1. Managed MongoDB Clusters for Training Data Repositories

    Python

    To create managed MongoDB clusters suitable for training data repositories, we can use the mongodbatlas.Cluster resource from the pulumi_mongodbatlas package. MongoDB Atlas provides fully managed MongoDB clusters in the cloud, which can be scaled and configured according to your needs, making it an excellent choice for storing and managing training data for machine learning or other data-intensive applications.

    The mongodbatlas.Cluster resource in Pulumi allows you to define the configuration of your MongoDB Atlas cluster, such as the cluster type, the cloud provider on which it should run, the region, instance size, disk size, and more.

    Below is a basic example of a Pulumi program that provisions a MongoDB Atlas cluster using Pulumi with Python. The program will create a new project in MongoDB Atlas and then provision an M10 cluster, which is suitable for development environments or small-scale applications. You can adjust the size and settings according to your requirements for a training data repository.

    Before running this program, you must set up your MongoDB Atlas API keys as environment variables or use Pulumi Config to securely manage these secrets.

    Here's the Pulumi Python program that achieves this:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Create a MongoDB Atlas project project = mongodbatlas.Project("my-training-data-project", name="Training Data Project", org_id="Replace this with your MongoDB Atlas organization ID") # Provision a MongoDB Atlas cluster within the project cluster = mongodbatlas.Cluster("my-training-data-cluster", project_id=project.id, cluster_type="REPLICASET", replication_factor=3, # This specifies the replication factor for the cluster provider_instance_size_name="M10", # This is the instance size for our cluster. You can choose a different size as per need. provider_name="AWS", # Cloud provider to use (AWS, GCP, AZURE) provider_region_name="EU_WEST_1", # The region where the cluster will be deployed disk_size_gb=10, # Specifies the disk size for each data-bearing server mongo_db_major_version="4.2", # Specifies the MongoDB server version provider_backup_enabled=True, # Enables cloud provider snapshots auto_scaling_disk_gb_enabled=True # Enables auto-scaling for storage ) # Export the connection string for the cluster to access it later pulumi.export("mongo_cluster_connection_string", cluster.connection_strings["standard_srv"])

    This program does the following:

    1. Imports the required Pulumi packages.
    2. Creates a new MongoDB Atlas project with mongodbatlas.Project. You need to replace the org_id with your actual MongoDB Atlas organization ID.
    3. Provisions a new MongoDB Atlas cluster within the project using mongodbatlas.Cluster. This is where you define your cluster's specifications. Adjust these specifications according to the requirements of your training data repository.
    4. Exports the connection string, which you can use to connect your application to the MongoDB cluster.

    Remember to replace placeholder values with actual data pertinent to your MongoDB Atlas account and desired configuration. After running pulumi up, Pulumi will handle the provisioning and configuration of your MongoDB Atlas cluster according to the specifications you've provided.

    For more details on the mongodbatlas Cluster resource, you can refer to the official Pulumi documentation.


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