MSDN Documentation

Azure ML Compute: Managing Compute Resources for Machine Learning

Last updated: October 26, 2023

Azure Machine Learning provides a robust and scalable platform for managing your machine learning workflows. A core component of this platform is the ability to provision and manage various types of compute resources. This guide will walk you through the different compute options available in Azure ML and how to effectively utilize them for your training and inference tasks.

Understanding Compute Options

Azure Machine Learning supports several compute targets, each suited for different scenarios:

• Compute Instances: Fully managed cloud-based workstations for development.
• Compute Clusters: Scalable clusters of CPU or GPU virtual machines for training large models.
• Inference Clusters (AKS): Kubernetes clusters for deploying and scaling models for real-time inference.
• Managed Endpoints: Simplified deployment of models for batch or real-time scoring.
• Local Compute: Using your local machine for development and small-scale testing (not covered in detail in this cloud-focused article).

Compute Instances

Compute Instances are essential for data scientists and ML engineers. They provide a dedicated, fully managed virtual machine in the cloud equipped with common ML tools, libraries, and frameworks like Jupyter Notebooks, VS Code, and MLflow.

Key Features:

• Always-on accessibility from your browser.
• Pre-configured development environment.
• Secure access to your workspace data.
• Can be stopped to save costs.

To create a Compute Instance, navigate to the 'Compute' section in your Azure ML studio and select 'Compute instances', then 'New'. Choose the VM size and operating system that best fits your needs.

Compute Clusters

For demanding training workloads, Compute Clusters offer auto-scaling capabilities. You can configure them to automatically scale up or down based on the number of jobs submitted, helping you optimize costs and performance.

Use Cases:

• Training deep learning models with large datasets.
• Hyperparameter tuning across multiple nodes.
• Running distributed training jobs.

When creating a Compute Cluster, you specify the minimum and maximum number of nodes, the VM size, and the scaling policy. Azure ML manages the provisioning and de-provisioning of nodes.

Example of a simple job submission to a compute cluster:

from azureml.core import Workspace, Experiment, Environment
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

# Load workspace
ws = Workspace.from_config()

# Specify a compute cluster name
cluster_name = "my-training-cluster"

# Check if the compute target already exists
try:
    compute_target = ComputeTarget(workspace=ws, name=cluster_name)
    print('Found existing compute target')
except ComputeTargetException:
    print('Creating a new compute target...')
    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS11_V2',
                                                       max_nodes=4)
    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)

print(f'Compute target state: {compute_target.get_status().state}')
                

Inference Clusters and Managed Endpoints

Deploying your trained models to production requires reliable and scalable compute. Azure ML offers:

• Inference Clusters (AKS): Integrate with Azure Kubernetes Service (AKS) for highly scalable, managed Kubernetes clusters. Ideal for complex deployment scenarios requiring fine-grained control.
• Managed Endpoints: A simpler, more abstracted way to deploy models for real-time scoring or batch inference. Azure ML handles the underlying infrastructure provisioning and management.

Choosing the right deployment strategy depends on your application's latency, throughput, and management overhead requirements.

Best Practices for Compute Management

• Right-size your VMs: Select VM sizes that match the computational demands of your tasks.
• Utilize auto-scaling: Configure compute clusters to scale automatically to optimize costs.
• Stop idle resources: Shut down Compute Instances when not in use to prevent unnecessary charges.
• Monitor usage: Keep track of your compute resource consumption to identify potential optimizations.

Effective management of compute resources is crucial for maximizing the efficiency and cost-effectiveness of your Azure Machine Learning projects. By understanding and leveraging the various compute options, you can build, train, and deploy models at scale.