Getting Started with Azure Machine Learning Compute Instances

Azure Machine Learning compute instances provide a fully managed cloud-based workstation for data scientists. They come pre-configured with popular data science tools, making it easier to start building and training models without worrying about environment setup.

What is a Compute Instance?

A compute instance is a managed cloud-based workstation optimized for machine learning development. It includes Jupyter, JupyterLab, VS Code (web), RStudio, and a terminal. The instance comes pre-installed with popular ML frameworks like PyTorch, TensorFlow, and scikit-learn.

Creating a Compute Instance via Azure CLI

# Install the Azure ML extension
az extension add -n ml

# Create a compute instance
az ml compute create --name my-compute-instance \
  --type ComputeInstance \
  --size Standard_DS3_v2 \
  --resource-group myresourcegroup \
  --workspace-name myworkspace

Creating via Python SDK

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

# Initialize ML client
credential = DefaultAzureCredential()
ml_client = MLClient(
    credential=credential,
    subscription_id="your-subscription-id",
    resource_group_name="myresourcegroup",
    workspace_name="myworkspace"
)

# Define compute instance configuration
compute_instance = ComputeInstance(
    name="my-compute-instance",
    size="Standard_DS3_v2",
    idle_time_before_shutdown="PT30M",  # Auto-shutdown after 30 min idle
    setup_scripts={
        "creation_script": {
            "script_source": "inline",
            "script": "pip install pandas numpy"
        }
    }
)

# Create the compute instance
ml_client.compute.begin_create_or_update(compute_instance).result()
print("Compute instance created successfully!")

Configuring Auto-Shutdown

One of the best features for cost management is the auto-shutdown capability:

from azure.ai.ml.entities import ComputeInstance, ComputeSchedules, ComputeStartStopSchedule
from azure.ai.ml.constants import TimeZone

# Create schedule for auto-shutdown
schedules = ComputeSchedules(
    compute_start_stop=[
        ComputeStartStopSchedule(
            trigger={"type": "Recurrence", "frequency": "Day", "interval": 1},
            action="Stop",
            schedule={"hours": 19, "minutes": 0, "time_zone": TimeZone.PACIFIC_STANDARD_TIME}
        )
    ]
)

compute_instance = ComputeInstance(
    name="scheduled-instance",
    size="Standard_DS3_v2",
    schedules=schedules
)

Accessing Your Compute Instance

Once created, you can access your compute instance through multiple interfaces:

1. Jupyter Notebooks: Navigate to the Azure ML Studio and click on “Notebooks”
2. JupyterLab: Full JupyterLab experience with extensions
3. VS Code Web: Browser-based VS Code experience
4. Terminal: SSH access for command-line operations

Best Practices

1. Choose the right VM size: Start with smaller instances like DS3_v2 for development, scale up for training
2. Enable auto-shutdown: Always configure idle shutdown to manage costs
3. Use setup scripts: Automate your environment configuration
4. Leverage SSH access: Connect from your local VS Code for a seamless experience

Cost Optimization Tips

# List all compute instances to audit
computes = ml_client.compute.list()
for compute in computes:
    if compute.type == "ComputeInstance":
        print(f"Instance: {compute.name}, State: {compute.state}, Size: {compute.size}")

# Stop idle instances
ml_client.compute.begin_stop("my-compute-instance").result()

Compute instances are the perfect starting point for your Azure ML journey. They provide everything you need to explore data, build models, and collaborate with your team - all in a managed, secure environment.