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Track Azure Databricks ML experiments with MLflow and Azure Machine Learning

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In this article, learn how to enable MLflow to connect to Azure Machine Learning while working in an Azure Databricks workspace. You can leverage this configuration for tracking, model management and model deployment.

MLflow is an open-source library for managing the life cycle of your machine learning experiments. MLFlow Tracking is a component of MLflow that logs and tracks your training job metrics and model artifacts. Learn more about Azure Databricks and MLflow.

See MLflow and Azure Machine Learning for additional MLflow and Azure Machine Learning functionality integrations.

If you have an MLflow Project to train with Azure Machine Learning, see Train ML models with MLflow Projects and Azure Machine Learning (preview).

Prerequisites

Install libraries

To install libraries on your cluster, navigate to the Libraries tab and select Install New

mlflow with azure databricks

In the Package field, type azureml-mlflow and then select install. Repeat this step as necessary to install other additional packages to your cluster for your experiment.

Azure DB install mlflow library

Track Azure Databricks runs with MLflow

Azure Databricks can be configured to track experiments using MLflow in two ways:

By default, dual-tracking is configured for you when you linked your Azure Databricks workspace.

Dual-tracking on Azure Databricks and Azure Machine Learning

Linking your ADB workspace to your Azure Machine Learning workspace enables you to track your experiment data in the Azure Machine Learning workspace and Azure Databricks workspace at the same time. This is referred as Dual-tracking.

To link your ADB workspace to a new or existing Azure Machine Learning workspace,

  1. Sign in to Azure portal.
  2. Navigate to your ADB workspace's Overview page.
  3. Select the Link Azure Machine Learning workspace button on the bottom right.

Link Azure DB and Azure Machine Learning workspaces

Warning

Dual-tracking in a private link enabled Azure Machine Learning workspace is not supported by the moment. Configure exclusive tracking with your Azure Machine Learning workspace instead.

After you link your Azure Databricks workspace with your Azure Machine Learning workspace, MLflow Tracking is automatically set to be tracked in all of the following places:

  • The linked Azure Machine Learning workspace.
  • Your original ADB workspace.

You can use then MLflow in Azure Databricks in the same way as you're used to. The following example sets the experiment name as it is usually done in Azure Databricks and start logging some parameters:

import mlflow 

experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}" 
mlflow.set_experiment(experimentName) 

with mlflow.start_run():
   mlflow.log_param('epochs', 20)
   pass

Note

As opposite to tracking, model registries don't support registering models at the same time on both Azure Machine Learning and Azure Databricks. Either one or the other has to be used. Please read the section Registering models in the registry with MLflow for more details.

Tracking exclusively on Azure Machine Learning workspace

If you prefer to manage your tracked experiments in a centralized location, you can set MLflow tracking to only track in your Azure Machine Learning workspace. This configuration has the advantage of enabling easier path to deployment using Azure Machine Learning deployment options.

Warning

For private link enabled Azure Machine Learning workspace, you have to deploy Azure Databricks in your own network (VNet injection) to ensure proper connectivity.

You have to configure the MLflow tracking URI to point exclusively to Azure Machine Learning, as it is demonstrated in the following example:

APPLIES TO: Python SDK azure-ai-ml v2 (preview)

You can get the Azure ML MLflow tracking URI using the Azure Machine Learning SDK v2 for Python. Ensure you have the library azure-ai-ml installed in the cluster you are using. The following sample gets the unique MLFLow tracking URI associated with your workspace. Then the method set_tracking_uri() points the MLflow tracking URI to that URI.

a. Using the workspace configuration file:

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
import mlflow

ml_client = MLClient.from_config(credential=DefaultAzureCredential()
azureml_mlflow_uri = ml_client.workspaces.get(ml_client.workspace_name).mlflow_tracking_uri
mlflow.set_tracking_uri(azureml_mlflow_uri)

Tip

You can download the workspace configuration file by:

  1. Navigate to Azure ML studio
  2. Click on the uper-right corner of the page -> Download config file.
  3. Save the file config.json in the same directory where you are working on.

b. Using the subscription ID, resource group name and workspace name:

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
import mlflow

#Enter details of your AzureML workspace
subscription_id = '<SUBSCRIPTION_ID>'
resource_group = '<RESOURCE_GROUP>'
workspace_name = '<AZUREML_WORKSPACE_NAME>'

ml_client = MLClient(credential=DefaultAzureCredential(),
                     subscription_id=subscription_id, 
                     resource_group_name=resource_group)

azureml_mlflow_uri = ml_client.workspaces.get(workspace_name).mlflow_tracking_uri
mlflow.set_tracking_uri(azureml_mlflow_uri)

Important

DefaultAzureCredential will try to pull the credentials from the available context. If you want to specify credentials in a different way, for instance using the web browser in an interactive way, you can use InteractiveBrowserCredential or any other method available in azure.identity package.

Experiment's names in Azure Machine Learning

When MLflow is configured to exclusively track experiments in Azure Machine Learning workspace, the experiment's naming convention has to follow the one used by Azure Machine Learning. In Azure Databricks, experiments are named with the path to where the experiment is saved like /Users/alice@contoso.com/iris-classifier. However, in Azure Machine Learning, you have to provide the experiment name directly. As in the previous example, the same experiment would be named iris-classifier directly:

mlflow.set_experiment(experiment_name="experiment-name")

Tracking parameters, metrics and artifacts

You can use then MLflow in Azure Databricks in the same way as you're used to. For details see Log & view metrics and log files.

Logging models with MLflow

After your model is trained, you can log it to the tracking server with the mlflow.<model_flavor>.log_model() method. <model_flavor>, refers to the framework associated with the model. Learn what model flavors are supported. In the following example, a model created with the Spark library MLLib is being registered:

mlflow.spark.log_model(model, artifact_path = "model")

It's worth to mention that the flavor spark doesn't correspond to the fact that we are training a model in a Spark cluster but because of the training framework it was used (you can perfectly train a model using TensorFlow with Spark and hence the flavor to use would be tensorflow).

Models are logged inside of the run being tracked. That means that models are available in either both Azure Databricks and Azure Machine Learning (default) or exclusively in Azure Machine Learning if you configured the tracking URI to point to it.

Important

Notice that here the parameter registered_model_name has not been specified. Read the section Registering models in the registry with MLflow for more details about the implications of such parameter and how the registry works.

Registering models in the registry with MLflow

As opposite to tracking, model registries can't operate at the same time in Azure Databricks and Azure Machine Learning. Either one or the other has to be used. By default, the Azure Databricks workspace is used for model registries; unless you chose to set MLflow Tracking to only track in your Azure Machine Learning workspace, then the model registry is the Azure Machine Learning workspace.

Then, considering you're using the default configuration, the following line will log a model inside the corresponding runs of both Azure Databricks and Azure Machine Learning, but it will register it only on Azure Databricks:

mlflow.spark.log_model(model, artifact_path = "model", 
                       registered_model_name = 'model_name')

  • If a registered model with the name doesn’t exist, the method registers a new model, creates version 1, and returns a ModelVersion MLflow object.

  • If a registered model with the name already exists, the method creates a new model version and returns the version object.

Using Azure Machine Learning Registry with MLflow

If you want to use Azure Machine Learning Model Registry instead of Azure Databricks, we recommend you to set MLflow Tracking to only track in your Azure Machine Learning workspace. This will remove the ambiguity of where models are being registered and simplifies complexity.

However, if you want to continue using the dual-tracking capabilities but register models in Azure Machine Learning, you can instruct MLflow to use Azure ML for model registries by configuring the MLflow Model Registry URI. This URI has the exact same format and value that the MLflow tracking URI.

mlflow.set_registry_uri(azureml_mlflow_uri)

Note

The value of azureml_mlflow_uri was obtained in the same way it was demostrated in Set MLflow Tracking to only track in your Azure Machine Learning workspace

For a complete example about this scenario please check the example Training models in Azure Databricks and deploying them on Azure ML.

Deploying and consuming models registered in Azure Machine Learning

Models registered in Azure Machine Learning Service using MLflow can be consumed as:

  • An Azure Machine Learning endpoint (real-time and batch): This deployment allows you to leverage Azure Machine Learning deployment capabilities for both real-time and batch inference in Azure Container Instances (ACI), Azure Kubernetes (AKS) or our Managed Inference Endpoints.

  • MLFlow model objects or Pandas UDFs, which can be used in Azure Databricks notebooks in streaming or batch pipelines.

Deploy models to Azure Machine Learning endpoints

You can leverage the azureml-mlflow plugin to deploy a model to your Azure Machine Learning workspace. Check How to deploy MLflow models page for a complete detail about how to deploy models to the different targets.

Important

Models need to be registered in Azure Machine Learning registry in order to deploy them. If your models happen to be registered in the MLflow instance inside Azure Databricks, you will have to register them again in Azure Machine Learning. If this is you case, please check the example Training models in Azure Databricks and deploying them on Azure ML

Deploy models to ADB for batch scoring using UDFs

You can choose Azure Databricks clusters for batch scoring. The MLFlow model is loaded and used as a Spark Pandas UDF to score new data.

from pyspark.sql.types import ArrayType, FloatType 

model_uri = "runs:/"+last_run_id+ {model_path} 

#Create a Spark UDF for the MLFlow model 
pyfunc_udf = mlflow.pyfunc.spark_udf(spark, model_uri) 

#Load Scoring Data into Spark Dataframe 
scoreDf = spark.table({table_name}).where({required_conditions}) 

#Make Prediction 
preds = (scoreDf 
           .withColumn('target_column_name', pyfunc_udf('Input_column1', 'Input_column2', ' Input_column3', …)) 
        ) 

display(preds)

Clean up resources

If you wish to keep your Azure Databricks workspace, but no longer need the Azure ML workspace, you can delete the Azure ML workspace. This action results in unlinking your Azure Databricks workspace and the Azure ML workspace.

If you don't plan to use the logged metrics and artifacts in your workspace, the ability to delete them individually is unavailable at this time. 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.

    Delete in the Azure portal

  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 Training models in Azure Databricks and deploying them on Azure ML demonstrates how to train models in Azure Databricks and deploy them in Azure ML. It also includes how to handle cases where you also want to track the experiments and models with the MLflow instance in Azure Databricks and leverage Azure ML for deployment.

Next steps