Track runs from your local machine or remote compute

Tracking using MLflow with Azure Machine Learning lets you store the logged metrics and artifacts runs that were executed on your local machine into your Azure Machine Learning workspace.

Set up tracking environment

To track a run that is not running on Azure Machine Learning compute (from now on referred to as "local compute"), you need to point your local compute to the Azure Machine Learning MLflow Tracking URI.

export MLFLOW_TRACKING_URI=$(az ml workspace show --query mlflow_tracking_uri | sed 's/"//g') 

import mlflow

region = ""
subscription_id = ""
resource_group = ""
workspace_name = ""

azureml_mlflow_uri = f"azureml://{region}.api.ml.azure.cn/mlflow/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
mlflow.set_tracking_uri(azureml_mlflow_uri)

Set experiment name

All MLflow runs are logged to the active experiment. By default, runs are logged to an experiment named Default that is automatically created for you. To configure the experiment you want to work on use MLflow command mlflow.set_experiment().

experiment_name = 'experiment_with_mlflow'
mlflow.set_experiment(experiment_name)

Start training run

After you set the MLflow experiment name, you can start your training run with start_run(). Then use log_metric() to activate the MLflow logging API and begin logging your training run metrics.

import os
from random import random

with mlflow.start_run() as mlflow_run:
    mlflow.log_param("hello_param", "world")
    mlflow.log_metric("hello_metric", random())
    os.system(f"echo 'hello world' > helloworld.txt")
    mlflow.log_artifact("helloworld.txt")

For details about how to log metrics, parameters and artifacts in a run using MLflow view How to log and view metrics.

Track runs running on Azure Machine Learning

APPLIES TO: Python SDK azureml v1

Remote runs (jobs) let you train your models in a more robust and repetitive way. They can also leverage more powerful computes, such as Machine Learning Compute clusters. See Use compute targets for model training to learn about different compute options.

When submitting runs, Azure Machine Learning automatically configures MLflow to work with the workspace the run is running in. This means that there is no need to configure the MLflow tracking URI. On top of that, experiments are automatically named based on the details of the experiment submission.

Creating a training routine

First, you should create a src subdirectory and create a file with your training code in a train.py file in the src subdirectory. All your training code will go into the src subdirectory, including train.py.

The training code is taken from this MLflow example in the Azure Machine Learning example repo.

Copy this code into the file:

# imports
import os
import mlflow

from random import random

# define functions
def main():
    mlflow.log_param("hello_param", "world")
    mlflow.log_metric("hello_metric", random())
    os.system(f"echo 'hello world' > helloworld.txt")
    mlflow.log_artifact("helloworld.txt")


# run functions
if __name__ == "__main__":
    # run main function
    main()

Configuring the experiment

You will need to use Python to submit the experiment to Azure Machine Learning. In a notebook or Python file, configure your compute and training run environment with the Environment class.

from azureml.core import Environment
from azureml.core.conda_dependencies import CondaDependencies

env = Environment(name="mlflow-env")

# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions
cd = CondaDependencies.create(
    conda_packages=["scikit-learn", "matplotlib"],
    pip_packages=["azureml-mlflow", "pandas", "numpy"]
    )

env.python.conda_dependencies = cd

Then, construct ScriptRunConfig with your remote compute as the compute target.

from azureml.core import ScriptRunConfig

src = ScriptRunConfig(source_directory="src",
                      script=training_script,
                      compute_target="<COMPUTE_NAME>",
                      environment=env)

With this compute and training run configuration, use the Experiment.submit() method to submit a run. This method automatically sets the MLflow tracking URI and directs the logging from MLflow to your Workspace.

from azureml.core import Experiment
from azureml.core import Workspace
ws = Workspace.from_config()

experiment_name = "experiment_with_mlflow"
exp = Experiment(workspace=ws, name=experiment_name)

run = exp.submit(src)

View metrics and artifacts in your workspace

The metrics and artifacts from MLflow logging are tracked in your workspace. To view them anytime, navigate to your workspace and find the experiment by name in your workspace in Azure Machine Learning studio. Or run the below code.

Retrieve run metric using MLflow get_run().

from mlflow.tracking import MlflowClient

# Use MlFlow to retrieve the run that was just completed
client = MlflowClient()
run_id = mlflow_run.info.run_id
finished_mlflow_run = MlflowClient().get_run(run_id)

metrics = finished_mlflow_run.data.metrics
tags = finished_mlflow_run.data.tags
params = finished_mlflow_run.data.params

print(metrics,tags,params)

To view the artifacts of a run, you can use MlFlowClient.list_artifacts()

client.list_artifacts(run_id)

To download an artifact to the current directory, you can use MLFlowClient.download_artifacts()

client.download_artifacts(run_id, "helloworld.txt", ".")

For more details about how to retrieve information from experiments and runs in Azure Machine Learning using MLflow view Manage experiments and runs with MLflow.

Compare and query

Compare and query all MLflow runs in your Azure Machine Learning workspace with the following code. Learn more about how to query runs with MLflow.

from mlflow.entities import ViewType

all_experiments = [exp.experiment_id for exp in MlflowClient().list_experiments()]
query = "metrics.hello_metric > 0"
runs = mlflow.search_runs(experiment_ids=all_experiments, filter_string=query, run_view_type=ViewType.ALL)

runs.head(10)

Automatic logging

With Azure Machine Learning and MLFlow, users can log metrics, model parameters and model artifacts automatically when training a model. A variety of popular machine learning libraries are supported.

To enable automatic logging insert the following code before your training code:

mlflow.autolog()

Learn more about Automatic logging with MLflow.

Manage models

Register and track your models with the Azure Machine Learning model registry, which supports the MLflow model registry. Azure Machine Learning models are aligned with the MLflow model schema making it easy to export and import these models across different workflows. The MLflow-related metadata, such as run ID, is also tracked with the registered model for traceability. Users can submit training runs, register, and deploy models produced from MLflow runs.

If you want to deploy and register your production ready model in one step, see Deploy and register MLflow models.

To register and view a model from a run, use the following steps:

1. Once a run is complete, call the register_model() method.

   # the model folder produced from a run is registered. This includes the MLmodel file, model.pkl and the conda.yaml.
   model_path = "model"
   model_uri = 'runs:/{}/{}'.format(run_id, model_path) 
   mlflow.register_model(model_uri,"registered_model_name")
   

2. View the registered model in your workspace with Azure Machine Learning studio.

   In the following example the registered model, my-model has MLflow tracking metadata tagged.

   

3. Select the Artifacts tab to see all the model files that align with the MLflow model schema (conda.yaml, MLmodel, model.pkl).

   

4. Select MLmodel to see the MLmodel file generated by the run.

   

Clean up resources

If you don't plan to use the logged metrics and artifacts in your workspace, the ability to delete them individually is currently unavailable. Instead, delete the resource group that contains the storage account and workspace, so you don't incur any charges:

1. In the Azure portal, select Resource groups on the far left.

   

2. From the list, select the resource group you created.

3. Select Delete resource group.

4. Enter the resource group name. Then select Delete.

Example notebooks

The MLflow with Azure Machine Learning notebooks demonstrate and expand upon concepts presented in this article. Also see the community-driven repository, AzureML-Examples.

Next steps

• Deploy models with MLflow.
• Monitor your production models for data drift.
• Track Azure Databricks runs with MLflow.
• Manage your models.