1. Running Apache Spark MLlib on GCP Dataproc for AI


    To run Apache Spark MLlib on GCP's DataProc service for AI tasks, you'll need to create a DataProc cluster and submit a Spark job that leverages the MLlib library. DataProc is a managed service on Google Cloud Platform (GCP) that simplifies the creation and management of clusters that run Hadoop, Spark, HBase, and other big data tools.

    Here are the steps we'd include in a Pulumi program:

    1. Create a GCP DataProc cluster.
    2. Define and submit a Spark job to the cluster that runs MLlib.
    3. Export relevant information such as the DataProc cluster name and job ID.

    MLlib is Apache Spark's scalable machine learning library that is designed to simplify machine learning tasks such as classification, regression, clustering, and collaborative filtering on big datasets. To execute an MLlib job, you'll need to submit your Spark job to the DataProc cluster you've set up.

    Below is a detailed Pulumi program in Python that defines and manages these resources:

    import pulumi import pulumi_gcp as gcp # Replace these variables with appropriate values for your job and script project = 'your-gcp-project' region = 'your-cluster-region' zone = 'the-zone-of-your-cluster' bucket_name = 'your-storage-bucket' main_python_file_uri = 'gs://your-storage-bucket/path-to-your-pyspark-code.py' # This should be your Python script that uses MLlib # First, we'll create a GCP DataProc cluster for running Spark jobs. # This cluster is configured minimally for demonstration purposes. # For production, ensure you configure the cluster to match your performance and cost requirements. cluster = gcp.dataproc.Cluster("ml-cluster", project=project, region=region, cluster_config={ "master_config": { "num_instances": 1, "machine_type": "n1-standard-1", }, "worker_config": { "num_instances": 2, "machine_type": "n1-standard-1", }, "gce_cluster_config": { "zone": zone, "bucket": bucket_name, } }) # With the cluster set up, we can now define a job that uses Spark MLlib. # The properties specified below are meant to be generic placeholders. # You'll need to adjust the arguments and properties to fit your Spark job's needs. spark_ml_job = gcp.dataproc.Job("spark-ml-job", project=project, region=region, pyspark_config=gcp.dataproc.JobPysparkConfigArgs( main_python_file_uri=main_python_file_uri, args=["--your-argument-key", "your-argument-value"], # Replace with actual arguments if needed. jar_file_uris=[ "file:///usr/lib/spark/examples/jars/spark-examples.jar" # Optional: Include JARs required by your job ] ), placement=gcp.dataproc.JobPlacementArgs( cluster_name=cluster.name )) # Finally, we'll export the names of the cluster and the Spark MLlib job for easy access. pulumi.export('dataproc_cluster_name', cluster.name) pulumi.export('spark_mllib_job_id', spark_ml_job.id)


    • We import the required Pulumi modules for GCP.
    • The gcp.dataproc.Cluster resource creates a new DataProc cluster that will host our Spark jobs. The configuration defines one master and two worker nodes, but you should adjust the machine_type, and num_instances as necessary for your workload.
    • The gcp.dataproc.Job resource represents a job submission to the DataProc cluster. We specify the pyspark_config with the URI of the Python script in a GCS bucket and any necessary arguments. Be sure to replace the main_python_file_uri with the location of your PySpark script. You can also include any required JAR files using jar_file_uris.
    • We use the pulumi.export function to output the names of the cluster and the job, which can be useful for querying their status or managing them outside of Pulumi.

    Remember to replace placeholder values with actual ones suitable for your context, such as your GCP project ID, region, and code locations. Make sure you also have the necessary permissions and that your Google Cloud account is properly set up and authenticated with Pulumi.