1. Optimizing AI Workloads with Databricks Cluster Policies


    Databricks is a unified data analytics platform that is known for its ability to handle massive datasets and run complex data processing jobs, like those often encountered with AI workloads. When working with Databricks on cloud platforms such as AWS, Azure, or GCP, Pulumi can be used to manage and optimize these workloads through infrastructure as code. One of the most potent ways to ensure these workloads are run efficiently is through the use of Databricks Cluster Policies.

    Cluster Policies in Databricks allow you to control the types of clusters that users can create, enforce certain tags or configurations, and help manage cloud costs by placing restrictions on the resources a cluster can use.

    To optimize AI workloads using Databricks Cluster Policies with Pulumi, you'll want to create a policy that specifies the types of VMs to use, sets up auto-scaling, controls the maximum number of nodes, and so forth. This can help to make sure that the clusters are well-suited for the workload, not over-provisioned, and managed effectively.

    Let's create a Pulumi program in Python that sets up a Databricks cluster policy to optimize AI workloads. The following program will:

    • Create a Databricks Cluster Policy which enforces certain configurations for clusters like the node types, auto-scaling, and number of nodes.
    • Use the Pulumi Databricks provider to manage the configuration.

    Here's the program that accomplishes this:

    import pulumi import pulumi_databricks as databricks # Create a Databricks cluster policy to optimize for AI workloads. # This cluster policy enforces the use of specific node types suited for AI workloads, # a maximum number of nodes to control costs, and enables auto-scaling. ai_cluster_policy = databricks.ClusterPolicy("ai-cluster-policy", definition={ # Specify the node types to use for this cluster. Choose node types with more CPU and memory for AI workloads. "node_type_id": { "type": "fixed", "value": "Standard_D3_v2" # This is an example node type on Azure, choose an appropriate one for your cloud and workload. }, # Specify the maximum number of nodes the cluster can have. "num_workers": { "type": "range", "max_value": 8, "min_value": 2, }, # Enable autoscaling for the cluster within the set range. "autoscale": { "type": "fixed", "value": { "min_workers": 2, "max_workers": 8, } }, # You can add additional policy constraints here as needed. }, description="Policy for AI workload optimized clusters.", ) # Export the ID of the cluster policy to be used when creating clusters or for reference. pulumi.export("ai_cluster_policy_id", ai_cluster_policy.id)

    This program defines a cluster policy with constraints on the node type and number of nodes used in the cluster. The node type chosen should be tailored to AI workload requirements - for your use, you'd select nodes that offer the CPU power, memory, and possibly GPUs if your AI workload benefits from GPU acceleration. The range for autoscaling allows the cluster to adjust resources based on the workload.

    To apply this cluster policy to a particular cluster, you'd include the policy_id parameter when creating a databricks.Cluster resource.

    Remember to configure Pulumi to use the appropriate cloud provider and access credentials for Databricks, which might involve setting up your Pulumi stack with the necessary configuration and environment variables.

    For using this in a respective cloud provider's context like AWS or Azure, the node type ID will change according to what the cloud provider offers and Databricks supports in that cloud. If you are working with AWS, the node type would be an instance type from EC2 that is compatible with Databricks. Similarly, for Google Cloud, you'd use the machine type from GCP's Compute Engine offerings.

    Once you've completed a run of pulumi up with the above code, it will set up the cluster policy in Databricks, and you will have the policy ID outputted. This ID can then be used by any cluster creation scripts or in other Pulumi programs to enforce the use of this policy when clusters for AI workloads are created.