1. Distributed Training of Machine Learning Models on OCI VCN


    To set up distributed training of machine learning models on Oracle Cloud Infrastructure (OCI), you would typically need to create a virtual cloud network (VCN) where you would deploy compute instances capable of performing machine learning tasks. You will need to ensure that the appropriate networking configurations, such as subnets, security rules, and potentially load balancers, are in place to facilitate communication between instances and potentially expose endpoints for monitoring or management.

    Below is a Pulumi program in Python that demonstrates how to create a VCN on OCI, along with necessary subnets, and deploy a compute instance that could be used to host a machine learning model training job. This example assumes that you have your OCI provider set up. For simplicity, I'm only creating one instance; in a real-world scenario, you'd create multiple instances and configure them to communicate with each other for distributed training.

    Before running this program, make sure you’ve installed the Pulumi CLI and the pulumi_oci Python package.

    Now, let's break down the tasks we're going to perform:

    1. Create a VCN: A VCN is a virtual version of a traditional network that you would operate in your own data center. It's necessary for setting up an isolated network space in OCI.
    2. Create a subnet: A subnet is a subdivision of the VCN that you've created. You can launch resources such as compute instances within a subnet.
    3. Deploy a compute instance: This instance will be used to run your machine learning workloads. In a distributed training context, you would deploy multiple such instances.

    Here's the code:

    import pulumi import pulumi_oci as oci # Define the compartment where the resources will be created. compartment_id = 'YOUR_COMPARTMENT_OCID' # Create a new VCN. vcn = oci.core.Vcn('ml-vcn', compartment_id=compartment_id, cidr_block="", display_name="ML_Training_VCN", dns_label="mltrainingvcn") # Create a subnet within the VCN. subnet = oci.core.Subnet('ml-subnet', compartment_id=compartment_id, vcn_id=vcn.id, cidr_block="", display_name="ML_Training_Subnet", dns_label="mltrainingsubnet") # Assuming you already have an image and shape you want to use for your instance, # which are conducive to machine learning tasks. For example, you might use an # NVIDIA GPU-equipped shape and an image with a deep learning environment. instance_image = 'YOUR_INSTANCE_IMAGE_OCID' instance_shape = 'YOUR_INSTANCE_SHAPE_NAME' # Create a compute instance within the subnet to run distributed training. ml_instance = oci.core.Instance('ml-instance', compartment_id=compartment_id, display_name="MLTrainingInstance", shape=instance_shape, create_vnic_details=oci.core.InstanceCreateVnicDetailsArgs( subnet_id=subnet.id ), source_details=oci.core.InstanceSourceDetailsArgs( source_type="image", image_id=instance_image )) # Export the public IP of the instance to easily access it. instance_public_ip = ml_instance.public_ip pulumi.export('instance_public_ip', instance_public_ip)


    Now, let's elaborate on what this code does:

    • The compartment_id variable specifies the OCI compartment where you want to organize your resources.
    • The vcn object is defined with a CIDR block that dictates the IP address range available for the network. A DNS label is also provided to allow for name resolution within the VCN.
    • The subnet object is a network within the VCN where instances can be launched. It's defined with its own CIDR block and DNS label.
    • The ml_instance object represents the compute instance that will run the machine learning training code. It is given a shape and image ID that should be suitable for machine learning tasks, such as having GPUs or pre-installed data science libraries.
    • The instance_public_ip is an output exported by Pulumi so that you can easily access the public IP of the created instance for SSH access or other forms of remote interaction.

    This program sets up a basic network and compute instance for machine learning tasks on OCI. In practice, you would scale this out by creating multiple instances for distributed training and configuring your machine learning code to leverage the hardware appropriately.