1. Streamlined AI Development Environments with Databricks Instance Pools


    To create a streamlined AI Development Environment using Databricks, you can leverage Pulumi to provision Databricks instance pools. Instance pools in Databricks are a mechanism for reducing cluster start and auto-scaling times by maintaining a set of idle, ready-to-use cloud instances. This approach can be particularly useful for accelerating the development lifecycle in AI projects where rapid iteration is common.

    In the context of Pulumi, you will use the pulumi_databricks Python package to interact with Databricks. Specifically, you will create an instance pool that can be used by Databricks clusters for AI development.

    Here's how you can do it:

    1. Install the required Pulumi provider.
    2. Import the necessary modules in your Pulumi program.
    3. Define the instance pool with required and optional parameters.
    4. Set up other necessary resources, such as AWS attributes if you're deploying on AWS, or similar for Azure or GCP.
    5. Export the IDs or other important properties to access them in your workflows or other Pulumi programs.

    Here's an example of how you might write a Pulumi program in Python to create a Databricks instance pool:

    import pulumi import pulumi_databricks as databricks # Create a new Databricks instance pool instance_pool = databricks.InstancePool("ai-instance-pool", # Provide a node type that determines the instance type used for the instance pool node_type_id="Standard_DS3_v2", # Replace with the node type ID you want to use # Define the minimum number of idle instances to maintain in the pool min_idle_instances=1, # Optionally define the maximum capacity of the pool max_capacity=10, # Enable elastic disk option if required enable_elastic_disk=True, # Define AWS attributes if working with AWS infrastructure aws_attributes=databricks.InstancePoolAwsAttributesArgs( # Specify the AWS availability zone zone_id="us-west-2a", # Optionally configure other attributes such as spot bid price percentage spot_bid_price_percent=100, ), # Optionally, provide a list of preloaded Docker images, if any preloaded_docker_images=[ databricks.InstancePoolPreloadedDockerImagesArgs( url="ubuntu/xenial:latest", basic_auth=databricks.InstancePoolPreloadedDockerImagesBasicAuthArgs( username="<your_username>", password="<your_password>", ) ) ], # Set the pool's idle instance auto-termination time in minutes idle_instance_autotermination_minutes=15, # Define the instance pool name instance_pool_name="AI Development Pool" ) # Export the ID of the created instance pool pulumi.export("instance_pool_id", instance_pool.id)

    In the above code:

    • You create an instance pool with an InstancePool resource.
    • You specify the node_type_id, which is the instance type on the cloud provider (AWS, Azure, or GCP) you are using. This should be selected based on your AI workload requirements.
    • min_idle_instances is set to specify the minimum number of instances that remain running and ready for use.
    • max_capacity provides a limit for the number of instances in the pool.
    • enable_elastic_disk decides whether to enable elastic disk options for the instances.
    • aws_attributes is an example of how you can configure cloud-specific settings like the AWS availability zone and spot instance configurations.
    • preloaded_docker_images allows you to specify Docker images that should be preloaded on the instances for use with Databricks jobs.
    • idle_instance_autotermination_minutes helps you to manage costs by automatically terminating instances that have been idle for a certain period of time.
    • Lastly, you export instance_pool_id, which is often useful for referencing the pool in other parts of your infrastructure or when setting up Databricks clusters.

    Make sure to replace the placeholders (like <your_username>, <your_password>, and Standard_DS3_v2 as per your actual credentials and required specifications.

    With this set up, you would be able to utilize instance pools to provide a more efficient development environment for AI applications, reducing the time it takes to start up new compute resources and therefore decreasing overall iteration time.