1. Custom Logging Configuration for AI Model Training on Databricks

    Python

    To configure custom logging in AI model training on Databricks using Pulumi, we will define a Databricks cluster and configure it to log custom metrics or messages that your model training process might emit.

    Databricks is a big data analytics platform powered by Apache Spark. It's often used for big data processing and machine learning tasks. In a typical Databricks setup, you would define clusters, which are sets of computation resources, to execute your data processing and machine learning tasks.

    For this task, we'll need to create a Databricks workspace, a cluster within that workspace, and potentially a job for executing the model training. Then we would configure the cluster's logging such that it captures the output you're interested in.

    Here's how you can set up the Databricks cluster and jobs with custom logging using Pulumi:

    1. Databricks Workspace: This is the environment for your Databricks resources. You have to create a workspace before you can create clusters or jobs.

    2. Databricks Cluster: This is a set of computation resources that runs your training jobs. The cluster's configuration allows you to enable different types of logging.

    3. Databricks Job (Optional): If you have a recurring training task, you might want to define a job in Databricks that can be triggered on a schedule or through some other automation.

    4. Logging Configuration: Databricks allows you to configure logging by setting Spark configurations and environment variables, which let you direct logs to a location such as a DBFS (Databricks File System) path.

    The following is an example program that demonstrates how to configure a Databricks cluster with custom logging. This doesn't include the specific code for model training or the setup of the actual logging services but focuses on the infrastructure setup part relevant to Pulumi.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace databricks_workspace = databricks.Workspace("myWorkspace", sku="standard", # Choose between standard, premium, or other available SKUs location="westus", # Specify the region for the workspace ) # Define the cluster configuration, including custom logging settings cluster = databricks.Cluster("training-cluster", spark_version="7.3.x-scala2.12", # Example Spark version; choose as per your requirement node_type_id="Standard_D3_v2", # Example node type; choose as per your requirement spark_conf={ # Spark configuration for logging, replace <destination> as per your logging service requirements "spark.driver.extraJavaOptions": "-Dlog4jspark.root.logger=WARN,console -Dlog4jspark.logFile=<log-destination>", "spark.executor.extraJavaOptions": "-Dlog4jspark.root.logger=WARN,console -Dlog4jspark.logFile=<log-destination>", }, num_workers=2, # Number of worker nodes for the cluster ) # Export the workspace URL for easy access pulumi.export("workspace_url", databricks_workspace.workspace_url) # Export cluster information pulumi.export("cluster_id", cluster.cluster_id)

    In this program:

    • We created a Databricks workspace named myWorkspace specifying its SKU and location.
    • We then defined a Databricks cluster named training-cluster with a certain Spark version and node type.
    • Within the spark_conf, you would specify the custom logging configurations. Replace <log-destination> with your desired destination for logs (for example, a path in DBFS where logs should be saved).

    This setup assumes that you've already configured your Databricks workspace and have the appropriate token set up in the Pulumi configuration for authentication.

    Remember to replace the placeholders like <log-destination> with actual values that apply to your use case. The logging configuration lines are typically more complex in real-world scenarios and might involve integrating with external logging services, storing logs in cloud storage, or setting up log rotation policies.

    To use this Pulumi program to set up your Databricks logging:

    1. Ensure you have the Pulumi CLI installed and are logged into the Pulumi service to store your state files.
    2. Set up the required Databricks provider configuration, which usually involves setting the Databricks workspace URL and a personal access token.
    3. Save the code into a file called __main__.py and run pulumi up to deploy your infrastructure.

    This program doesn't specify how to write the custom logs from within your AI model training code. You will need to integrate with the logging system, such as using Log4j or a similar library within your Spark job to produce the actual logs, which are then captured by your defined logging configuration.


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