Understanding Azure AI ML Compute Clusters

Azure Machine Learning compute clusters provide a managed, scalable compute infrastructure for training and batch inference workloads. They allow you to easily provision, scale, and manage compute resources without needing to manage virtual machines directly.

Key Benefits:

  • Scalability: Automatically scale up or down based on workload demands.
  • Cost-Effectiveness: Pay only for the compute you use, with options for low-priority VMs for cost savings.
  • Managed Infrastructure: Azure handles the provisioning, patching, and scaling of underlying infrastructure.
  • Flexibility: Support for various VM sizes and types to suit different training needs.
  • Integration: Seamless integration with Azure Machine Learning workspaces, pipelines, and experiments.

When to Use Compute Clusters:

Compute clusters are ideal for:

  • Model Training: Distribute training jobs across multiple nodes for faster iteration.
  • Batch Inference: Process large datasets for predictions at scale.
  • Experimentation: Quickly spin up compute for exploring different model architectures and hyperparameters.

Creating a Compute Cluster

You can create compute clusters using the Azure Machine Learning studio, Python SDK, or Azure CLI. Here's a basic example using the Python SDK:


from azure.ai.ml import MLClient
from azure.ai.ml.entities import AmlCompute
from azure.identity import DefaultAzureCredential

# Authenticate and create MLClient
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id="YOUR_SUBSCRIPTION_ID", resource_group_name="YOUR_RESOURCE_GROUP", workspace_name="YOUR_WORKSPACE_NAME"
)

# Define compute configuration
compute_name = "cpu-cluster-sample"
compute_cluster = AmlCompute(
    name=compute_name,
    size="STANDARD_DS3_V2",  # Choose an appropriate VM size
    min_instances=0,
    max_instances=4,
    idle_time_before_scale_down=120, # Scale down after 120 seconds of inactivity
)

# Create the compute cluster
try:
    compute = ml_client.compute.begin_create_or_update(compute_cluster).result()
    print(f"Compute cluster '{compute_name}' created successfully.")
except Exception as e:
    print(f"Error creating compute cluster: {e}")

Managing Compute Clusters

You can monitor resource usage, scale limits, and update configurations through the Azure portal or the Azure ML SDK. Key parameters include:

  • size: The VM size for the cluster nodes.
  • min_instances: The minimum number of nodes.
  • max_instances: The maximum number of nodes for autoscaling.
  • idle_time_before_scale_down: How long to wait before scaling down idle nodes.

Cost Considerations

The cost of compute clusters depends on the VM size, the number of nodes, and the duration they are running. Consider using low-priority VMs for non-critical workloads to significantly reduce costs.

Further Reading: