1. Optimized Data Storage for Large Language Models with Databricks


    To optimize data storage for large language models with Databricks on the Pulumi platform, you can use several Databricks resources. In this context, we will focus on creating a Databricks cluster configured to handle large amounts of data efficiently, and setting up a Databricks table which will be used to store the data for your language models.

    The Databricks Cluster will be configured with autoscaling to optimize resource usage and cost. It will also be configured with a larger node type to handle the computational requirements of processing large language models.

    The Databricks Table will serve as the storage layer for the processed data. The table schema will be defined explicitly with necessary columns, data types, and partitioning to enhance query performance.

    Here's a Python program using Pulumi to set up an optimized data storage for large language models using Databricks:

    import pulumi import pulumi_databricks as databricks # Create a Databricks cluster configured for large datasets cluster = databricks.Cluster("large-dataset-cluster", num_workers=3, autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=50 ), node_type_id="i3.xlarge", # Choose an appropriate instance type spark_version="7.3.x-scala2.12", spark_conf={"spark.databricks.io.cache.enabled": "true"}, # Enable the DBIO caching for better performance custom_tags={ "Purpose": "Processing large language models" } ) # Define the schema for the Databricks table that will store your language model data columns=[ databricks.TableColumnArgs( name="model_id", type_json="'STRING'", nullable=False, type_name="STRING" ), databricks.TableColumnArgs( name="model_data", type_json="'BINARY'", nullable=False, type_name="BINARY" ), # Additional columns can be added as per the requirement ] # Create a Databricks table with the defined schema table = databricks.Table("language-model-data", name="language_model_data", columns=columns, table_type="MANAGED", # Use 'EXTERNAL' if you are using an external storage schema_name="default", storage_location="dbfs:/mnt/large-models/", # Specify the DBFS location for your data ) # Export the Databricks cluster and table information pulumi.export("cluster_id", cluster.id) pulumi.export("cluster_name", cluster.cluster_name) pulumi.export("table_name", table.name)

    In this program:

    • We first import necessary modules (pulumi and pulumi_databricks).
    • We then create a Databricks cluster that is apt for handling large datasets by setting the num_workers, autoscale parameters for dynamic scaling according to the load, and a larger node type (i3.xlarge) to handle intense workloads.
    • The cluster is also configured to have DBIO caching enabled which is beneficial for performance on large datasets.
    • Afterwards, we define the schema for the Databricks table with mandatory fields suitable to store large language model data (in this case, a model_id as STRING and model_data as BINARY).
    • We then create a Databricks table with the MANAGED type, which means Databricks manages both the storage and the metadata of the table. If you need to use your own storage (like S3, ADLS, etc.), you would specify EXTERNAL and provide the respective storage location URI.
    • The storage_location in the table is where the actual data will reside. Here we use a mounted location on dbfs.
    • Finally, we export the cluster and table information for reference or for use in other Pulumi stacks.

    Before running this Pulumi program, make sure that you have the correct Databricks workspace set up and have the necessary permissions to create clusters and tables. The instance types, scaling configuration, table schema, and storage locations should all be modified according to the specific requirements and scale of your large language models.