Train with MLflow Projects in Azure Machine Learning (preview)

Artikkeli
08/28/2024

In this article, learn how to submit training jobs with MLflow Projects that use Azure Machine Learning workspaces for tracking. You can submit jobs and only track them with Azure Machine Learning or migrate your runs to the cloud to run completely on Azure Machine Learning Compute.

Warning

Support for MLproject files (MLflow Projects) in Azure Machine Learning will be fully retired in September 2026. MLflow is still fully supported and is still the recommended way to track machine learning workloads in Azure Machine Learning.

As you continue to use MLflow, we recommend that you transition from MLproject files to Azure Machine Learning Jobs, using either the Azure CLI or the Azure Machine Learning SDK for Python (v2). For more information on Azure Machine Learning jobs, see Track ML experiments and models with MLflow.

MLflow Projects allow for you to organize and describe your code to let other data scientists (or automated tools) run it. MLflow Projects with Azure Machine Learning enable you to track and manage your training runs in your workspace.

Important

This feature is currently in public preview. This preview version is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities.

For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Learn more about the MLflow and Azure Machine Learning integration.

Prerequisites

Install the MLflow SDK mlflow package and the Azure Machine Learning azureml-mlflow plugin for MLflow as follows:
```
pip install mlflow azureml-mlflow
```
Tip

You can use the mlflow-skinny package, which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This package is recommended for users who primarily need the MLflow tracking and logging capabilities without importing the full suite of features, including deployments.
Create an Azure Machine Learning workspace. To create a workspace, see Create resources you need to get started. Review the access permissions you need to perform your MLflow operations in your workspace.
To do remote tracking, or track experiments running outside Azure Machine Learning, configure MLflow to point to the tracking URI of your Azure Machine Learning workspace. For more information on how to connect MLflow to your workspace, see Configure MLflow for Azure Machine Learning.

Using Azure Machine Learning as backend for MLflow projects requires the package azureml-core:
```
pip install azureml-core
```

Connect to your workspace

If you're working outside Azure Machine Learning, you need to configure MLflow to point to your Azure Machine Learning workspace's tracking URI. You can find the instructions at Configure MLflow for Azure Machine Learning.

Track MLflow Projects in Azure Machine Learning workspaces

This example shows how to submit MLflow projects and track them Azure Machine Learning.

Add the azureml-mlflow package as a pip dependency to your environment configuration file in order to track metrics and key artifacts in your workspace.

conda.yaml
```
name: mlflow-example
channels:
  - defaults
dependencies:
  - numpy>=1.14.3
  - pandas>=1.0.0
  - scikit-learn
  - pip:
    - mlflow
    - azureml-mlflow
```
Submit the local run and ensure you set the parameter backend = "azureml", which adds support of automatic tracking, model's capture, log files, snapshots, and printed errors in your workspace. In this example we assume the MLflow project you are trying to run is in the same folder you currently are, uri=".".
- MLflow CLI
- Python
```
mlflow run . --experiment-name  --backend azureml --env-manager=local -P alpha=0.3
```
```
local_env_run = mlflow.projects.run(
    uri=".", 
    parameters={"alpha":0.3},
    backend = "azureml",
    env_manager="local",
    backend_config = backend_config, 
)
```
View your runs and metrics in the Azure Machine Learning studio.

Train MLflow projects in Azure Machine Learning jobs

This example shows how to submit MLflow projects as a job running on Azure Machine Learning compute.

Create the backend configuration object, in this case we are going to indicate COMPUTE. This parameter references the name of your remote compute cluster you want to use for running your project. If COMPUTE is present, the project will be automatically submitted as an Azure Machine Learning job to the indicated compute.
- MLflow CLI
- Python
backend_config.json
```
{
    "COMPUTE": "cpu-cluster"
}
```
```
backend_config = {"COMPUTE": "cpu-cluster"}
```
Add the azureml-mlflow package as a pip dependency to your environment configuration file in order to track metrics and key artifacts in your workspace.

conda.yaml
```
name: mlflow-example
channels:
  - defaults
dependencies:
  - numpy>=1.14.3
  - pandas>=1.0.0
  - scikit-learn
  - pip:
    - mlflow
    - azureml-mlflow
```
Submit the local run and ensure you set the parameter backend = "azureml", which adds support of automatic tracking, model's capture, log files, snapshots, and printed errors in your workspace. In this example we assume the MLflow project you are trying to run is in the same folder you currently are, uri=".".
- MLflow CLI
- Python
```
mlflow run . --backend azureml --backend-config backend_config.json -P alpha=0.3
```
```
local_env_run = mlflow.projects.run(
    uri=".", 
    parameters={"alpha":0.3},
    backend = "azureml",
    backend_config = backend_config, 
)
```
Note

Since Azure Machine Learning jobs always run in the context of environments, the parameter env_manager is ignored.

View your runs and metrics in the Azure Machine Learning studio.

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:

In the Azure portal, select Resource groups on the far left.
From the list, select the resource group you created.
Select Delete resource group.
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.

Note

A community-driven repository of examples using mlflow can be found at https://github.com/Azure/azureml-examples.

Jaa