1. Large Language Model Inference on Databricks Clusters


    Large language models require significant computing resources for inference tasks. Databricks is a platform that provides an interactive workspace and supports large-scale data processing and machine learning workloads. To perform large language model inference on Databricks, you need to set up a Databricks cluster, install the necessary libraries, and potentially leverage optimized machine learning runtime environments provided by Databricks.

    Below is a Pulumi Python program that will create a Databricks cluster tailored for large language model inference. In this program, you'll see resources such as databricks.Cluster which creates a new computational cluster within Databricks. The autoscale property allows the cluster to automatically scale the number of workers up or down based on workload. Additionally, the nodeTypeId specifies the type of virtual machines that will be used for the cluster nodes, and sparkVersion denotes the version of Apache Spark to be used, which is often tied to the Databricks runtime version.

    The sparkEnvVars can be used to set environment variables that might be necessary for certain libraries or configurations, and initScripts can be used to run scripts during cluster initialization to install additional dependencies.

    We'll also use a databricks.Library to install necessary Python packages for interacting with the large language models like Huggingface transformers or any ML/NLP libraries.

    Please note that actual implementation details would depend on the specific requirements of the language model and the computation needed for inference tasks. Be sure your Databricks workspace is properly set up and that you have the necessary access rights to create and manipulate clusters.

    Let's go through the program that sets up a Databricks cluster ready for machine learning tasks:

    import pulumi import pulumi_databricks as databricks # Define a Databricks cluster configuration # This specifies the node type, Spark version, and enables autoscaling within a range of workers. cluster = databricks.Cluster("ai-model-inference-cluster", autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=8 ), node_type_id="Standard_D3_v2", # Node type to be used, should be chosen based on model requirements cluster_name="large-language-model-inference", spark_version="7.3.x-scala2.12", # Spark version compatible with Databricks runtime spark_env_vars={ "PYSPARK_PYTHON": "/databricks/python3/bin/python3", # Environment variable for PySpark }, init_scripts=[databricks.ClusterInitScriptsArgs( dbfs=databricks.ClusterInitScriptsDbfsArgs( destination="dbfs:/databricks/scripts/init.sh" # Path to an initialization script that can install additional dependencies ) )], # Further configurations may be set here based on the specific needs of the model inference task ) # Attach a Python library like Huggingface 'transformers' which is commonly used for NLP models # This library could be used to run large language models for inference. library = databricks.Library("transformers-library", cluster_id=cluster.id, pypi=databricks.LibraryPypiArgs( package="transformers", # The name of the PyPi package to install ) ) # Exports the cluster URL for direct access or further configurations. pulumi.export('cluster_url', cluster.url)

    This program sets up a Databricks cluster with an autoscaling configuration, which is recommended for jobs where the computation load can vary. Here, the cluster is set to scale between 2 to 8 worker nodes dynamically.

    The node_type_id should be configured based on your model's computational requirements, and the spark_version chosen must be compatible with the libraries and runtimes you plan to use.

    The init_scripts is an optional configuration which is useful if you need to run a setup script hosted in a DBFS (Databricks File System) location to prepare the environment for your workload.

    Finally, the databricks.Library resource is attached to the cluster, specifying a common library used for natural language processing tasks. Depending on your exact use case, different or additional libraries might be required.

    After deploying this infrastructure with Pulumi, you would actually run the language model inference tasks within Databricks, using notebooks or jobs that leverage the computational power of the newly created cluster.