1. Provisioning Scalable AI Model Training Clusters

    Python

    To provision scalable AI model training clusters, we will use Azure Machine Learning services, which allow for creating various machine learning resources, including compute clusters that can be automatically scaled based on the workload.

    We will implement this using the Pulumi infrastructure as code. Specifically, we will create:

    • An Azure Machine Learning Workspace: This serves as the foundational block in the cloud that you use to experiment, train, and deploy machine learning models.
    • An Azure Machine Learning Compute Cluster: This is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The cluster will automatically scale out to accommodate the workload.

    Here's a Pulumi program in Python that creates a scalable AI model training cluster:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group if it doesn't exist resource_group = azure_native.resources.ResourceGroup('ai_model_training_rg') # Create an Azure Machine Learning Workspace ml_workspace = azure_native.machinelearningservices.Workspace( "ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs( name="Basic", # Choose 'Enterprise' for more robust capabilities ) ) # Create a Machine Learning Compute Cluster compute_cluster = azure_native.machinelearningservices.Compute( "ai_compute_cluster", compute_name="aicluster", location=resource_group.location, properties=azure_native.machinelearningservices.ComputeInstanceArgs( compute_type="AmlCompute", properties=azure_native.machinelearningservices.AmlComputePropertiesArgs( vm_size="STANDARD_D2_V2", # Choose a different SKU depending on your requirements vm_priority="dedicated", scale_settings=azure_native.machinelearningservices.ScaleSettingsArgs( max_node_count=10, min_node_count=0, node_idle_time_before_scale_down="PT120S", # Autoscale down after 2 minutes of idle time ), ), ), resource_group_name=resource_group.name, workspace_name=ml_workspace.name, ) # Export URLs and important information of the resources created pulumi.export("resource_group_name", resource_group.name) pulumi.export("ml_workspace_name", ml_workspace.name) pulumi.export("compute_cluster_name", compute_cluster.name)

    In this program, we create a resource group as a logical container where all Azure resources can live. Next, we instantiate an Azure Machine Learning Workspace providing the name, location, and SKU (choose "Enterprise" for a more feature-rich workspace, "Basic" for standard use).

    Then, we create a compute cluster under this workspace. In the definition of the Compute resource, we specify the VM size and priority. Moreover, we provide the scale_settings that contain configurations for the auto-scaling feature, such as the maximum and minimum node counts and the time to wait before scaling down idle nodes.

    This program allows for a scalable cluster that can adjust its size based on the processing demand automatically. You would store your training scripts and data in the workspace, and this computing cluster would facilitate running those training jobs at scale.

    Remember to run pulumi up after writing this program in a __main__.py file, which will prompt you with a summary of the resources that will be created. Confirm the prompt to proceed with the deployment.

    For detailed information on the properties used in each resource, you can visit the Pulumi Azure Machine Learning documentation pages:


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